diff --git a/app.py b/app.py index a0ee891be2cd5b3104da77cdc491a0ceddc1807b..a6c2220bfb8fe9141b54ebefaa16e1fd5f3a595d 100644 --- a/app.py +++ b/app.py @@ -1,63 +1,21 @@ import gradio as gr -import argparse, os import cv2 import torch +from imwatermark import WatermarkEncoder import numpy as np -from omegaconf import OmegaConf from PIL import Image -from tqdm import tqdm, trange -from itertools import islice -from einops import rearrange -from torchvision.utils import make_grid -from pytorch_lightning import seed_everything -from torch import autocast -from contextlib import nullcontext -from imwatermark import WatermarkEncoder import re - -from ldm.util import instantiate_from_config -from ldm.models.diffusion.ddim import DDIMSampler -from ldm.models.diffusion.plms import PLMSSampler -from ldm.models.diffusion.dpm_solver import DPMSolverSampler -from huggingface_hub import hf_hub_download from datasets import load_dataset - -torch.set_grad_enabled(False) +from diffusers import DiffusionPipeline, EulerDiscreteScheduler from share_btn import community_icon_html, loading_icon_html, share_js REPO_ID = "stabilityai/stable-diffusion-2" -CKPT_NAME = "768-v-ema.ckpt" -CONFIG_PATH = "./configs/stable-diffusion/v2-inference-v.yaml" -device = "cuda" -stable_diffusion_2_path = hf_hub_download(repo_id=REPO_ID, filename=CKPT_NAME) - -torch.set_grad_enabled(False) - -def chunk(it, size): - it = iter(it) - return iter(lambda: tuple(islice(it, size)), ()) - - -def load_model_from_config(config, ckpt, verbose=False): - print(f"Loading model from {ckpt}") - pl_sd = torch.load(ckpt, map_location="cpu") - if "global_step" in pl_sd: - print(f"Global Step: {pl_sd['global_step']}") - sd = pl_sd["state_dict"] - model = instantiate_from_config(config.model) - m, u = model.load_state_dict(sd, strict=False) - if len(m) > 0 and verbose: - print("missing keys:") - print(m) - if len(u) > 0 and verbose: - print("unexpected keys:") - print(u) - - model.cuda() - model.eval() - return model +device = "cuda" if torch.cuda.is_available() else "cpu" +wm = "SDV2" +wm_encoder = WatermarkEncoder() +wm_encoder.set_watermark('bytes', wm.encode('utf-8')) def put_watermark(img, wm_encoder=None): if wm_encoder is not None: img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) @@ -65,234 +23,28 @@ def put_watermark(img, wm_encoder=None): img = Image.fromarray(img[:, :, ::-1]) return img -#When running locally, you won`t have access to this, so you can remove this part +repo_id = "stabilityai/stable-diffusion-2" +scheduler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler", prediction_type="v_prediction") +pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16", scheduler=scheduler) +pipe = pipe.to(device) +pipe.enable_xformers_memory_efficient_attention() + +#If you have duplicated this Space or is running locally, you can remove this part word_list_dataset = load_dataset("stabilityai/word-list", data_files="list.txt", use_auth_token=True) word_list = word_list_dataset["train"]['text'] -config = OmegaConf.load(CONFIG_PATH) -model = load_model_from_config(config, stable_diffusion_2_path) -device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") -model = model.to(device) - -def parse_args(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--prompt", - type=str, - nargs="?", - default="a professional photograph of an astronaut riding a triceratops", - help="the prompt to render" - ) - parser.add_argument( - "--outdir", - type=str, - nargs="?", - help="dir to write results to", - default="outputs/txt2img-samples" - ) - parser.add_argument( - "--steps", - type=int, - default=50, - help="number of ddim sampling steps", - ) - parser.add_argument( - "--plms", - action='store_true', - help="use plms sampling", - ) - parser.add_argument( - "--dpm", - action='store_true', - help="use DPM (2) sampler", - ) - parser.add_argument( - "--fixed_code", - action='store_true', - help="if enabled, uses the same starting code across all samples ", - ) - parser.add_argument( - "--ddim_eta", - type=float, - default=0.0, - help="ddim eta (eta=0.0 corresponds to deterministic sampling", - ) - parser.add_argument( - "--n_iter", - type=int, - default=3, - help="sample this often", - ) - parser.add_argument( - "--H", - type=int, - default=512, - help="image height, in pixel space", - ) - parser.add_argument( - "--W", - type=int, - default=512, - help="image width, in pixel space", - ) - parser.add_argument( - "--C", - type=int, - default=4, - help="latent channels", - ) - parser.add_argument( - "--f", - type=int, - default=8, - help="downsampling factor, most often 8 or 16", - ) - parser.add_argument( - "--n_samples", - type=int, - default=3, - help="how many samples to produce for each given prompt. A.k.a batch size", - ) - parser.add_argument( - "--n_rows", - type=int, - default=0, - help="rows in the grid (default: n_samples)", - ) - parser.add_argument( - "--scale", - type=float, - default=9.0, - help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))", - ) - parser.add_argument( - "--from-file", - type=str, - help="if specified, load prompts from this file, separated by newlines", - ) - parser.add_argument( - "--config", - type=str, - default="configs/stable-diffusion/v2-inference.yaml", - help="path to config which constructs model", - ) - parser.add_argument( - "--ckpt", - type=str, - help="path to checkpoint of model", - ) - parser.add_argument( - "--seed", - type=int, - default=42, - help="the seed (for reproducible sampling)", - ) - parser.add_argument( - "--precision", - type=str, - help="evaluate at this precision", - choices=["full", "autocast"], - default="autocast" - ) - parser.add_argument( - "--repeat", - type=int, - default=1, - help="repeat each prompt in file this often", - ) - opt = parser.parse_args() - return opt - def infer(prompt, samples, steps, scale, seed): - opt = parse_args() - opt.seed = seed - seed_everything(seed) - + #If you have duplicated this Space or is running locally, you can remove this part for filter in word_list: if re.search(rf"\b{filter}\b", prompt): raise gr.Error("Unsafe content found. Please try again with different prompts.") - - opt.n_samples = samples - opt.scale = scale - opt.prompt = prompt - opt.steps = steps - opt.n_iter = 1 - sampler = DPMSolverSampler(model) - os.makedirs(opt.outdir, exist_ok=True) - outpath = opt.outdir - - print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...") - wm = "SDV2" - wm_encoder = WatermarkEncoder() - wm_encoder.set_watermark('bytes', wm.encode('utf-8')) - - batch_size = opt.n_samples - n_rows = opt.n_rows if opt.n_rows > 0 else batch_size - if not opt.from_file: - prompt = opt.prompt - assert prompt is not None - data = [batch_size * [prompt]] - else: - print(f"reading prompts from {opt.from_file}") - with open(opt.from_file, "r") as f: - data = f.read().splitlines() - data = [p for p in data for i in range(opt.repeat)] - data = list(chunk(data, batch_size)) - prompt = prompt - assert prompt is not None - data = [batch_size * [prompt]] - - sample_path = os.path.join(outpath, "samples") - os.makedirs(sample_path, exist_ok=True) - sample_count = 0 - base_count = len(os.listdir(sample_path)) - grid_count = len(os.listdir(outpath)) - 1 - - opt.W = 768 - opt.H = 768 - - start_code = None - if opt.fixed_code: - start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device) - - precision_scope = autocast if opt.precision == "autocast" else nullcontext - image_samples = [] - with torch.no_grad(), \ - precision_scope("cuda"), \ - model.ema_scope(): - all_samples = list() - for n in trange(opt.n_iter, desc="Sampling"): - for prompts in tqdm(data, desc="data"): - uc = None - if opt.scale != 1.0: - uc = model.get_learned_conditioning(batch_size * [""]) - if isinstance(prompts, tuple): - prompts = list(prompts) - c = model.get_learned_conditioning(prompts) - shape = [opt.C, opt.H // opt.f, opt.W // opt.f] - samples, _ = sampler.sample(S=opt.steps, - conditioning=c, - batch_size=opt.n_samples, - shape=shape, - verbose=False, - unconditional_guidance_scale=opt.scale, - unconditional_conditioning=uc, - eta=opt.ddim_eta, - x_T=start_code) - - x_samples = model.decode_first_stage(samples) - x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0) - - for x_sample in x_samples: - x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c') - img = Image.fromarray(x_sample.astype(np.uint8)) - img = put_watermark(img, wm_encoder) - image_samples.append(img) - base_count += 1 - sample_count += 1 - - all_samples.append(x_samples) - return image_samples + generator = torch.Generator(device=device).manual_seed(seed) + images = pipe(prompt, width=768, height=768, num_inference_steps=steps, guidance_scale=scale, num_images_per_prompt=samples, generator=generator).images + images_watermarked = [] + for image in images: + image = put_watermark(image, wm_encoder) + images_watermarked.append(image) + return images_watermarked css = """ .gradio-container { @@ -412,7 +164,8 @@ css = """ #prompt-container{ gap: 0; } - #component-14{border-top-width: 1px !important} + #component-9{margin-top: -19px} + .image_duplication{position: absolute; width: 100px; left: 50px} """ block = gr.Blocks(css=css) @@ -421,36 +174,36 @@ examples = [ [ 'A high tech solarpunk utopia in the Amazon rainforest', 4, - 45, - 7.5, + 25, + 9, 1024, ], [ 'A pikachu fine dining with a view to the Eiffel Tower', 4, - 45, - 7, + 25, + 9, 1024, ], [ 'A mecha robot in a favela in expressionist style', 4, - 45, - 7, + 25, + 9, 1024, ], [ 'an insect robot preparing a delicious meal', 4, - 45, - 7, + 25, + 9, 1024, ], [ "A small cabin on top of a snowy mountain in the style of Disney, artstation", 4, - 45, - 7, + 25, + 9, 1024, ], ] @@ -458,7 +211,7 @@ examples = [ with block: gr.HTML( """ -
+
+

Stable Diffusion 2 is the latest text-to-image model from StabilityAI. Access Stable Diffusion 1 Space here
For faster generation and API access you can try DreamStudio Beta + >. To skip the queue you can Duplicate Space

""" @@ -563,7 +316,7 @@ with block: loading_icon = gr.HTML(loading_icon_html) share_button = gr.Button("Share to community", elem_id="share-btn") - ex = gr.Examples(examples=examples, fn=infer, inputs=[text, samples, steps, scale, seed], outputs=[gallery, community_icon, loading_icon, share_button], cache_examples=False) + ex = gr.Examples(examples=examples, fn=infer, inputs=[text, samples, steps, scale, seed], outputs=[gallery], cache_examples=False) ex.dataset.headers = [""] text.submit(infer, inputs=[text, samples, steps, scale, seed], outputs=[gallery]) @@ -578,7 +331,7 @@ with block: gr.HTML( """
-

Model by Stability AI - Gradio Demo by ๐Ÿค— Hugging Face +

Model by Stability AI - Gradio Demo by ๐Ÿค— Hugging Face using the ๐Ÿงจ diffusers library

@@ -590,4 +343,4 @@ Despite how impressive being able to turn text into image is, beware to the fact """ ) -block.queue(concurrency_count=1, max_size=25).launch(max_threads=150) \ No newline at end of file +block.queue(concurrency_count=1, max_size=50).launch(max_threads=150) \ No newline at end of file diff --git a/configs/stable-diffusion/v2-inference-v.yaml b/configs/stable-diffusion/v2-inference-v.yaml deleted file mode 100644 index 8ec8dfbfefe94ae8522c93017668fea78d580acf..0000000000000000000000000000000000000000 --- a/configs/stable-diffusion/v2-inference-v.yaml +++ /dev/null @@ -1,68 +0,0 @@ -model: - base_learning_rate: 1.0e-4 - target: ldm.models.diffusion.ddpm.LatentDiffusion - params: - parameterization: "v" - linear_start: 0.00085 - linear_end: 0.0120 - num_timesteps_cond: 1 - log_every_t: 200 - timesteps: 1000 - first_stage_key: "jpg" - cond_stage_key: "txt" - image_size: 64 - channels: 4 - cond_stage_trainable: false - conditioning_key: crossattn - monitor: val/loss_simple_ema - scale_factor: 0.18215 - use_ema: False # we set this to false because this is an inference only config - - unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - use_fp16: True - image_size: 32 # unused - in_channels: 4 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_head_channels: 64 # need to fix for flash-attn - use_spatial_transformer: True - use_linear_in_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False - - first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - #attn_type: "vanilla-xformers" - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity - - cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" diff --git a/configs/stable-diffusion/v2-inference.yaml b/configs/stable-diffusion/v2-inference.yaml deleted file mode 100644 index 152c4f3c2b36c3b246a9cb10eb8166134b0d2e1c..0000000000000000000000000000000000000000 --- a/configs/stable-diffusion/v2-inference.yaml +++ /dev/null @@ -1,67 +0,0 @@ -model: - base_learning_rate: 1.0e-4 - target: ldm.models.diffusion.ddpm.LatentDiffusion - params: - linear_start: 0.00085 - linear_end: 0.0120 - num_timesteps_cond: 1 - log_every_t: 200 - timesteps: 1000 - first_stage_key: "jpg" - cond_stage_key: "txt" - image_size: 64 - channels: 4 - cond_stage_trainable: false - conditioning_key: crossattn - monitor: val/loss_simple_ema - scale_factor: 0.18215 - use_ema: False # we set this to false because this is an inference only config - - unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - use_fp16: True - image_size: 32 # unused - in_channels: 4 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_head_channels: 64 # need to fix for flash-attn - use_spatial_transformer: True - use_linear_in_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False - - first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - #attn_type: "vanilla-xformers" - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity - - cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" diff --git a/configs/stable-diffusion/v2-inpainting-inference.yaml b/configs/stable-diffusion/v2-inpainting-inference.yaml deleted file mode 100644 index 32a9471d71b828c51bcbbabfe34c5f6c8282c803..0000000000000000000000000000000000000000 --- a/configs/stable-diffusion/v2-inpainting-inference.yaml +++ /dev/null @@ -1,158 +0,0 @@ -model: - base_learning_rate: 5.0e-05 - target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion - params: - linear_start: 0.00085 - linear_end: 0.0120 - num_timesteps_cond: 1 - log_every_t: 200 - timesteps: 1000 - first_stage_key: "jpg" - cond_stage_key: "txt" - image_size: 64 - channels: 4 - cond_stage_trainable: false - conditioning_key: hybrid - scale_factor: 0.18215 - monitor: val/loss_simple_ema - finetune_keys: null - use_ema: False - - unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - image_size: 32 # unused - in_channels: 9 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_head_channels: 64 # need to fix for flash-attn - use_spatial_transformer: True - use_linear_in_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False - - first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - #attn_type: "vanilla-xformers" - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [ ] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity - - cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" - - -data: - target: ldm.data.laion.WebDataModuleFromConfig - params: - tar_base: null # for concat as in LAION-A - p_unsafe_threshold: 0.1 - filter_word_list: "data/filters.yaml" - max_pwatermark: 0.45 - batch_size: 8 - num_workers: 6 - multinode: True - min_size: 512 - train: - shards: - - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -" - - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -" - - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -" - - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -" - - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -" #{00000-94333}.tar" - shuffle: 10000 - image_key: jpg - image_transforms: - - target: torchvision.transforms.Resize - params: - size: 512 - interpolation: 3 - - target: torchvision.transforms.RandomCrop - params: - size: 512 - postprocess: - target: ldm.data.laion.AddMask - params: - mode: "512train-large" - p_drop: 0.25 - # NOTE use enough shards to avoid empty validation loops in workers - validation: - shards: - - "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - " - shuffle: 0 - image_key: jpg - image_transforms: - - target: torchvision.transforms.Resize - params: - size: 512 - interpolation: 3 - - target: torchvision.transforms.CenterCrop - params: - size: 512 - postprocess: - target: ldm.data.laion.AddMask - params: - mode: "512train-large" - p_drop: 0.25 - -lightning: - find_unused_parameters: True - modelcheckpoint: - params: - every_n_train_steps: 5000 - - callbacks: - metrics_over_trainsteps_checkpoint: - params: - every_n_train_steps: 10000 - - image_logger: - target: main.ImageLogger - params: - enable_autocast: False - disabled: False - batch_frequency: 1000 - max_images: 4 - increase_log_steps: False - log_first_step: False - log_images_kwargs: - use_ema_scope: False - inpaint: False - plot_progressive_rows: False - plot_diffusion_rows: False - N: 4 - unconditional_guidance_scale: 5.0 - unconditional_guidance_label: [""] - ddim_steps: 50 # todo check these out for depth2img, - ddim_eta: 0.0 # todo check these out for depth2img, - - trainer: - benchmark: True - val_check_interval: 5000000 - num_sanity_val_steps: 0 - accumulate_grad_batches: 1 diff --git a/configs/stable-diffusion/v2-midas-inference.yaml b/configs/stable-diffusion/v2-midas-inference.yaml deleted file mode 100644 index f20c30f618b81091e31c2c4cf15325fa38638af4..0000000000000000000000000000000000000000 --- a/configs/stable-diffusion/v2-midas-inference.yaml +++ /dev/null @@ -1,74 +0,0 @@ -model: - base_learning_rate: 5.0e-07 - target: ldm.models.diffusion.ddpm.LatentDepth2ImageDiffusion - params: - linear_start: 0.00085 - linear_end: 0.0120 - num_timesteps_cond: 1 - log_every_t: 200 - timesteps: 1000 - first_stage_key: "jpg" - cond_stage_key: "txt" - image_size: 64 - channels: 4 - cond_stage_trainable: false - conditioning_key: hybrid - scale_factor: 0.18215 - monitor: val/loss_simple_ema - finetune_keys: null - use_ema: False - - depth_stage_config: - target: ldm.modules.midas.api.MiDaSInference - params: - model_type: "dpt_hybrid" - - unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - image_size: 32 # unused - in_channels: 5 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_head_channels: 64 # need to fix for flash-attn - use_spatial_transformer: True - use_linear_in_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False - - first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - #attn_type: "vanilla-xformers" - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [ ] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity - - cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" - - diff --git a/configs/stable-diffusion/x4-upscaling.yaml b/configs/stable-diffusion/x4-upscaling.yaml deleted file mode 100644 index 2db0964af699f86d1891c761710a2d53f59b842c..0000000000000000000000000000000000000000 --- a/configs/stable-diffusion/x4-upscaling.yaml +++ /dev/null @@ -1,76 +0,0 @@ -model: - base_learning_rate: 1.0e-04 - target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion - params: - parameterization: "v" - low_scale_key: "lr" - linear_start: 0.0001 - linear_end: 0.02 - num_timesteps_cond: 1 - log_every_t: 200 - timesteps: 1000 - first_stage_key: "jpg" - cond_stage_key: "txt" - image_size: 128 - channels: 4 - cond_stage_trainable: false - conditioning_key: "hybrid-adm" - monitor: val/loss_simple_ema - scale_factor: 0.08333 - use_ema: False - - low_scale_config: - target: ldm.modules.diffusionmodules.upscaling.ImageConcatWithNoiseAugmentation - params: - noise_schedule_config: # image space - linear_start: 0.0001 - linear_end: 0.02 - max_noise_level: 350 - - unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - num_classes: 1000 # timesteps for noise conditioning (here constant, just need one) - image_size: 128 - in_channels: 7 - out_channels: 4 - model_channels: 256 - attention_resolutions: [ 2,4,8] - num_res_blocks: 2 - channel_mult: [ 1, 2, 2, 4] - disable_self_attentions: [True, True, True, False] - disable_middle_self_attn: False - num_heads: 8 - use_spatial_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False - use_linear_in_transformer: True - - first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - ddconfig: - # attn_type: "vanilla-xformers" this model needs efficient attention to be feasible on HR data, also the decoder seems to break in half precision (UNet is fine though) - double_z: True - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: [ 1,2,4 ] # num_down = len(ch_mult)-1 - num_res_blocks: 2 - attn_resolutions: [ ] - dropout: 0.0 - - lossconfig: - target: torch.nn.Identity - - cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" - diff --git a/environment.yaml b/environment.yaml deleted file mode 100644 index 4687b309b60ae2d6040fcb3f90a380cf6fb11b21..0000000000000000000000000000000000000000 --- a/environment.yaml +++ /dev/null @@ -1,29 +0,0 @@ -name: ldm -channels: - - pytorch - - defaults -dependencies: - - python=3.8.5 - - pip=20.3 - - cudatoolkit=11.3 - - pytorch=1.12.1 - - torchvision=0.13.1 - - numpy=1.23.1 - - pip: - - albumentations==1.3.0 - - opencv-python==4.6.0.66 - - imageio==2.9.0 - - imageio-ffmpeg==0.4.2 - - pytorch-lightning==1.4.2 - - omegaconf==2.1.1 - - test-tube>=0.7.5 - - streamlit==1.12.1 - - einops==0.3.0 - - transformers==4.19.2 - - webdataset==0.2.5 - - kornia==0.6 - - open_clip_torch==2.0.2 - - invisible-watermark>=0.1.5 - - streamlit-drawable-canvas==0.8.0 - - torchmetrics==0.6.0 - - -e . diff --git a/ldm/data/__init__.py b/ldm/data/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/ldm/data/util.py b/ldm/data/util.py deleted file mode 100644 index 5b60ceb2349e3bd7900ff325740e2022d2903b1c..0000000000000000000000000000000000000000 --- a/ldm/data/util.py +++ /dev/null @@ -1,24 +0,0 @@ -import torch - -from ldm.modules.midas.api import load_midas_transform - - -class AddMiDaS(object): - def __init__(self, model_type): - super().__init__() - self.transform = load_midas_transform(model_type) - - def pt2np(self, x): - x = ((x + 1.0) * .5).detach().cpu().numpy() - return x - - def np2pt(self, x): - x = torch.from_numpy(x) * 2 - 1. - return x - - def __call__(self, sample): - # sample['jpg'] is tensor hwc in [-1, 1] at this point - x = self.pt2np(sample['jpg']) - x = self.transform({"image": x})["image"] - sample['midas_in'] = x - return sample \ No newline at end of file diff --git a/ldm/models/autoencoder.py b/ldm/models/autoencoder.py deleted file mode 100644 index d122549995ce2cd64092c81a58419ed4a15a02fd..0000000000000000000000000000000000000000 --- a/ldm/models/autoencoder.py +++ /dev/null @@ -1,219 +0,0 @@ -import torch -import pytorch_lightning as pl -import torch.nn.functional as F -from contextlib import contextmanager - -from ldm.modules.diffusionmodules.model import Encoder, Decoder -from ldm.modules.distributions.distributions import DiagonalGaussianDistribution - -from ldm.util import instantiate_from_config -from ldm.modules.ema import LitEma - - -class AutoencoderKL(pl.LightningModule): - def __init__(self, - ddconfig, - lossconfig, - embed_dim, - ckpt_path=None, - ignore_keys=[], - image_key="image", - colorize_nlabels=None, - monitor=None, - ema_decay=None, - learn_logvar=False - ): - super().__init__() - self.learn_logvar = learn_logvar - self.image_key = image_key - self.encoder = Encoder(**ddconfig) - self.decoder = Decoder(**ddconfig) - self.loss = instantiate_from_config(lossconfig) - assert ddconfig["double_z"] - self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) - self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) - self.embed_dim = embed_dim - if colorize_nlabels is not None: - assert type(colorize_nlabels)==int - self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) - if monitor is not None: - self.monitor = monitor - - self.use_ema = ema_decay is not None - if self.use_ema: - self.ema_decay = ema_decay - assert 0. < ema_decay < 1. - self.model_ema = LitEma(self, decay=ema_decay) - print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") - - if ckpt_path is not None: - self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) - - def init_from_ckpt(self, path, ignore_keys=list()): - sd = torch.load(path, map_location="cpu")["state_dict"] - keys = list(sd.keys()) - for k in keys: - for ik in ignore_keys: - if k.startswith(ik): - print("Deleting key {} from state_dict.".format(k)) - del sd[k] - self.load_state_dict(sd, strict=False) - print(f"Restored from {path}") - - @contextmanager - def ema_scope(self, context=None): - if self.use_ema: - self.model_ema.store(self.parameters()) - self.model_ema.copy_to(self) - if context is not None: - print(f"{context}: Switched to EMA weights") - try: - yield None - finally: - if self.use_ema: - self.model_ema.restore(self.parameters()) - if context is not None: - print(f"{context}: Restored training weights") - - def on_train_batch_end(self, *args, **kwargs): - if self.use_ema: - self.model_ema(self) - - def encode(self, x): - h = self.encoder(x) - moments = self.quant_conv(h) - posterior = DiagonalGaussianDistribution(moments) - return posterior - - def decode(self, z): - z = self.post_quant_conv(z) - dec = self.decoder(z) - return dec - - def forward(self, input, sample_posterior=True): - posterior = self.encode(input) - if sample_posterior: - z = posterior.sample() - else: - z = posterior.mode() - dec = self.decode(z) - return dec, posterior - - def get_input(self, batch, k): - x = batch[k] - if len(x.shape) == 3: - x = x[..., None] - x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() - return x - - def training_step(self, batch, batch_idx, optimizer_idx): - inputs = self.get_input(batch, self.image_key) - reconstructions, posterior = self(inputs) - - if optimizer_idx == 0: - # train encoder+decoder+logvar - aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, - last_layer=self.get_last_layer(), split="train") - self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) - self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) - return aeloss - - if optimizer_idx == 1: - # train the discriminator - discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, - last_layer=self.get_last_layer(), split="train") - - self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) - self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) - return discloss - - def validation_step(self, batch, batch_idx): - log_dict = self._validation_step(batch, batch_idx) - with self.ema_scope(): - log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema") - return log_dict - - def _validation_step(self, batch, batch_idx, postfix=""): - inputs = self.get_input(batch, self.image_key) - reconstructions, posterior = self(inputs) - aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step, - last_layer=self.get_last_layer(), split="val"+postfix) - - discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step, - last_layer=self.get_last_layer(), split="val"+postfix) - - self.log(f"val{postfix}/rec_loss", log_dict_ae[f"val{postfix}/rec_loss"]) - self.log_dict(log_dict_ae) - self.log_dict(log_dict_disc) - return self.log_dict - - def configure_optimizers(self): - lr = self.learning_rate - ae_params_list = list(self.encoder.parameters()) + list(self.decoder.parameters()) + list( - self.quant_conv.parameters()) + list(self.post_quant_conv.parameters()) - if self.learn_logvar: - print(f"{self.__class__.__name__}: Learning logvar") - ae_params_list.append(self.loss.logvar) - opt_ae = torch.optim.Adam(ae_params_list, - lr=lr, betas=(0.5, 0.9)) - opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), - lr=lr, betas=(0.5, 0.9)) - return [opt_ae, opt_disc], [] - - def get_last_layer(self): - return self.decoder.conv_out.weight - - @torch.no_grad() - def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs): - log = dict() - x = self.get_input(batch, self.image_key) - x = x.to(self.device) - if not only_inputs: - xrec, posterior = self(x) - if x.shape[1] > 3: - # colorize with random projection - assert xrec.shape[1] > 3 - x = self.to_rgb(x) - xrec = self.to_rgb(xrec) - log["samples"] = self.decode(torch.randn_like(posterior.sample())) - log["reconstructions"] = xrec - if log_ema or self.use_ema: - with self.ema_scope(): - xrec_ema, posterior_ema = self(x) - if x.shape[1] > 3: - # colorize with random projection - assert xrec_ema.shape[1] > 3 - xrec_ema = self.to_rgb(xrec_ema) - log["samples_ema"] = self.decode(torch.randn_like(posterior_ema.sample())) - log["reconstructions_ema"] = xrec_ema - log["inputs"] = x - return log - - def to_rgb(self, x): - assert self.image_key == "segmentation" - if not hasattr(self, "colorize"): - self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) - x = F.conv2d(x, weight=self.colorize) - x = 2.*(x-x.min())/(x.max()-x.min()) - 1. - return x - - -class IdentityFirstStage(torch.nn.Module): - def __init__(self, *args, vq_interface=False, **kwargs): - self.vq_interface = vq_interface - super().__init__() - - def encode(self, x, *args, **kwargs): - return x - - def decode(self, x, *args, **kwargs): - return x - - def quantize(self, x, *args, **kwargs): - if self.vq_interface: - return x, None, [None, None, None] - return x - - def forward(self, x, *args, **kwargs): - return x - diff --git a/ldm/models/diffusion/__init__.py b/ldm/models/diffusion/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py deleted file mode 100644 index 27ead0ea914c64c747b64e690662899fb3801144..0000000000000000000000000000000000000000 --- a/ldm/models/diffusion/ddim.py +++ /dev/null @@ -1,336 +0,0 @@ -"""SAMPLING ONLY.""" - -import torch -import numpy as np -from tqdm import tqdm - -from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor - - -class DDIMSampler(object): - def __init__(self, model, schedule="linear", **kwargs): - super().__init__() - self.model = model - self.ddpm_num_timesteps = model.num_timesteps - self.schedule = schedule - - def register_buffer(self, name, attr): - if type(attr) == torch.Tensor: - if attr.device != torch.device("cuda"): - attr = attr.to(torch.device("cuda")) - setattr(self, name, attr) - - def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): - self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, - num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) - alphas_cumprod = self.model.alphas_cumprod - assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' - to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) - - self.register_buffer('betas', to_torch(self.model.betas)) - self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) - self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) - - # calculations for diffusion q(x_t | x_{t-1}) and others - self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) - self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) - self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) - self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) - self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) - - # ddim sampling parameters - ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), - ddim_timesteps=self.ddim_timesteps, - eta=ddim_eta,verbose=verbose) - self.register_buffer('ddim_sigmas', ddim_sigmas) - self.register_buffer('ddim_alphas', ddim_alphas) - self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) - self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) - sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( - (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( - 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) - self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) - - @torch.no_grad() - def sample(self, - S, - batch_size, - shape, - conditioning=None, - callback=None, - normals_sequence=None, - img_callback=None, - quantize_x0=False, - eta=0., - mask=None, - x0=None, - temperature=1., - noise_dropout=0., - score_corrector=None, - corrector_kwargs=None, - verbose=True, - x_T=None, - log_every_t=100, - unconditional_guidance_scale=1., - unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... - dynamic_threshold=None, - ucg_schedule=None, - **kwargs - ): - if conditioning is not None: - if isinstance(conditioning, dict): - ctmp = conditioning[list(conditioning.keys())[0]] - while isinstance(ctmp, list): ctmp = ctmp[0] - cbs = ctmp.shape[0] - if cbs != batch_size: - print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") - - elif isinstance(conditioning, list): - for ctmp in conditioning: - if ctmp.shape[0] != batch_size: - print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") - - else: - if conditioning.shape[0] != batch_size: - print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") - - self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) - # sampling - C, H, W = shape - size = (batch_size, C, H, W) - print(f'Data shape for DDIM sampling is {size}, eta {eta}') - - samples, intermediates = self.ddim_sampling(conditioning, size, - callback=callback, - img_callback=img_callback, - quantize_denoised=quantize_x0, - mask=mask, x0=x0, - ddim_use_original_steps=False, - noise_dropout=noise_dropout, - temperature=temperature, - score_corrector=score_corrector, - corrector_kwargs=corrector_kwargs, - x_T=x_T, - log_every_t=log_every_t, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning, - dynamic_threshold=dynamic_threshold, - ucg_schedule=ucg_schedule - ) - return samples, intermediates - - @torch.no_grad() - def ddim_sampling(self, cond, shape, - x_T=None, ddim_use_original_steps=False, - callback=None, timesteps=None, quantize_denoised=False, - mask=None, x0=None, img_callback=None, log_every_t=100, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None, - ucg_schedule=None): - device = self.model.betas.device - b = shape[0] - if x_T is None: - img = torch.randn(shape, device=device) - else: - img = x_T - - if timesteps is None: - timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps - elif timesteps is not None and not ddim_use_original_steps: - subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 - timesteps = self.ddim_timesteps[:subset_end] - - intermediates = {'x_inter': [img], 'pred_x0': [img]} - time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps) - total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] - print(f"Running DDIM Sampling with {total_steps} timesteps") - - iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) - - for i, step in enumerate(iterator): - index = total_steps - i - 1 - ts = torch.full((b,), step, device=device, dtype=torch.long) - - if mask is not None: - assert x0 is not None - img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? - img = img_orig * mask + (1. - mask) * img - - if ucg_schedule is not None: - assert len(ucg_schedule) == len(time_range) - unconditional_guidance_scale = ucg_schedule[i] - - outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, - quantize_denoised=quantize_denoised, temperature=temperature, - noise_dropout=noise_dropout, score_corrector=score_corrector, - corrector_kwargs=corrector_kwargs, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning, - dynamic_threshold=dynamic_threshold) - img, pred_x0 = outs - if callback: callback(i) - if img_callback: img_callback(pred_x0, i) - - if index % log_every_t == 0 or index == total_steps - 1: - intermediates['x_inter'].append(img) - intermediates['pred_x0'].append(pred_x0) - - return img, intermediates - - @torch.no_grad() - def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None, - dynamic_threshold=None): - b, *_, device = *x.shape, x.device - - if unconditional_conditioning is None or unconditional_guidance_scale == 1.: - model_output = self.model.apply_model(x, t, c) - else: - x_in = torch.cat([x] * 2) - t_in = torch.cat([t] * 2) - if isinstance(c, dict): - assert isinstance(unconditional_conditioning, dict) - c_in = dict() - for k in c: - if isinstance(c[k], list): - c_in[k] = [torch.cat([ - unconditional_conditioning[k][i], - c[k][i]]) for i in range(len(c[k]))] - else: - c_in[k] = torch.cat([ - unconditional_conditioning[k], - c[k]]) - elif isinstance(c, list): - c_in = list() - assert isinstance(unconditional_conditioning, list) - for i in range(len(c)): - c_in.append(torch.cat([unconditional_conditioning[i], c[i]])) - else: - c_in = torch.cat([unconditional_conditioning, c]) - model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) - model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond) - - if self.model.parameterization == "v": - e_t = self.model.predict_eps_from_z_and_v(x, t, model_output) - else: - e_t = model_output - - if score_corrector is not None: - assert self.model.parameterization == "eps", 'not implemented' - e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) - - alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas - alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev - sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas - sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas - # select parameters corresponding to the currently considered timestep - a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) - a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) - sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) - sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) - - # current prediction for x_0 - if self.model.parameterization != "v": - pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() - else: - pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output) - - if quantize_denoised: - pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) - - if dynamic_threshold is not None: - raise NotImplementedError() - - # direction pointing to x_t - dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t - noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature - if noise_dropout > 0.: - noise = torch.nn.functional.dropout(noise, p=noise_dropout) - x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise - return x_prev, pred_x0 - - @torch.no_grad() - def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None, - unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None): - num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0] - - assert t_enc <= num_reference_steps - num_steps = t_enc - - if use_original_steps: - alphas_next = self.alphas_cumprod[:num_steps] - alphas = self.alphas_cumprod_prev[:num_steps] - else: - alphas_next = self.ddim_alphas[:num_steps] - alphas = torch.tensor(self.ddim_alphas_prev[:num_steps]) - - x_next = x0 - intermediates = [] - inter_steps = [] - for i in tqdm(range(num_steps), desc='Encoding Image'): - t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long) - if unconditional_guidance_scale == 1.: - noise_pred = self.model.apply_model(x_next, t, c) - else: - assert unconditional_conditioning is not None - e_t_uncond, noise_pred = torch.chunk( - self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)), - torch.cat((unconditional_conditioning, c))), 2) - noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond) - - xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next - weighted_noise_pred = alphas_next[i].sqrt() * ( - (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred - x_next = xt_weighted + weighted_noise_pred - if return_intermediates and i % ( - num_steps // return_intermediates) == 0 and i < num_steps - 1: - intermediates.append(x_next) - inter_steps.append(i) - elif return_intermediates and i >= num_steps - 2: - intermediates.append(x_next) - inter_steps.append(i) - if callback: callback(i) - - out = {'x_encoded': x_next, 'intermediate_steps': inter_steps} - if return_intermediates: - out.update({'intermediates': intermediates}) - return x_next, out - - @torch.no_grad() - def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): - # fast, but does not allow for exact reconstruction - # t serves as an index to gather the correct alphas - if use_original_steps: - sqrt_alphas_cumprod = self.sqrt_alphas_cumprod - sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod - else: - sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) - sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas - - if noise is None: - noise = torch.randn_like(x0) - return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + - extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise) - - @torch.no_grad() - def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, - use_original_steps=False, callback=None): - - timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps - timesteps = timesteps[:t_start] - - time_range = np.flip(timesteps) - total_steps = timesteps.shape[0] - print(f"Running DDIM Sampling with {total_steps} timesteps") - - iterator = tqdm(time_range, desc='Decoding image', total=total_steps) - x_dec = x_latent - for i, step in enumerate(iterator): - index = total_steps - i - 1 - ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long) - x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning) - if callback: callback(i) - return x_dec \ No newline at end of file diff --git a/ldm/models/diffusion/ddpm.py b/ldm/models/diffusion/ddpm.py deleted file mode 100644 index ff53bd8a7b84bd4fc2cd1b41c8f6d5819d803d6e..0000000000000000000000000000000000000000 --- a/ldm/models/diffusion/ddpm.py +++ /dev/null @@ -1,1796 +0,0 @@ -""" -wild mixture of -https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py -https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py -https://github.com/CompVis/taming-transformers --- merci -""" - -import torch -import torch.nn as nn -import numpy as np -import pytorch_lightning as pl -from torch.optim.lr_scheduler import LambdaLR -from einops import rearrange, repeat -from contextlib import contextmanager, nullcontext -from functools import partial -import itertools -from tqdm import tqdm -from torchvision.utils import make_grid -from pytorch_lightning.utilities.distributed import rank_zero_only -from omegaconf import ListConfig - -from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config -from ldm.modules.ema import LitEma -from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution -from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL -from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like -from ldm.models.diffusion.ddim import DDIMSampler - - -__conditioning_keys__ = {'concat': 'c_concat', - 'crossattn': 'c_crossattn', - 'adm': 'y'} - - -def disabled_train(self, mode=True): - """Overwrite model.train with this function to make sure train/eval mode - does not change anymore.""" - return self - - -def uniform_on_device(r1, r2, shape, device): - return (r1 - r2) * torch.rand(*shape, device=device) + r2 - - -class DDPM(pl.LightningModule): - # classic DDPM with Gaussian diffusion, in image space - def __init__(self, - unet_config, - timesteps=1000, - beta_schedule="linear", - loss_type="l2", - ckpt_path=None, - ignore_keys=[], - load_only_unet=False, - monitor="val/loss", - use_ema=True, - first_stage_key="image", - image_size=256, - channels=3, - log_every_t=100, - clip_denoised=True, - linear_start=1e-4, - linear_end=2e-2, - cosine_s=8e-3, - given_betas=None, - original_elbo_weight=0., - v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta - l_simple_weight=1., - conditioning_key=None, - parameterization="eps", # all assuming fixed variance schedules - scheduler_config=None, - use_positional_encodings=False, - learn_logvar=False, - logvar_init=0., - make_it_fit=False, - ucg_training=None, - reset_ema=False, - reset_num_ema_updates=False, - ): - super().__init__() - assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"' - self.parameterization = parameterization - print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") - self.cond_stage_model = None - self.clip_denoised = clip_denoised - self.log_every_t = log_every_t - self.first_stage_key = first_stage_key - self.image_size = image_size # try conv? - self.channels = channels - self.use_positional_encodings = use_positional_encodings - self.model = DiffusionWrapper(unet_config, conditioning_key) - count_params(self.model, verbose=True) - self.use_ema = use_ema - if self.use_ema: - self.model_ema = LitEma(self.model) - print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") - - self.use_scheduler = scheduler_config is not None - if self.use_scheduler: - self.scheduler_config = scheduler_config - - self.v_posterior = v_posterior - self.original_elbo_weight = original_elbo_weight - self.l_simple_weight = l_simple_weight - - if monitor is not None: - self.monitor = monitor - self.make_it_fit = make_it_fit - if reset_ema: assert exists(ckpt_path) - if ckpt_path is not None: - self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet) - if reset_ema: - assert self.use_ema - print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.") - self.model_ema = LitEma(self.model) - if reset_num_ema_updates: - print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ") - assert self.use_ema - self.model_ema.reset_num_updates() - - self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps, - linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) - - self.loss_type = loss_type - - self.learn_logvar = learn_logvar - self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,)) - if self.learn_logvar: - self.logvar = nn.Parameter(self.logvar, requires_grad=True) - - self.ucg_training = ucg_training or dict() - if self.ucg_training: - self.ucg_prng = np.random.RandomState() - - def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, - linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): - if exists(given_betas): - betas = given_betas - else: - betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, - cosine_s=cosine_s) - alphas = 1. - betas - alphas_cumprod = np.cumprod(alphas, axis=0) - alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) - - timesteps, = betas.shape - self.num_timesteps = int(timesteps) - self.linear_start = linear_start - self.linear_end = linear_end - assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' - - to_torch = partial(torch.tensor, dtype=torch.float32) - - self.register_buffer('betas', to_torch(betas)) - self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) - self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) - - # calculations for diffusion q(x_t | x_{t-1}) and others - self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) - self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) - self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) - self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) - self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) - - # calculations for posterior q(x_{t-1} | x_t, x_0) - posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / ( - 1. - alphas_cumprod) + self.v_posterior * betas - # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) - self.register_buffer('posterior_variance', to_torch(posterior_variance)) - # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain - self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) - self.register_buffer('posterior_mean_coef1', to_torch( - betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) - self.register_buffer('posterior_mean_coef2', to_torch( - (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) - - if self.parameterization == "eps": - lvlb_weights = self.betas ** 2 / ( - 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)) - elif self.parameterization == "x0": - lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod)) - elif self.parameterization == "v": - lvlb_weights = torch.ones_like(self.betas ** 2 / ( - 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))) - else: - raise NotImplementedError("mu not supported") - # TODO how to choose this term - lvlb_weights[0] = lvlb_weights[1] - self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) - assert not torch.isnan(self.lvlb_weights).all() - - @contextmanager - def ema_scope(self, context=None): - if self.use_ema: - self.model_ema.store(self.model.parameters()) - self.model_ema.copy_to(self.model) - if context is not None: - print(f"{context}: Switched to EMA weights") - try: - yield None - finally: - if self.use_ema: - self.model_ema.restore(self.model.parameters()) - if context is not None: - print(f"{context}: Restored training weights") - - @torch.no_grad() - def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): - sd = torch.load(path, map_location="cpu") - if "state_dict" in list(sd.keys()): - sd = sd["state_dict"] - keys = list(sd.keys()) - for k in keys: - for ik in ignore_keys: - if k.startswith(ik): - print("Deleting key {} from state_dict.".format(k)) - del sd[k] - if self.make_it_fit: - n_params = len([name for name, _ in - itertools.chain(self.named_parameters(), - self.named_buffers())]) - for name, param in tqdm( - itertools.chain(self.named_parameters(), - self.named_buffers()), - desc="Fitting old weights to new weights", - total=n_params - ): - if not name in sd: - continue - old_shape = sd[name].shape - new_shape = param.shape - assert len(old_shape) == len(new_shape) - if len(new_shape) > 2: - # we only modify first two axes - assert new_shape[2:] == old_shape[2:] - # assumes first axis corresponds to output dim - if not new_shape == old_shape: - new_param = param.clone() - old_param = sd[name] - if len(new_shape) == 1: - for i in range(new_param.shape[0]): - new_param[i] = old_param[i % old_shape[0]] - elif len(new_shape) >= 2: - for i in range(new_param.shape[0]): - for j in range(new_param.shape[1]): - new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]] - - n_used_old = torch.ones(old_shape[1]) - for j in range(new_param.shape[1]): - n_used_old[j % old_shape[1]] += 1 - n_used_new = torch.zeros(new_shape[1]) - for j in range(new_param.shape[1]): - n_used_new[j] = n_used_old[j % old_shape[1]] - - n_used_new = n_used_new[None, :] - while len(n_used_new.shape) < len(new_shape): - n_used_new = n_used_new.unsqueeze(-1) - new_param /= n_used_new - - sd[name] = new_param - - missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( - sd, strict=False) - print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") - if len(missing) > 0: - print(f"Missing Keys:\n {missing}") - if len(unexpected) > 0: - print(f"\nUnexpected Keys:\n {unexpected}") - - def q_mean_variance(self, x_start, t): - """ - Get the distribution q(x_t | x_0). - :param x_start: the [N x C x ...] tensor of noiseless inputs. - :param t: the number of diffusion steps (minus 1). Here, 0 means one step. - :return: A tuple (mean, variance, log_variance), all of x_start's shape. - """ - mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start) - variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) - log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) - return mean, variance, log_variance - - def predict_start_from_noise(self, x_t, t, noise): - return ( - extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - - extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise - ) - - def predict_start_from_z_and_v(self, x_t, t, v): - # self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) - # self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) - return ( - extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t - - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v - ) - - def predict_eps_from_z_and_v(self, x_t, t, v): - return ( - extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v + - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t - ) - - def q_posterior(self, x_start, x_t, t): - posterior_mean = ( - extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + - extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t - ) - posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) - posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape) - return posterior_mean, posterior_variance, posterior_log_variance_clipped - - def p_mean_variance(self, x, t, clip_denoised: bool): - model_out = self.model(x, t) - if self.parameterization == "eps": - x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) - elif self.parameterization == "x0": - x_recon = model_out - if clip_denoised: - x_recon.clamp_(-1., 1.) - - model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) - return model_mean, posterior_variance, posterior_log_variance - - @torch.no_grad() - def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): - b, *_, device = *x.shape, x.device - model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised) - noise = noise_like(x.shape, device, repeat_noise) - # no noise when t == 0 - nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) - return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise - - @torch.no_grad() - def p_sample_loop(self, shape, return_intermediates=False): - device = self.betas.device - b = shape[0] - img = torch.randn(shape, device=device) - intermediates = [img] - for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps): - img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long), - clip_denoised=self.clip_denoised) - if i % self.log_every_t == 0 or i == self.num_timesteps - 1: - intermediates.append(img) - if return_intermediates: - return img, intermediates - return img - - @torch.no_grad() - def sample(self, batch_size=16, return_intermediates=False): - image_size = self.image_size - channels = self.channels - return self.p_sample_loop((batch_size, channels, image_size, image_size), - return_intermediates=return_intermediates) - - def q_sample(self, x_start, t, noise=None): - noise = default(noise, lambda: torch.randn_like(x_start)) - return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) - - def get_v(self, x, noise, t): - return ( - extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise - - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x - ) - - def get_loss(self, pred, target, mean=True): - if self.loss_type == 'l1': - loss = (target - pred).abs() - if mean: - loss = loss.mean() - elif self.loss_type == 'l2': - if mean: - loss = torch.nn.functional.mse_loss(target, pred) - else: - loss = torch.nn.functional.mse_loss(target, pred, reduction='none') - else: - raise NotImplementedError("unknown loss type '{loss_type}'") - - return loss - - def p_losses(self, x_start, t, noise=None): - noise = default(noise, lambda: torch.randn_like(x_start)) - x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) - model_out = self.model(x_noisy, t) - - loss_dict = {} - if self.parameterization == "eps": - target = noise - elif self.parameterization == "x0": - target = x_start - elif self.parameterization == "v": - target = self.get_v(x_start, noise, t) - else: - raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported") - - loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3]) - - log_prefix = 'train' if self.training else 'val' - - loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()}) - loss_simple = loss.mean() * self.l_simple_weight - - loss_vlb = (self.lvlb_weights[t] * loss).mean() - loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb}) - - loss = loss_simple + self.original_elbo_weight * loss_vlb - - loss_dict.update({f'{log_prefix}/loss': loss}) - - return loss, loss_dict - - def forward(self, x, *args, **kwargs): - # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size - # assert h == img_size and w == img_size, f'height and width of image must be {img_size}' - t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() - return self.p_losses(x, t, *args, **kwargs) - - def get_input(self, batch, k): - x = batch[k] - if len(x.shape) == 3: - x = x[..., None] - x = rearrange(x, 'b h w c -> b c h w') - x = x.to(memory_format=torch.contiguous_format).float() - return x - - def shared_step(self, batch): - x = self.get_input(batch, self.first_stage_key) - loss, loss_dict = self(x) - return loss, loss_dict - - def training_step(self, batch, batch_idx): - for k in self.ucg_training: - p = self.ucg_training[k]["p"] - val = self.ucg_training[k]["val"] - if val is None: - val = "" - for i in range(len(batch[k])): - if self.ucg_prng.choice(2, p=[1 - p, p]): - batch[k][i] = val - - loss, loss_dict = self.shared_step(batch) - - self.log_dict(loss_dict, prog_bar=True, - logger=True, on_step=True, on_epoch=True) - - self.log("global_step", self.global_step, - prog_bar=True, logger=True, on_step=True, on_epoch=False) - - if self.use_scheduler: - lr = self.optimizers().param_groups[0]['lr'] - self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False) - - return loss - - @torch.no_grad() - def validation_step(self, batch, batch_idx): - _, loss_dict_no_ema = self.shared_step(batch) - with self.ema_scope(): - _, loss_dict_ema = self.shared_step(batch) - loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema} - self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) - self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) - - def on_train_batch_end(self, *args, **kwargs): - if self.use_ema: - self.model_ema(self.model) - - def _get_rows_from_list(self, samples): - n_imgs_per_row = len(samples) - denoise_grid = rearrange(samples, 'n b c h w -> b n c h w') - denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') - denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) - return denoise_grid - - @torch.no_grad() - def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): - log = dict() - x = self.get_input(batch, self.first_stage_key) - N = min(x.shape[0], N) - n_row = min(x.shape[0], n_row) - x = x.to(self.device)[:N] - log["inputs"] = x - - # get diffusion row - diffusion_row = list() - x_start = x[:n_row] - - for t in range(self.num_timesteps): - if t % self.log_every_t == 0 or t == self.num_timesteps - 1: - t = repeat(torch.tensor([t]), '1 -> b', b=n_row) - t = t.to(self.device).long() - noise = torch.randn_like(x_start) - x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) - diffusion_row.append(x_noisy) - - log["diffusion_row"] = self._get_rows_from_list(diffusion_row) - - if sample: - # get denoise row - with self.ema_scope("Plotting"): - samples, denoise_row = self.sample(batch_size=N, return_intermediates=True) - - log["samples"] = samples - log["denoise_row"] = self._get_rows_from_list(denoise_row) - - if return_keys: - if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: - return log - else: - return {key: log[key] for key in return_keys} - return log - - def configure_optimizers(self): - lr = self.learning_rate - params = list(self.model.parameters()) - if self.learn_logvar: - params = params + [self.logvar] - opt = torch.optim.AdamW(params, lr=lr) - return opt - - -class LatentDiffusion(DDPM): - """main class""" - - def __init__(self, - first_stage_config, - cond_stage_config, - num_timesteps_cond=None, - cond_stage_key="image", - cond_stage_trainable=False, - concat_mode=True, - cond_stage_forward=None, - conditioning_key=None, - scale_factor=1.0, - scale_by_std=False, - force_null_conditioning=False, - *args, **kwargs): - self.force_null_conditioning = force_null_conditioning - self.num_timesteps_cond = default(num_timesteps_cond, 1) - self.scale_by_std = scale_by_std - assert self.num_timesteps_cond <= kwargs['timesteps'] - # for backwards compatibility after implementation of DiffusionWrapper - if conditioning_key is None: - conditioning_key = 'concat' if concat_mode else 'crossattn' - if cond_stage_config == '__is_unconditional__' and not self.force_null_conditioning: - conditioning_key = None - ckpt_path = kwargs.pop("ckpt_path", None) - reset_ema = kwargs.pop("reset_ema", False) - reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False) - ignore_keys = kwargs.pop("ignore_keys", []) - super().__init__(conditioning_key=conditioning_key, *args, **kwargs) - self.concat_mode = concat_mode - self.cond_stage_trainable = cond_stage_trainable - self.cond_stage_key = cond_stage_key - try: - self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 - except: - self.num_downs = 0 - if not scale_by_std: - self.scale_factor = scale_factor - else: - self.register_buffer('scale_factor', torch.tensor(scale_factor)) - self.instantiate_first_stage(first_stage_config) - self.instantiate_cond_stage(cond_stage_config) - self.cond_stage_forward = cond_stage_forward - self.clip_denoised = False - self.bbox_tokenizer = None - - self.restarted_from_ckpt = False - if ckpt_path is not None: - self.init_from_ckpt(ckpt_path, ignore_keys) - self.restarted_from_ckpt = True - if reset_ema: - assert self.use_ema - print( - f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.") - self.model_ema = LitEma(self.model) - if reset_num_ema_updates: - print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ") - assert self.use_ema - self.model_ema.reset_num_updates() - - def make_cond_schedule(self, ): - self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) - ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() - self.cond_ids[:self.num_timesteps_cond] = ids - - @rank_zero_only - @torch.no_grad() - def on_train_batch_start(self, batch, batch_idx, dataloader_idx): - # only for very first batch - if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: - assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' - # set rescale weight to 1./std of encodings - print("### USING STD-RESCALING ###") - x = super().get_input(batch, self.first_stage_key) - x = x.to(self.device) - encoder_posterior = self.encode_first_stage(x) - z = self.get_first_stage_encoding(encoder_posterior).detach() - del self.scale_factor - self.register_buffer('scale_factor', 1. / z.flatten().std()) - print(f"setting self.scale_factor to {self.scale_factor}") - print("### USING STD-RESCALING ###") - - def register_schedule(self, - given_betas=None, beta_schedule="linear", timesteps=1000, - linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): - super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) - - self.shorten_cond_schedule = self.num_timesteps_cond > 1 - if self.shorten_cond_schedule: - self.make_cond_schedule() - - def instantiate_first_stage(self, config): - model = instantiate_from_config(config) - self.first_stage_model = model.eval() - self.first_stage_model.train = disabled_train - for param in self.first_stage_model.parameters(): - param.requires_grad = False - - def instantiate_cond_stage(self, config): - if not self.cond_stage_trainable: - if config == "__is_first_stage__": - print("Using first stage also as cond stage.") - self.cond_stage_model = self.first_stage_model - elif config == "__is_unconditional__": - print(f"Training {self.__class__.__name__} as an unconditional model.") - self.cond_stage_model = None - # self.be_unconditional = True - else: - model = instantiate_from_config(config) - self.cond_stage_model = model.eval() - self.cond_stage_model.train = disabled_train - for param in self.cond_stage_model.parameters(): - param.requires_grad = False - else: - assert config != '__is_first_stage__' - assert config != '__is_unconditional__' - model = instantiate_from_config(config) - self.cond_stage_model = model - - def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): - denoise_row = [] - for zd in tqdm(samples, desc=desc): - denoise_row.append(self.decode_first_stage(zd.to(self.device), - force_not_quantize=force_no_decoder_quantization)) - n_imgs_per_row = len(denoise_row) - denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W - denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') - denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') - denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) - return denoise_grid - - def get_first_stage_encoding(self, encoder_posterior): - if isinstance(encoder_posterior, DiagonalGaussianDistribution): - z = encoder_posterior.sample() - elif isinstance(encoder_posterior, torch.Tensor): - z = encoder_posterior - else: - raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") - return self.scale_factor * z - - def get_learned_conditioning(self, c): - if self.cond_stage_forward is None: - if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): - c = self.cond_stage_model.encode(c) - if isinstance(c, DiagonalGaussianDistribution): - c = c.mode() - else: - c = self.cond_stage_model(c) - else: - assert hasattr(self.cond_stage_model, self.cond_stage_forward) - c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) - return c - - def meshgrid(self, h, w): - y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) - x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) - - arr = torch.cat([y, x], dim=-1) - return arr - - def delta_border(self, h, w): - """ - :param h: height - :param w: width - :return: normalized distance to image border, - wtith min distance = 0 at border and max dist = 0.5 at image center - """ - lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) - arr = self.meshgrid(h, w) / lower_right_corner - dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] - dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] - edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] - return edge_dist - - def get_weighting(self, h, w, Ly, Lx, device): - weighting = self.delta_border(h, w) - weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], - self.split_input_params["clip_max_weight"], ) - weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) - - if self.split_input_params["tie_braker"]: - L_weighting = self.delta_border(Ly, Lx) - L_weighting = torch.clip(L_weighting, - self.split_input_params["clip_min_tie_weight"], - self.split_input_params["clip_max_tie_weight"]) - - L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) - weighting = weighting * L_weighting - return weighting - - def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code - """ - :param x: img of size (bs, c, h, w) - :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) - """ - bs, nc, h, w = x.shape - - # number of crops in image - Ly = (h - kernel_size[0]) // stride[0] + 1 - Lx = (w - kernel_size[1]) // stride[1] + 1 - - if uf == 1 and df == 1: - fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) - unfold = torch.nn.Unfold(**fold_params) - - fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) - - weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) - normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap - weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) - - elif uf > 1 and df == 1: - fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) - unfold = torch.nn.Unfold(**fold_params) - - fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), - dilation=1, padding=0, - stride=(stride[0] * uf, stride[1] * uf)) - fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) - - weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) - normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap - weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) - - elif df > 1 and uf == 1: - fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) - unfold = torch.nn.Unfold(**fold_params) - - fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df), - dilation=1, padding=0, - stride=(stride[0] // df, stride[1] // df)) - fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) - - weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) - normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap - weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) - - else: - raise NotImplementedError - - return fold, unfold, normalization, weighting - - @torch.no_grad() - def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, - cond_key=None, return_original_cond=False, bs=None, return_x=False): - x = super().get_input(batch, k) - if bs is not None: - x = x[:bs] - x = x.to(self.device) - encoder_posterior = self.encode_first_stage(x) - z = self.get_first_stage_encoding(encoder_posterior).detach() - - if self.model.conditioning_key is not None and not self.force_null_conditioning: - if cond_key is None: - cond_key = self.cond_stage_key - if cond_key != self.first_stage_key: - if cond_key in ['caption', 'coordinates_bbox', "txt"]: - xc = batch[cond_key] - elif cond_key in ['class_label', 'cls']: - xc = batch - else: - xc = super().get_input(batch, cond_key).to(self.device) - else: - xc = x - if not self.cond_stage_trainable or force_c_encode: - if isinstance(xc, dict) or isinstance(xc, list): - c = self.get_learned_conditioning(xc) - else: - c = self.get_learned_conditioning(xc.to(self.device)) - else: - c = xc - if bs is not None: - c = c[:bs] - - if self.use_positional_encodings: - pos_x, pos_y = self.compute_latent_shifts(batch) - ckey = __conditioning_keys__[self.model.conditioning_key] - c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y} - - else: - c = None - xc = None - if self.use_positional_encodings: - pos_x, pos_y = self.compute_latent_shifts(batch) - c = {'pos_x': pos_x, 'pos_y': pos_y} - out = [z, c] - if return_first_stage_outputs: - xrec = self.decode_first_stage(z) - out.extend([x, xrec]) - if return_x: - out.extend([x]) - if return_original_cond: - out.append(xc) - return out - - @torch.no_grad() - def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): - if predict_cids: - if z.dim() == 4: - z = torch.argmax(z.exp(), dim=1).long() - z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) - z = rearrange(z, 'b h w c -> b c h w').contiguous() - - z = 1. / self.scale_factor * z - return self.first_stage_model.decode(z) - - @torch.no_grad() - def encode_first_stage(self, x): - return self.first_stage_model.encode(x) - - def shared_step(self, batch, **kwargs): - x, c = self.get_input(batch, self.first_stage_key) - loss = self(x, c) - return loss - - def forward(self, x, c, *args, **kwargs): - t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() - if self.model.conditioning_key is not None: - assert c is not None - if self.cond_stage_trainable: - c = self.get_learned_conditioning(c) - if self.shorten_cond_schedule: # TODO: drop this option - tc = self.cond_ids[t].to(self.device) - c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) - return self.p_losses(x, c, t, *args, **kwargs) - - def apply_model(self, x_noisy, t, cond, return_ids=False): - if isinstance(cond, dict): - # hybrid case, cond is expected to be a dict - pass - else: - if not isinstance(cond, list): - cond = [cond] - key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' - cond = {key: cond} - - x_recon = self.model(x_noisy, t, **cond) - - if isinstance(x_recon, tuple) and not return_ids: - return x_recon[0] - else: - return x_recon - - def _predict_eps_from_xstart(self, x_t, t, pred_xstart): - return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ - extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) - - def _prior_bpd(self, x_start): - """ - Get the prior KL term for the variational lower-bound, measured in - bits-per-dim. - This term can't be optimized, as it only depends on the encoder. - :param x_start: the [N x C x ...] tensor of inputs. - :return: a batch of [N] KL values (in bits), one per batch element. - """ - batch_size = x_start.shape[0] - t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) - qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) - kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) - return mean_flat(kl_prior) / np.log(2.0) - - def p_losses(self, x_start, cond, t, noise=None): - noise = default(noise, lambda: torch.randn_like(x_start)) - x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) - model_output = self.apply_model(x_noisy, t, cond) - - loss_dict = {} - prefix = 'train' if self.training else 'val' - - if self.parameterization == "x0": - target = x_start - elif self.parameterization == "eps": - target = noise - elif self.parameterization == "v": - target = self.get_v(x_start, noise, t) - else: - raise NotImplementedError() - - loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) - loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) - - logvar_t = self.logvar[t].to(self.device) - loss = loss_simple / torch.exp(logvar_t) + logvar_t - # loss = loss_simple / torch.exp(self.logvar) + self.logvar - if self.learn_logvar: - loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) - loss_dict.update({'logvar': self.logvar.data.mean()}) - - loss = self.l_simple_weight * loss.mean() - - loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) - loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() - loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) - loss += (self.original_elbo_weight * loss_vlb) - loss_dict.update({f'{prefix}/loss': loss}) - - return loss, loss_dict - - def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, - return_x0=False, score_corrector=None, corrector_kwargs=None): - t_in = t - model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) - - if score_corrector is not None: - assert self.parameterization == "eps" - model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) - - if return_codebook_ids: - model_out, logits = model_out - - if self.parameterization == "eps": - x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) - elif self.parameterization == "x0": - x_recon = model_out - else: - raise NotImplementedError() - - if clip_denoised: - x_recon.clamp_(-1., 1.) - if quantize_denoised: - x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) - model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) - if return_codebook_ids: - return model_mean, posterior_variance, posterior_log_variance, logits - elif return_x0: - return model_mean, posterior_variance, posterior_log_variance, x_recon - else: - return model_mean, posterior_variance, posterior_log_variance - - @torch.no_grad() - def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, - return_codebook_ids=False, quantize_denoised=False, return_x0=False, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): - b, *_, device = *x.shape, x.device - outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, - return_codebook_ids=return_codebook_ids, - quantize_denoised=quantize_denoised, - return_x0=return_x0, - score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) - if return_codebook_ids: - raise DeprecationWarning("Support dropped.") - model_mean, _, model_log_variance, logits = outputs - elif return_x0: - model_mean, _, model_log_variance, x0 = outputs - else: - model_mean, _, model_log_variance = outputs - - noise = noise_like(x.shape, device, repeat_noise) * temperature - if noise_dropout > 0.: - noise = torch.nn.functional.dropout(noise, p=noise_dropout) - # no noise when t == 0 - nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) - - if return_codebook_ids: - return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) - if return_x0: - return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 - else: - return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise - - @torch.no_grad() - def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, - img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., - score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, - log_every_t=None): - if not log_every_t: - log_every_t = self.log_every_t - timesteps = self.num_timesteps - if batch_size is not None: - b = batch_size if batch_size is not None else shape[0] - shape = [batch_size] + list(shape) - else: - b = batch_size = shape[0] - if x_T is None: - img = torch.randn(shape, device=self.device) - else: - img = x_T - intermediates = [] - if cond is not None: - if isinstance(cond, dict): - cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else - list(map(lambda x: x[:batch_size], cond[key])) for key in cond} - else: - cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] - - if start_T is not None: - timesteps = min(timesteps, start_T) - iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', - total=timesteps) if verbose else reversed( - range(0, timesteps)) - if type(temperature) == float: - temperature = [temperature] * timesteps - - for i in iterator: - ts = torch.full((b,), i, device=self.device, dtype=torch.long) - if self.shorten_cond_schedule: - assert self.model.conditioning_key != 'hybrid' - tc = self.cond_ids[ts].to(cond.device) - cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) - - img, x0_partial = self.p_sample(img, cond, ts, - clip_denoised=self.clip_denoised, - quantize_denoised=quantize_denoised, return_x0=True, - temperature=temperature[i], noise_dropout=noise_dropout, - score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) - if mask is not None: - assert x0 is not None - img_orig = self.q_sample(x0, ts) - img = img_orig * mask + (1. - mask) * img - - if i % log_every_t == 0 or i == timesteps - 1: - intermediates.append(x0_partial) - if callback: callback(i) - if img_callback: img_callback(img, i) - return img, intermediates - - @torch.no_grad() - def p_sample_loop(self, cond, shape, return_intermediates=False, - x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, - mask=None, x0=None, img_callback=None, start_T=None, - log_every_t=None): - - if not log_every_t: - log_every_t = self.log_every_t - device = self.betas.device - b = shape[0] - if x_T is None: - img = torch.randn(shape, device=device) - else: - img = x_T - - intermediates = [img] - if timesteps is None: - timesteps = self.num_timesteps - - if start_T is not None: - timesteps = min(timesteps, start_T) - iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed( - range(0, timesteps)) - - if mask is not None: - assert x0 is not None - assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match - - for i in iterator: - ts = torch.full((b,), i, device=device, dtype=torch.long) - if self.shorten_cond_schedule: - assert self.model.conditioning_key != 'hybrid' - tc = self.cond_ids[ts].to(cond.device) - cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) - - img = self.p_sample(img, cond, ts, - clip_denoised=self.clip_denoised, - quantize_denoised=quantize_denoised) - if mask is not None: - img_orig = self.q_sample(x0, ts) - img = img_orig * mask + (1. - mask) * img - - if i % log_every_t == 0 or i == timesteps - 1: - intermediates.append(img) - if callback: callback(i) - if img_callback: img_callback(img, i) - - if return_intermediates: - return img, intermediates - return img - - @torch.no_grad() - def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, - verbose=True, timesteps=None, quantize_denoised=False, - mask=None, x0=None, shape=None, **kwargs): - if shape is None: - shape = (batch_size, self.channels, self.image_size, self.image_size) - if cond is not None: - if isinstance(cond, dict): - cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else - list(map(lambda x: x[:batch_size], cond[key])) for key in cond} - else: - cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] - return self.p_sample_loop(cond, - shape, - return_intermediates=return_intermediates, x_T=x_T, - verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, - mask=mask, x0=x0) - - @torch.no_grad() - def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs): - if ddim: - ddim_sampler = DDIMSampler(self) - shape = (self.channels, self.image_size, self.image_size) - samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size, - shape, cond, verbose=False, **kwargs) - - else: - samples, intermediates = self.sample(cond=cond, batch_size=batch_size, - return_intermediates=True, **kwargs) - - return samples, intermediates - - @torch.no_grad() - def get_unconditional_conditioning(self, batch_size, null_label=None): - if null_label is not None: - xc = null_label - if isinstance(xc, ListConfig): - xc = list(xc) - if isinstance(xc, dict) or isinstance(xc, list): - c = self.get_learned_conditioning(xc) - else: - if hasattr(xc, "to"): - xc = xc.to(self.device) - c = self.get_learned_conditioning(xc) - else: - if self.cond_stage_key in ["class_label", "cls"]: - xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device) - return self.get_learned_conditioning(xc) - else: - raise NotImplementedError("todo") - if isinstance(c, list): # in case the encoder gives us a list - for i in range(len(c)): - c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device) - else: - c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device) - return c - - @torch.no_grad() - def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None, - quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, - plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None, - use_ema_scope=True, - **kwargs): - ema_scope = self.ema_scope if use_ema_scope else nullcontext - use_ddim = ddim_steps is not None - - log = dict() - z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, - return_first_stage_outputs=True, - force_c_encode=True, - return_original_cond=True, - bs=N) - N = min(x.shape[0], N) - n_row = min(x.shape[0], n_row) - log["inputs"] = x - log["reconstruction"] = xrec - if self.model.conditioning_key is not None: - if hasattr(self.cond_stage_model, "decode"): - xc = self.cond_stage_model.decode(c) - log["conditioning"] = xc - elif self.cond_stage_key in ["caption", "txt"]: - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25) - log["conditioning"] = xc - elif self.cond_stage_key in ['class_label', "cls"]: - try: - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25) - log['conditioning'] = xc - except KeyError: - # probably no "human_label" in batch - pass - elif isimage(xc): - log["conditioning"] = xc - if ismap(xc): - log["original_conditioning"] = self.to_rgb(xc) - - if plot_diffusion_rows: - # get diffusion row - diffusion_row = list() - z_start = z[:n_row] - for t in range(self.num_timesteps): - if t % self.log_every_t == 0 or t == self.num_timesteps - 1: - t = repeat(torch.tensor([t]), '1 -> b', b=n_row) - t = t.to(self.device).long() - noise = torch.randn_like(z_start) - z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) - diffusion_row.append(self.decode_first_stage(z_noisy)) - - diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W - diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') - diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') - diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) - log["diffusion_row"] = diffusion_grid - - if sample: - # get denoise row - with ema_scope("Sampling"): - samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta) - # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) - x_samples = self.decode_first_stage(samples) - log["samples"] = x_samples - if plot_denoise_rows: - denoise_grid = self._get_denoise_row_from_list(z_denoise_row) - log["denoise_row"] = denoise_grid - - if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( - self.first_stage_model, IdentityFirstStage): - # also display when quantizing x0 while sampling - with ema_scope("Plotting Quantized Denoised"): - samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta, - quantize_denoised=True) - # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, - # quantize_denoised=True) - x_samples = self.decode_first_stage(samples.to(self.device)) - log["samples_x0_quantized"] = x_samples - - if unconditional_guidance_scale > 1.0: - uc = self.get_unconditional_conditioning(N, unconditional_guidance_label) - if self.model.conditioning_key == "crossattn-adm": - uc = {"c_crossattn": [uc], "c_adm": c["c_adm"]} - with ema_scope("Sampling with classifier-free guidance"): - samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=uc, - ) - x_samples_cfg = self.decode_first_stage(samples_cfg) - log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg - - if inpaint: - # make a simple center square - b, h, w = z.shape[0], z.shape[2], z.shape[3] - mask = torch.ones(N, h, w).to(self.device) - # zeros will be filled in - mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. - mask = mask[:, None, ...] - with ema_scope("Plotting Inpaint"): - samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta, - ddim_steps=ddim_steps, x0=z[:N], mask=mask) - x_samples = self.decode_first_stage(samples.to(self.device)) - log["samples_inpainting"] = x_samples - log["mask"] = mask - - # outpaint - mask = 1. - mask - with ema_scope("Plotting Outpaint"): - samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta, - ddim_steps=ddim_steps, x0=z[:N], mask=mask) - x_samples = self.decode_first_stage(samples.to(self.device)) - log["samples_outpainting"] = x_samples - - if plot_progressive_rows: - with ema_scope("Plotting Progressives"): - img, progressives = self.progressive_denoising(c, - shape=(self.channels, self.image_size, self.image_size), - batch_size=N) - prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") - log["progressive_row"] = prog_row - - if return_keys: - if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: - return log - else: - return {key: log[key] for key in return_keys} - return log - - def configure_optimizers(self): - lr = self.learning_rate - params = list(self.model.parameters()) - if self.cond_stage_trainable: - print(f"{self.__class__.__name__}: Also optimizing conditioner params!") - params = params + list(self.cond_stage_model.parameters()) - if self.learn_logvar: - print('Diffusion model optimizing logvar') - params.append(self.logvar) - opt = torch.optim.AdamW(params, lr=lr) - if self.use_scheduler: - assert 'target' in self.scheduler_config - scheduler = instantiate_from_config(self.scheduler_config) - - print("Setting up LambdaLR scheduler...") - scheduler = [ - { - 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), - 'interval': 'step', - 'frequency': 1 - }] - return [opt], scheduler - return opt - - @torch.no_grad() - def to_rgb(self, x): - x = x.float() - if not hasattr(self, "colorize"): - self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) - x = nn.functional.conv2d(x, weight=self.colorize) - x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. - return x - - -class DiffusionWrapper(pl.LightningModule): - def __init__(self, diff_model_config, conditioning_key): - super().__init__() - self.sequential_cross_attn = diff_model_config.pop("sequential_crossattn", False) - self.diffusion_model = instantiate_from_config(diff_model_config) - self.conditioning_key = conditioning_key - assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm', 'crossattn-adm'] - - def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None): - if self.conditioning_key is None: - out = self.diffusion_model(x, t) - elif self.conditioning_key == 'concat': - xc = torch.cat([x] + c_concat, dim=1) - out = self.diffusion_model(xc, t) - elif self.conditioning_key == 'crossattn': - if not self.sequential_cross_attn: - cc = torch.cat(c_crossattn, 1) - else: - cc = c_crossattn - out = self.diffusion_model(x, t, context=cc) - elif self.conditioning_key == 'hybrid': - xc = torch.cat([x] + c_concat, dim=1) - cc = torch.cat(c_crossattn, 1) - out = self.diffusion_model(xc, t, context=cc) - elif self.conditioning_key == 'hybrid-adm': - assert c_adm is not None - xc = torch.cat([x] + c_concat, dim=1) - cc = torch.cat(c_crossattn, 1) - out = self.diffusion_model(xc, t, context=cc, y=c_adm) - elif self.conditioning_key == 'crossattn-adm': - assert c_adm is not None - cc = torch.cat(c_crossattn, 1) - out = self.diffusion_model(x, t, context=cc, y=c_adm) - elif self.conditioning_key == 'adm': - cc = c_crossattn[0] - out = self.diffusion_model(x, t, y=cc) - else: - raise NotImplementedError() - - return out - - -class LatentUpscaleDiffusion(LatentDiffusion): - def __init__(self, *args, low_scale_config, low_scale_key="LR", noise_level_key=None, **kwargs): - super().__init__(*args, **kwargs) - # assumes that neither the cond_stage nor the low_scale_model contain trainable params - assert not self.cond_stage_trainable - self.instantiate_low_stage(low_scale_config) - self.low_scale_key = low_scale_key - self.noise_level_key = noise_level_key - - def instantiate_low_stage(self, config): - model = instantiate_from_config(config) - self.low_scale_model = model.eval() - self.low_scale_model.train = disabled_train - for param in self.low_scale_model.parameters(): - param.requires_grad = False - - @torch.no_grad() - def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False): - if not log_mode: - z, c = super().get_input(batch, k, force_c_encode=True, bs=bs) - else: - z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, - force_c_encode=True, return_original_cond=True, bs=bs) - x_low = batch[self.low_scale_key][:bs] - x_low = rearrange(x_low, 'b h w c -> b c h w') - x_low = x_low.to(memory_format=torch.contiguous_format).float() - zx, noise_level = self.low_scale_model(x_low) - if self.noise_level_key is not None: - # get noise level from batch instead, e.g. when extracting a custom noise level for bsr - raise NotImplementedError('TODO') - - all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level} - if log_mode: - # TODO: maybe disable if too expensive - x_low_rec = self.low_scale_model.decode(zx) - return z, all_conds, x, xrec, xc, x_low, x_low_rec, noise_level - return z, all_conds - - @torch.no_grad() - def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, - plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, - unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True, - **kwargs): - ema_scope = self.ema_scope if use_ema_scope else nullcontext - use_ddim = ddim_steps is not None - - log = dict() - z, c, x, xrec, xc, x_low, x_low_rec, noise_level = self.get_input(batch, self.first_stage_key, bs=N, - log_mode=True) - N = min(x.shape[0], N) - n_row = min(x.shape[0], n_row) - log["inputs"] = x - log["reconstruction"] = xrec - log["x_lr"] = x_low - log[f"x_lr_rec_@noise_levels{'-'.join(map(lambda x: str(x), list(noise_level.cpu().numpy())))}"] = x_low_rec - if self.model.conditioning_key is not None: - if hasattr(self.cond_stage_model, "decode"): - xc = self.cond_stage_model.decode(c) - log["conditioning"] = xc - elif self.cond_stage_key in ["caption", "txt"]: - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25) - log["conditioning"] = xc - elif self.cond_stage_key in ['class_label', 'cls']: - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25) - log['conditioning'] = xc - elif isimage(xc): - log["conditioning"] = xc - if ismap(xc): - log["original_conditioning"] = self.to_rgb(xc) - - if plot_diffusion_rows: - # get diffusion row - diffusion_row = list() - z_start = z[:n_row] - for t in range(self.num_timesteps): - if t % self.log_every_t == 0 or t == self.num_timesteps - 1: - t = repeat(torch.tensor([t]), '1 -> b', b=n_row) - t = t.to(self.device).long() - noise = torch.randn_like(z_start) - z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) - diffusion_row.append(self.decode_first_stage(z_noisy)) - - diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W - diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') - diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') - diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) - log["diffusion_row"] = diffusion_grid - - if sample: - # get denoise row - with ema_scope("Sampling"): - samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta) - # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) - x_samples = self.decode_first_stage(samples) - log["samples"] = x_samples - if plot_denoise_rows: - denoise_grid = self._get_denoise_row_from_list(z_denoise_row) - log["denoise_row"] = denoise_grid - - if unconditional_guidance_scale > 1.0: - uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label) - # TODO explore better "unconditional" choices for the other keys - # maybe guide away from empty text label and highest noise level and maximally degraded zx? - uc = dict() - for k in c: - if k == "c_crossattn": - assert isinstance(c[k], list) and len(c[k]) == 1 - uc[k] = [uc_tmp] - elif k == "c_adm": # todo: only run with text-based guidance? - assert isinstance(c[k], torch.Tensor) - #uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level - uc[k] = c[k] - elif isinstance(c[k], list): - uc[k] = [c[k][i] for i in range(len(c[k]))] - else: - uc[k] = c[k] - - with ema_scope("Sampling with classifier-free guidance"): - samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=uc, - ) - x_samples_cfg = self.decode_first_stage(samples_cfg) - log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg - - if plot_progressive_rows: - with ema_scope("Plotting Progressives"): - img, progressives = self.progressive_denoising(c, - shape=(self.channels, self.image_size, self.image_size), - batch_size=N) - prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") - log["progressive_row"] = prog_row - - return log - - -class LatentFinetuneDiffusion(LatentDiffusion): - """ - Basis for different finetunas, such as inpainting or depth2image - To disable finetuning mode, set finetune_keys to None - """ - - def __init__(self, - concat_keys: tuple, - finetune_keys=("model.diffusion_model.input_blocks.0.0.weight", - "model_ema.diffusion_modelinput_blocks00weight" - ), - keep_finetune_dims=4, - # if model was trained without concat mode before and we would like to keep these channels - c_concat_log_start=None, # to log reconstruction of c_concat codes - c_concat_log_end=None, - *args, **kwargs - ): - ckpt_path = kwargs.pop("ckpt_path", None) - ignore_keys = kwargs.pop("ignore_keys", list()) - super().__init__(*args, **kwargs) - self.finetune_keys = finetune_keys - self.concat_keys = concat_keys - self.keep_dims = keep_finetune_dims - self.c_concat_log_start = c_concat_log_start - self.c_concat_log_end = c_concat_log_end - if exists(self.finetune_keys): assert exists(ckpt_path), 'can only finetune from a given checkpoint' - if exists(ckpt_path): - self.init_from_ckpt(ckpt_path, ignore_keys) - - def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): - sd = torch.load(path, map_location="cpu") - if "state_dict" in list(sd.keys()): - sd = sd["state_dict"] - keys = list(sd.keys()) - for k in keys: - for ik in ignore_keys: - if k.startswith(ik): - print("Deleting key {} from state_dict.".format(k)) - del sd[k] - - # make it explicit, finetune by including extra input channels - if exists(self.finetune_keys) and k in self.finetune_keys: - new_entry = None - for name, param in self.named_parameters(): - if name in self.finetune_keys: - print( - f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only") - new_entry = torch.zeros_like(param) # zero init - assert exists(new_entry), 'did not find matching parameter to modify' - new_entry[:, :self.keep_dims, ...] = sd[k] - sd[k] = new_entry - - missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( - sd, strict=False) - print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") - if len(missing) > 0: - print(f"Missing Keys: {missing}") - if len(unexpected) > 0: - print(f"Unexpected Keys: {unexpected}") - - @torch.no_grad() - def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, - quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, - plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None, - use_ema_scope=True, - **kwargs): - ema_scope = self.ema_scope if use_ema_scope else nullcontext - use_ddim = ddim_steps is not None - - log = dict() - z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True) - c_cat, c = c["c_concat"][0], c["c_crossattn"][0] - N = min(x.shape[0], N) - n_row = min(x.shape[0], n_row) - log["inputs"] = x - log["reconstruction"] = xrec - if self.model.conditioning_key is not None: - if hasattr(self.cond_stage_model, "decode"): - xc = self.cond_stage_model.decode(c) - log["conditioning"] = xc - elif self.cond_stage_key in ["caption", "txt"]: - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25) - log["conditioning"] = xc - elif self.cond_stage_key in ['class_label', 'cls']: - xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25) - log['conditioning'] = xc - elif isimage(xc): - log["conditioning"] = xc - if ismap(xc): - log["original_conditioning"] = self.to_rgb(xc) - - if not (self.c_concat_log_start is None and self.c_concat_log_end is None): - log["c_concat_decoded"] = self.decode_first_stage(c_cat[:, self.c_concat_log_start:self.c_concat_log_end]) - - if plot_diffusion_rows: - # get diffusion row - diffusion_row = list() - z_start = z[:n_row] - for t in range(self.num_timesteps): - if t % self.log_every_t == 0 or t == self.num_timesteps - 1: - t = repeat(torch.tensor([t]), '1 -> b', b=n_row) - t = t.to(self.device).long() - noise = torch.randn_like(z_start) - z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) - diffusion_row.append(self.decode_first_stage(z_noisy)) - - diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W - diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') - diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') - diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) - log["diffusion_row"] = diffusion_grid - - if sample: - # get denoise row - with ema_scope("Sampling"): - samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, - batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta) - # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) - x_samples = self.decode_first_stage(samples) - log["samples"] = x_samples - if plot_denoise_rows: - denoise_grid = self._get_denoise_row_from_list(z_denoise_row) - log["denoise_row"] = denoise_grid - - if unconditional_guidance_scale > 1.0: - uc_cross = self.get_unconditional_conditioning(N, unconditional_guidance_label) - uc_cat = c_cat - uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]} - with ema_scope("Sampling with classifier-free guidance"): - samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, - batch_size=N, ddim=use_ddim, - ddim_steps=ddim_steps, eta=ddim_eta, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=uc_full, - ) - x_samples_cfg = self.decode_first_stage(samples_cfg) - log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg - - return log - - -class LatentInpaintDiffusion(LatentFinetuneDiffusion): - """ - can either run as pure inpainting model (only concat mode) or with mixed conditionings, - e.g. mask as concat and text via cross-attn. - To disable finetuning mode, set finetune_keys to None - """ - - def __init__(self, - concat_keys=("mask", "masked_image"), - masked_image_key="masked_image", - *args, **kwargs - ): - super().__init__(concat_keys, *args, **kwargs) - self.masked_image_key = masked_image_key - assert self.masked_image_key in concat_keys - - @torch.no_grad() - def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False): - # note: restricted to non-trainable encoders currently - assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpainting' - z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, - force_c_encode=True, return_original_cond=True, bs=bs) - - assert exists(self.concat_keys) - c_cat = list() - for ck in self.concat_keys: - cc = rearrange(batch[ck], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float() - if bs is not None: - cc = cc[:bs] - cc = cc.to(self.device) - bchw = z.shape - if ck != self.masked_image_key: - cc = torch.nn.functional.interpolate(cc, size=bchw[-2:]) - else: - cc = self.get_first_stage_encoding(self.encode_first_stage(cc)) - c_cat.append(cc) - c_cat = torch.cat(c_cat, dim=1) - all_conds = {"c_concat": [c_cat], "c_crossattn": [c]} - if return_first_stage_outputs: - return z, all_conds, x, xrec, xc - return z, all_conds - - @torch.no_grad() - def log_images(self, *args, **kwargs): - log = super(LatentInpaintDiffusion, self).log_images(*args, **kwargs) - log["masked_image"] = rearrange(args[0]["masked_image"], - 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float() - return log - - -class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion): - """ - condition on monocular depth estimation - """ - - def __init__(self, depth_stage_config, concat_keys=("midas_in",), *args, **kwargs): - super().__init__(concat_keys=concat_keys, *args, **kwargs) - self.depth_model = instantiate_from_config(depth_stage_config) - self.depth_stage_key = concat_keys[0] - - @torch.no_grad() - def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False): - # note: restricted to non-trainable encoders currently - assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for depth2img' - z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, - force_c_encode=True, return_original_cond=True, bs=bs) - - assert exists(self.concat_keys) - assert len(self.concat_keys) == 1 - c_cat = list() - for ck in self.concat_keys: - cc = batch[ck] - if bs is not None: - cc = cc[:bs] - cc = cc.to(self.device) - cc = self.depth_model(cc) - cc = torch.nn.functional.interpolate( - cc, - size=z.shape[2:], - mode="bicubic", - align_corners=False, - ) - # TODO: think about this. ideally rescale by some global values - depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3], - keepdim=True) - cc = 2. * (cc - depth_min) / (depth_max - depth_min + 0.001) - 1. - c_cat.append(cc) - c_cat = torch.cat(c_cat, dim=1) - all_conds = {"c_concat": [c_cat], "c_crossattn": [c]} - if return_first_stage_outputs: - return z, all_conds, x, xrec, xc - return z, all_conds - - @torch.no_grad() - def log_images(self, *args, **kwargs): - log = super().log_images(*args, **kwargs) - depth = self.depth_model(args[0][self.depth_stage_key]) - depth_min, depth_max = torch.amin(depth, dim=[1, 2, 3], keepdim=True), \ - torch.amax(depth, dim=[1, 2, 3], keepdim=True) - log["depth"] = 2. * (depth - depth_min) / (depth_max - depth_min) - 1. - return log - - -class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion): - """ - condition on low-res image (and optionally on some spatial noise augmentation) - """ - def __init__(self, concat_keys=("lr",), reshuffle_patch_size=None, - low_scale_config=None, low_scale_key=None, *args, **kwargs): - super().__init__(concat_keys=concat_keys, *args, **kwargs) - self.reshuffle_patch_size = reshuffle_patch_size - self.low_scale_model = None - if low_scale_config is not None: - print("Initializing a low-scale model") - assert exists(low_scale_key) - self.instantiate_low_stage(low_scale_config) - self.low_scale_key = low_scale_key - - def instantiate_low_stage(self, config): - model = instantiate_from_config(config) - self.low_scale_model = model.eval() - self.low_scale_model.train = disabled_train - for param in self.low_scale_model.parameters(): - param.requires_grad = False - - @torch.no_grad() - def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False): - # note: restricted to non-trainable encoders currently - assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for upscaling-ft' - z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, - force_c_encode=True, return_original_cond=True, bs=bs) - - assert exists(self.concat_keys) - assert len(self.concat_keys) == 1 - # optionally make spatial noise_level here - c_cat = list() - noise_level = None - for ck in self.concat_keys: - cc = batch[ck] - cc = rearrange(cc, 'b h w c -> b c h w') - if exists(self.reshuffle_patch_size): - assert isinstance(self.reshuffle_patch_size, int) - cc = rearrange(cc, 'b c (p1 h) (p2 w) -> b (p1 p2 c) h w', - p1=self.reshuffle_patch_size, p2=self.reshuffle_patch_size) - if bs is not None: - cc = cc[:bs] - cc = cc.to(self.device) - if exists(self.low_scale_model) and ck == self.low_scale_key: - cc, noise_level = self.low_scale_model(cc) - c_cat.append(cc) - c_cat = torch.cat(c_cat, dim=1) - if exists(noise_level): - all_conds = {"c_concat": [c_cat], "c_crossattn": [c], "c_adm": noise_level} - else: - all_conds = {"c_concat": [c_cat], "c_crossattn": [c]} - if return_first_stage_outputs: - return z, all_conds, x, xrec, xc - return z, all_conds - - @torch.no_grad() - def log_images(self, *args, **kwargs): - log = super().log_images(*args, **kwargs) - log["lr"] = rearrange(args[0]["lr"], 'b h w c -> b c h w') - return log diff --git a/ldm/models/diffusion/dpm_solver/__init__.py b/ldm/models/diffusion/dpm_solver/__init__.py deleted file mode 100644 index 7427f38c07530afbab79154ea8aaf88c4bf70a08..0000000000000000000000000000000000000000 --- a/ldm/models/diffusion/dpm_solver/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .sampler import DPMSolverSampler \ No newline at end of file diff --git a/ldm/models/diffusion/dpm_solver/dpm_solver.py b/ldm/models/diffusion/dpm_solver/dpm_solver.py deleted file mode 100644 index 095e5ba3ce0b1aa7f4b3f1e2e5d8fff7cfe6dc8c..0000000000000000000000000000000000000000 --- a/ldm/models/diffusion/dpm_solver/dpm_solver.py +++ /dev/null @@ -1,1154 +0,0 @@ -import torch -import torch.nn.functional as F -import math -from tqdm import tqdm - - -class NoiseScheduleVP: - def __init__( - self, - schedule='discrete', - betas=None, - alphas_cumprod=None, - continuous_beta_0=0.1, - continuous_beta_1=20., - ): - """Create a wrapper class for the forward SDE (VP type). - *** - Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. - We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images. - *** - The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ). - We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper). - Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have: - log_alpha_t = self.marginal_log_mean_coeff(t) - sigma_t = self.marginal_std(t) - lambda_t = self.marginal_lambda(t) - Moreover, as lambda(t) is an invertible function, we also support its inverse function: - t = self.inverse_lambda(lambda_t) - =============================================================== - We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]). - 1. For discrete-time DPMs: - For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by: - t_i = (i + 1) / N - e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1. - We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3. - Args: - betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details) - alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details) - Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`. - **Important**: Please pay special attention for the args for `alphas_cumprod`: - The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that - q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ). - Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have - alpha_{t_n} = \sqrt{\hat{alpha_n}}, - and - log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}). - 2. For continuous-time DPMs: - We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise - schedule are the default settings in DDPM and improved-DDPM: - Args: - beta_min: A `float` number. The smallest beta for the linear schedule. - beta_max: A `float` number. The largest beta for the linear schedule. - cosine_s: A `float` number. The hyperparameter in the cosine schedule. - cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule. - T: A `float` number. The ending time of the forward process. - =============================================================== - Args: - schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs, - 'linear' or 'cosine' for continuous-time DPMs. - Returns: - A wrapper object of the forward SDE (VP type). - - =============================================================== - Example: - # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1): - >>> ns = NoiseScheduleVP('discrete', betas=betas) - # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1): - >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) - # For continuous-time DPMs (VPSDE), linear schedule: - >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.) - """ - - if schedule not in ['discrete', 'linear', 'cosine']: - raise ValueError( - "Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format( - schedule)) - - self.schedule = schedule - if schedule == 'discrete': - if betas is not None: - log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) - else: - assert alphas_cumprod is not None - log_alphas = 0.5 * torch.log(alphas_cumprod) - self.total_N = len(log_alphas) - self.T = 1. - self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1)) - self.log_alpha_array = log_alphas.reshape((1, -1,)) - else: - self.total_N = 1000 - self.beta_0 = continuous_beta_0 - self.beta_1 = continuous_beta_1 - self.cosine_s = 0.008 - self.cosine_beta_max = 999. - self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * ( - 1. + self.cosine_s) / math.pi - self.cosine_s - self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.)) - self.schedule = schedule - if schedule == 'cosine': - # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T. - # Note that T = 0.9946 may be not the optimal setting. However, we find it works well. - self.T = 0.9946 - else: - self.T = 1. - - def marginal_log_mean_coeff(self, t): - """ - Compute log(alpha_t) of a given continuous-time label t in [0, T]. - """ - if self.schedule == 'discrete': - return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), - self.log_alpha_array.to(t.device)).reshape((-1)) - elif self.schedule == 'linear': - return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 - elif self.schedule == 'cosine': - log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.)) - log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0 - return log_alpha_t - - def marginal_alpha(self, t): - """ - Compute alpha_t of a given continuous-time label t in [0, T]. - """ - return torch.exp(self.marginal_log_mean_coeff(t)) - - def marginal_std(self, t): - """ - Compute sigma_t of a given continuous-time label t in [0, T]. - """ - return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) - - def marginal_lambda(self, t): - """ - Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. - """ - log_mean_coeff = self.marginal_log_mean_coeff(t) - log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) - return log_mean_coeff - log_std - - def inverse_lambda(self, lamb): - """ - Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. - """ - if self.schedule == 'linear': - tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) - Delta = self.beta_0 ** 2 + tmp - return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) - elif self.schedule == 'discrete': - log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb) - t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), - torch.flip(self.t_array.to(lamb.device), [1])) - return t.reshape((-1,)) - else: - log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) - t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * ( - 1. + self.cosine_s) / math.pi - self.cosine_s - t = t_fn(log_alpha) - return t - - -def model_wrapper( - model, - noise_schedule, - model_type="noise", - model_kwargs={}, - guidance_type="uncond", - condition=None, - unconditional_condition=None, - guidance_scale=1., - classifier_fn=None, - classifier_kwargs={}, -): - """Create a wrapper function for the noise prediction model. - DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to - firstly wrap the model function to a noise prediction model that accepts the continuous time as the input. - We support four types of the diffusion model by setting `model_type`: - 1. "noise": noise prediction model. (Trained by predicting noise). - 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0). - 3. "v": velocity prediction model. (Trained by predicting the velocity). - The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2]. - [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models." - arXiv preprint arXiv:2202.00512 (2022). - [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models." - arXiv preprint arXiv:2210.02303 (2022). - - 4. "score": marginal score function. (Trained by denoising score matching). - Note that the score function and the noise prediction model follows a simple relationship: - ``` - noise(x_t, t) = -sigma_t * score(x_t, t) - ``` - We support three types of guided sampling by DPMs by setting `guidance_type`: - 1. "uncond": unconditional sampling by DPMs. - The input `model` has the following format: - `` - model(x, t_input, **model_kwargs) -> noise | x_start | v | score - `` - 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier. - The input `model` has the following format: - `` - model(x, t_input, **model_kwargs) -> noise | x_start | v | score - `` - The input `classifier_fn` has the following format: - `` - classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond) - `` - [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis," - in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794. - 3. "classifier-free": classifier-free guidance sampling by conditional DPMs. - The input `model` has the following format: - `` - model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score - `` - And if cond == `unconditional_condition`, the model output is the unconditional DPM output. - [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance." - arXiv preprint arXiv:2207.12598 (2022). - - The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999) - or continuous-time labels (i.e. epsilon to T). - We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise: - `` - def model_fn(x, t_continuous) -> noise: - t_input = get_model_input_time(t_continuous) - return noise_pred(model, x, t_input, **model_kwargs) - `` - where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver. - =============================================================== - Args: - model: A diffusion model with the corresponding format described above. - noise_schedule: A noise schedule object, such as NoiseScheduleVP. - model_type: A `str`. The parameterization type of the diffusion model. - "noise" or "x_start" or "v" or "score". - model_kwargs: A `dict`. A dict for the other inputs of the model function. - guidance_type: A `str`. The type of the guidance for sampling. - "uncond" or "classifier" or "classifier-free". - condition: A pytorch tensor. The condition for the guided sampling. - Only used for "classifier" or "classifier-free" guidance type. - unconditional_condition: A pytorch tensor. The condition for the unconditional sampling. - Only used for "classifier-free" guidance type. - guidance_scale: A `float`. The scale for the guided sampling. - classifier_fn: A classifier function. Only used for the classifier guidance. - classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function. - Returns: - A noise prediction model that accepts the noised data and the continuous time as the inputs. - """ - - def get_model_input_time(t_continuous): - """ - Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. - For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N]. - For continuous-time DPMs, we just use `t_continuous`. - """ - if noise_schedule.schedule == 'discrete': - return (t_continuous - 1. / noise_schedule.total_N) * 1000. - else: - return t_continuous - - def noise_pred_fn(x, t_continuous, cond=None): - if t_continuous.reshape((-1,)).shape[0] == 1: - t_continuous = t_continuous.expand((x.shape[0])) - t_input = get_model_input_time(t_continuous) - if cond is None: - output = model(x, t_input, **model_kwargs) - else: - output = model(x, t_input, cond, **model_kwargs) - if model_type == "noise": - return output - elif model_type == "x_start": - alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) - dims = x.dim() - return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims) - elif model_type == "v": - alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) - dims = x.dim() - return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x - elif model_type == "score": - sigma_t = noise_schedule.marginal_std(t_continuous) - dims = x.dim() - return -expand_dims(sigma_t, dims) * output - - def cond_grad_fn(x, t_input): - """ - Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). - """ - with torch.enable_grad(): - x_in = x.detach().requires_grad_(True) - log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) - return torch.autograd.grad(log_prob.sum(), x_in)[0] - - def model_fn(x, t_continuous): - """ - The noise predicition model function that is used for DPM-Solver. - """ - if t_continuous.reshape((-1,)).shape[0] == 1: - t_continuous = t_continuous.expand((x.shape[0])) - if guidance_type == "uncond": - return noise_pred_fn(x, t_continuous) - elif guidance_type == "classifier": - assert classifier_fn is not None - t_input = get_model_input_time(t_continuous) - cond_grad = cond_grad_fn(x, t_input) - sigma_t = noise_schedule.marginal_std(t_continuous) - noise = noise_pred_fn(x, t_continuous) - return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad - elif guidance_type == "classifier-free": - if guidance_scale == 1. or unconditional_condition is None: - return noise_pred_fn(x, t_continuous, cond=condition) - else: - x_in = torch.cat([x] * 2) - t_in = torch.cat([t_continuous] * 2) - c_in = torch.cat([unconditional_condition, condition]) - noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) - return noise_uncond + guidance_scale * (noise - noise_uncond) - - assert model_type in ["noise", "x_start", "v"] - assert guidance_type in ["uncond", "classifier", "classifier-free"] - return model_fn - - -class DPM_Solver: - def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.): - """Construct a DPM-Solver. - We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0"). - If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver). - If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++). - In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True. - The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales. - Args: - model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]): - `` - def model_fn(x, t_continuous): - return noise - `` - noise_schedule: A noise schedule object, such as NoiseScheduleVP. - predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model. - thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1]. - max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding. - - [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b. - """ - self.model = model_fn - self.noise_schedule = noise_schedule - self.predict_x0 = predict_x0 - self.thresholding = thresholding - self.max_val = max_val - - def noise_prediction_fn(self, x, t): - """ - Return the noise prediction model. - """ - return self.model(x, t) - - def data_prediction_fn(self, x, t): - """ - Return the data prediction model (with thresholding). - """ - noise = self.noise_prediction_fn(x, t) - dims = x.dim() - alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) - x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims) - if self.thresholding: - p = 0.995 # A hyperparameter in the paper of "Imagen" [1]. - s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) - s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims) - x0 = torch.clamp(x0, -s, s) / s - return x0 - - def model_fn(self, x, t): - """ - Convert the model to the noise prediction model or the data prediction model. - """ - if self.predict_x0: - return self.data_prediction_fn(x, t) - else: - return self.noise_prediction_fn(x, t) - - def get_time_steps(self, skip_type, t_T, t_0, N, device): - """Compute the intermediate time steps for sampling. - Args: - skip_type: A `str`. The type for the spacing of the time steps. We support three types: - - 'logSNR': uniform logSNR for the time steps. - - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.) - - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.) - t_T: A `float`. The starting time of the sampling (default is T). - t_0: A `float`. The ending time of the sampling (default is epsilon). - N: A `int`. The total number of the spacing of the time steps. - device: A torch device. - Returns: - A pytorch tensor of the time steps, with the shape (N + 1,). - """ - if skip_type == 'logSNR': - lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) - lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) - logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) - return self.noise_schedule.inverse_lambda(logSNR_steps) - elif skip_type == 'time_uniform': - return torch.linspace(t_T, t_0, N + 1).to(device) - elif skip_type == 'time_quadratic': - t_order = 2 - t = torch.linspace(t_T ** (1. / t_order), t_0 ** (1. / t_order), N + 1).pow(t_order).to(device) - return t - else: - raise ValueError( - "Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) - - def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): - """ - Get the order of each step for sampling by the singlestep DPM-Solver. - We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast". - Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is: - - If order == 1: - We take `steps` of DPM-Solver-1 (i.e. DDIM). - - If order == 2: - - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling. - - If steps % 2 == 0, we use K steps of DPM-Solver-2. - - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1. - - If order == 3: - - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling. - - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1. - - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1. - - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2. - ============================================ - Args: - order: A `int`. The max order for the solver (2 or 3). - steps: A `int`. The total number of function evaluations (NFE). - skip_type: A `str`. The type for the spacing of the time steps. We support three types: - - 'logSNR': uniform logSNR for the time steps. - - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.) - - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.) - t_T: A `float`. The starting time of the sampling (default is T). - t_0: A `float`. The ending time of the sampling (default is epsilon). - device: A torch device. - Returns: - orders: A list of the solver order of each step. - """ - if order == 3: - K = steps // 3 + 1 - if steps % 3 == 0: - orders = [3, ] * (K - 2) + [2, 1] - elif steps % 3 == 1: - orders = [3, ] * (K - 1) + [1] - else: - orders = [3, ] * (K - 1) + [2] - elif order == 2: - if steps % 2 == 0: - K = steps // 2 - orders = [2, ] * K - else: - K = steps // 2 + 1 - orders = [2, ] * (K - 1) + [1] - elif order == 1: - K = 1 - orders = [1, ] * steps - else: - raise ValueError("'order' must be '1' or '2' or '3'.") - if skip_type == 'logSNR': - # To reproduce the results in DPM-Solver paper - timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device) - else: - timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[ - torch.cumsum(torch.tensor([0, ] + orders)).to(device)] - return timesteps_outer, orders - - def denoise_to_zero_fn(self, x, s): - """ - Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. - """ - return self.data_prediction_fn(x, s) - - def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False): - """ - DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`. - Args: - x: A pytorch tensor. The initial value at time `s`. - s: A pytorch tensor. The starting time, with the shape (x.shape[0],). - t: A pytorch tensor. The ending time, with the shape (x.shape[0],). - model_s: A pytorch tensor. The model function evaluated at time `s`. - If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it. - return_intermediate: A `bool`. If true, also return the model value at time `s`. - Returns: - x_t: A pytorch tensor. The approximated solution at time `t`. - """ - ns = self.noise_schedule - dims = x.dim() - lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t) - h = lambda_t - lambda_s - log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t) - sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t) - alpha_t = torch.exp(log_alpha_t) - - if self.predict_x0: - phi_1 = torch.expm1(-h) - if model_s is None: - model_s = self.model_fn(x, s) - x_t = ( - expand_dims(sigma_t / sigma_s, dims) * x - - expand_dims(alpha_t * phi_1, dims) * model_s - ) - if return_intermediate: - return x_t, {'model_s': model_s} - else: - return x_t - else: - phi_1 = torch.expm1(h) - if model_s is None: - model_s = self.model_fn(x, s) - x_t = ( - expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x - - expand_dims(sigma_t * phi_1, dims) * model_s - ) - if return_intermediate: - return x_t, {'model_s': model_s} - else: - return x_t - - def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False, - solver_type='dpm_solver'): - """ - Singlestep solver DPM-Solver-2 from time `s` to time `t`. - Args: - x: A pytorch tensor. The initial value at time `s`. - s: A pytorch tensor. The starting time, with the shape (x.shape[0],). - t: A pytorch tensor. The ending time, with the shape (x.shape[0],). - r1: A `float`. The hyperparameter of the second-order solver. - model_s: A pytorch tensor. The model function evaluated at time `s`. - If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it. - return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time). - solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. - The type slightly impacts the performance. We recommend to use 'dpm_solver' type. - Returns: - x_t: A pytorch tensor. The approximated solution at time `t`. - """ - if solver_type not in ['dpm_solver', 'taylor']: - raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type)) - if r1 is None: - r1 = 0.5 - ns = self.noise_schedule - dims = x.dim() - lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t) - h = lambda_t - lambda_s - lambda_s1 = lambda_s + r1 * h - s1 = ns.inverse_lambda(lambda_s1) - log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff( - s1), ns.marginal_log_mean_coeff(t) - sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t) - alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t) - - if self.predict_x0: - phi_11 = torch.expm1(-r1 * h) - phi_1 = torch.expm1(-h) - - if model_s is None: - model_s = self.model_fn(x, s) - x_s1 = ( - expand_dims(sigma_s1 / sigma_s, dims) * x - - expand_dims(alpha_s1 * phi_11, dims) * model_s - ) - model_s1 = self.model_fn(x_s1, s1) - if solver_type == 'dpm_solver': - x_t = ( - expand_dims(sigma_t / sigma_s, dims) * x - - expand_dims(alpha_t * phi_1, dims) * model_s - - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s) - ) - elif solver_type == 'taylor': - x_t = ( - expand_dims(sigma_t / sigma_s, dims) * x - - expand_dims(alpha_t * phi_1, dims) * model_s - + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * ( - model_s1 - model_s) - ) - else: - phi_11 = torch.expm1(r1 * h) - phi_1 = torch.expm1(h) - - if model_s is None: - model_s = self.model_fn(x, s) - x_s1 = ( - expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x - - expand_dims(sigma_s1 * phi_11, dims) * model_s - ) - model_s1 = self.model_fn(x_s1, s1) - if solver_type == 'dpm_solver': - x_t = ( - expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x - - expand_dims(sigma_t * phi_1, dims) * model_s - - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s) - ) - elif solver_type == 'taylor': - x_t = ( - expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x - - expand_dims(sigma_t * phi_1, dims) * model_s - - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s) - ) - if return_intermediate: - return x_t, {'model_s': model_s, 'model_s1': model_s1} - else: - return x_t - - def singlestep_dpm_solver_third_update(self, x, s, t, r1=1. / 3., r2=2. / 3., model_s=None, model_s1=None, - return_intermediate=False, solver_type='dpm_solver'): - """ - Singlestep solver DPM-Solver-3 from time `s` to time `t`. - Args: - x: A pytorch tensor. The initial value at time `s`. - s: A pytorch tensor. The starting time, with the shape (x.shape[0],). - t: A pytorch tensor. The ending time, with the shape (x.shape[0],). - r1: A `float`. The hyperparameter of the third-order solver. - r2: A `float`. The hyperparameter of the third-order solver. - model_s: A pytorch tensor. The model function evaluated at time `s`. - If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it. - model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`). - If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it. - return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times). - solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. - The type slightly impacts the performance. We recommend to use 'dpm_solver' type. - Returns: - x_t: A pytorch tensor. The approximated solution at time `t`. - """ - if solver_type not in ['dpm_solver', 'taylor']: - raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type)) - if r1 is None: - r1 = 1. / 3. - if r2 is None: - r2 = 2. / 3. - ns = self.noise_schedule - dims = x.dim() - lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t) - h = lambda_t - lambda_s - lambda_s1 = lambda_s + r1 * h - lambda_s2 = lambda_s + r2 * h - s1 = ns.inverse_lambda(lambda_s1) - s2 = ns.inverse_lambda(lambda_s2) - log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff( - s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t) - sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std( - s2), ns.marginal_std(t) - alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t) - - if self.predict_x0: - phi_11 = torch.expm1(-r1 * h) - phi_12 = torch.expm1(-r2 * h) - phi_1 = torch.expm1(-h) - phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1. - phi_2 = phi_1 / h + 1. - phi_3 = phi_2 / h - 0.5 - - if model_s is None: - model_s = self.model_fn(x, s) - if model_s1 is None: - x_s1 = ( - expand_dims(sigma_s1 / sigma_s, dims) * x - - expand_dims(alpha_s1 * phi_11, dims) * model_s - ) - model_s1 = self.model_fn(x_s1, s1) - x_s2 = ( - expand_dims(sigma_s2 / sigma_s, dims) * x - - expand_dims(alpha_s2 * phi_12, dims) * model_s - + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s) - ) - model_s2 = self.model_fn(x_s2, s2) - if solver_type == 'dpm_solver': - x_t = ( - expand_dims(sigma_t / sigma_s, dims) * x - - expand_dims(alpha_t * phi_1, dims) * model_s - + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s) - ) - elif solver_type == 'taylor': - D1_0 = (1. / r1) * (model_s1 - model_s) - D1_1 = (1. / r2) * (model_s2 - model_s) - D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1) - D2 = 2. * (D1_1 - D1_0) / (r2 - r1) - x_t = ( - expand_dims(sigma_t / sigma_s, dims) * x - - expand_dims(alpha_t * phi_1, dims) * model_s - + expand_dims(alpha_t * phi_2, dims) * D1 - - expand_dims(alpha_t * phi_3, dims) * D2 - ) - else: - phi_11 = torch.expm1(r1 * h) - phi_12 = torch.expm1(r2 * h) - phi_1 = torch.expm1(h) - phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1. - phi_2 = phi_1 / h - 1. - phi_3 = phi_2 / h - 0.5 - - if model_s is None: - model_s = self.model_fn(x, s) - if model_s1 is None: - x_s1 = ( - expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x - - expand_dims(sigma_s1 * phi_11, dims) * model_s - ) - model_s1 = self.model_fn(x_s1, s1) - x_s2 = ( - expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x - - expand_dims(sigma_s2 * phi_12, dims) * model_s - - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s) - ) - model_s2 = self.model_fn(x_s2, s2) - if solver_type == 'dpm_solver': - x_t = ( - expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x - - expand_dims(sigma_t * phi_1, dims) * model_s - - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s) - ) - elif solver_type == 'taylor': - D1_0 = (1. / r1) * (model_s1 - model_s) - D1_1 = (1. / r2) * (model_s2 - model_s) - D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1) - D2 = 2. * (D1_1 - D1_0) / (r2 - r1) - x_t = ( - expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x - - expand_dims(sigma_t * phi_1, dims) * model_s - - expand_dims(sigma_t * phi_2, dims) * D1 - - expand_dims(sigma_t * phi_3, dims) * D2 - ) - - if return_intermediate: - return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2} - else: - return x_t - - def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"): - """ - Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`. - Args: - x: A pytorch tensor. The initial value at time `s`. - model_prev_list: A list of pytorch tensor. The previous computed model values. - t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],) - t: A pytorch tensor. The ending time, with the shape (x.shape[0],). - solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. - The type slightly impacts the performance. We recommend to use 'dpm_solver' type. - Returns: - x_t: A pytorch tensor. The approximated solution at time `t`. - """ - if solver_type not in ['dpm_solver', 'taylor']: - raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type)) - ns = self.noise_schedule - dims = x.dim() - model_prev_1, model_prev_0 = model_prev_list - t_prev_1, t_prev_0 = t_prev_list - lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda( - t_prev_0), ns.marginal_lambda(t) - log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) - sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) - alpha_t = torch.exp(log_alpha_t) - - h_0 = lambda_prev_0 - lambda_prev_1 - h = lambda_t - lambda_prev_0 - r0 = h_0 / h - D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1) - if self.predict_x0: - if solver_type == 'dpm_solver': - x_t = ( - expand_dims(sigma_t / sigma_prev_0, dims) * x - - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0 - - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0 - ) - elif solver_type == 'taylor': - x_t = ( - expand_dims(sigma_t / sigma_prev_0, dims) * x - - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0 - + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0 - ) - else: - if solver_type == 'dpm_solver': - x_t = ( - expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x - - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0 - - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0 - ) - elif solver_type == 'taylor': - x_t = ( - expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x - - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0 - - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0 - ) - return x_t - - def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'): - """ - Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`. - Args: - x: A pytorch tensor. The initial value at time `s`. - model_prev_list: A list of pytorch tensor. The previous computed model values. - t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],) - t: A pytorch tensor. The ending time, with the shape (x.shape[0],). - solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. - The type slightly impacts the performance. We recommend to use 'dpm_solver' type. - Returns: - x_t: A pytorch tensor. The approximated solution at time `t`. - """ - ns = self.noise_schedule - dims = x.dim() - model_prev_2, model_prev_1, model_prev_0 = model_prev_list - t_prev_2, t_prev_1, t_prev_0 = t_prev_list - lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda( - t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t) - log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) - sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) - alpha_t = torch.exp(log_alpha_t) - - h_1 = lambda_prev_1 - lambda_prev_2 - h_0 = lambda_prev_0 - lambda_prev_1 - h = lambda_t - lambda_prev_0 - r0, r1 = h_0 / h, h_1 / h - D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1) - D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2) - D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1) - D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1) - if self.predict_x0: - x_t = ( - expand_dims(sigma_t / sigma_prev_0, dims) * x - - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0 - + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1 - - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h ** 2 - 0.5), dims) * D2 - ) - else: - x_t = ( - expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x - - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0 - - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1 - - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h ** 2 - 0.5), dims) * D2 - ) - return x_t - - def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None, - r2=None): - """ - Singlestep DPM-Solver with the order `order` from time `s` to time `t`. - Args: - x: A pytorch tensor. The initial value at time `s`. - s: A pytorch tensor. The starting time, with the shape (x.shape[0],). - t: A pytorch tensor. The ending time, with the shape (x.shape[0],). - order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3. - return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times). - solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. - The type slightly impacts the performance. We recommend to use 'dpm_solver' type. - r1: A `float`. The hyperparameter of the second-order or third-order solver. - r2: A `float`. The hyperparameter of the third-order solver. - Returns: - x_t: A pytorch tensor. The approximated solution at time `t`. - """ - if order == 1: - return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate) - elif order == 2: - return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate, - solver_type=solver_type, r1=r1) - elif order == 3: - return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate, - solver_type=solver_type, r1=r1, r2=r2) - else: - raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order)) - - def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'): - """ - Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`. - Args: - x: A pytorch tensor. The initial value at time `s`. - model_prev_list: A list of pytorch tensor. The previous computed model values. - t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],) - t: A pytorch tensor. The ending time, with the shape (x.shape[0],). - order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3. - solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. - The type slightly impacts the performance. We recommend to use 'dpm_solver' type. - Returns: - x_t: A pytorch tensor. The approximated solution at time `t`. - """ - if order == 1: - return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1]) - elif order == 2: - return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type) - elif order == 3: - return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type) - else: - raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order)) - - def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5, - solver_type='dpm_solver'): - """ - The adaptive step size solver based on singlestep DPM-Solver. - Args: - x: A pytorch tensor. The initial value at time `t_T`. - order: A `int`. The (higher) order of the solver. We only support order == 2 or 3. - t_T: A `float`. The starting time of the sampling (default is T). - t_0: A `float`. The ending time of the sampling (default is epsilon). - h_init: A `float`. The initial step size (for logSNR). - atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1]. - rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05. - theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1]. - t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the - current time and `t_0` is less than `t_err`. The default setting is 1e-5. - solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. - The type slightly impacts the performance. We recommend to use 'dpm_solver' type. - Returns: - x_0: A pytorch tensor. The approximated solution at time `t_0`. - [1] A. Jolicoeur-Martineau, K. Li, R. Pichรฉ-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021. - """ - ns = self.noise_schedule - s = t_T * torch.ones((x.shape[0],)).to(x) - lambda_s = ns.marginal_lambda(s) - lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x)) - h = h_init * torch.ones_like(s).to(x) - x_prev = x - nfe = 0 - if order == 2: - r1 = 0.5 - lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True) - higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, - solver_type=solver_type, - **kwargs) - elif order == 3: - r1, r2 = 1. / 3., 2. / 3. - lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, - return_intermediate=True, - solver_type=solver_type) - higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2, - solver_type=solver_type, - **kwargs) - else: - raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order)) - while torch.abs((s - t_0)).mean() > t_err: - t = ns.inverse_lambda(lambda_s + h) - x_lower, lower_noise_kwargs = lower_update(x, s, t) - x_higher = higher_update(x, s, t, **lower_noise_kwargs) - delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev))) - norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True)) - E = norm_fn((x_higher - x_lower) / delta).max() - if torch.all(E <= 1.): - x = x_higher - s = t - x_prev = x_lower - lambda_s = ns.marginal_lambda(s) - h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s) - nfe += order - print('adaptive solver nfe', nfe) - return x - - def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform', - method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver', - atol=0.0078, rtol=0.05, - ): - """ - Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`. - ===================================================== - We support the following algorithms for both noise prediction model and data prediction model: - - 'singlestep': - Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver. - We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps). - The total number of function evaluations (NFE) == `steps`. - Given a fixed NFE == `steps`, the sampling procedure is: - - If `order` == 1: - - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM). - - If `order` == 2: - - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling. - - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2. - - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1. - - If `order` == 3: - - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling. - - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1. - - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1. - - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2. - - 'multistep': - Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`. - We initialize the first `order` values by lower order multistep solvers. - Given a fixed NFE == `steps`, the sampling procedure is: - Denote K = steps. - - If `order` == 1: - - We use K steps of DPM-Solver-1 (i.e. DDIM). - - If `order` == 2: - - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2. - - If `order` == 3: - - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3. - - 'singlestep_fixed': - Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3). - We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE. - - 'adaptive': - Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper). - We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`. - You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs - (NFE) and the sample quality. - - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2. - - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3. - ===================================================== - Some advices for choosing the algorithm: - - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs: - Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`. - e.g. - >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False) - >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3, - skip_type='time_uniform', method='singlestep') - - For **guided sampling with large guidance scale** by DPMs: - Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`. - e.g. - >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True) - >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2, - skip_type='time_uniform', method='multistep') - We support three types of `skip_type`: - - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images** - - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**. - - 'time_quadratic': quadratic time for the time steps. - ===================================================== - Args: - x: A pytorch tensor. The initial value at time `t_start` - e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution. - steps: A `int`. The total number of function evaluations (NFE). - t_start: A `float`. The starting time of the sampling. - If `T` is None, we use self.noise_schedule.T (default is 1.0). - t_end: A `float`. The ending time of the sampling. - If `t_end` is None, we use 1. / self.noise_schedule.total_N. - e.g. if total_N == 1000, we have `t_end` == 1e-3. - For discrete-time DPMs: - - We recommend `t_end` == 1. / self.noise_schedule.total_N. - For continuous-time DPMs: - - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15. - order: A `int`. The order of DPM-Solver. - skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'. - method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'. - denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step. - Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1). - This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and - score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID - for diffusion models sampling by diffusion SDEs for low-resolutional images - (such as CIFAR-10). However, we observed that such trick does not matter for - high-resolutional images. As it needs an additional NFE, we do not recommend - it for high-resolutional images. - lower_order_final: A `bool`. Whether to use lower order solvers at the final steps. - Only valid for `method=multistep` and `steps < 15`. We empirically find that - this trick is a key to stabilizing the sampling by DPM-Solver with very few steps - (especially for steps <= 10). So we recommend to set it to be `True`. - solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`. - atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'. - rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'. - Returns: - x_end: A pytorch tensor. The approximated solution at time `t_end`. - """ - t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end - t_T = self.noise_schedule.T if t_start is None else t_start - device = x.device - if method == 'adaptive': - with torch.no_grad(): - x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol, - solver_type=solver_type) - elif method == 'multistep': - assert steps >= order - timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) - assert timesteps.shape[0] - 1 == steps - with torch.no_grad(): - vec_t = timesteps[0].expand((x.shape[0])) - model_prev_list = [self.model_fn(x, vec_t)] - t_prev_list = [vec_t] - # Init the first `order` values by lower order multistep DPM-Solver. - for init_order in tqdm(range(1, order), desc="DPM init order"): - vec_t = timesteps[init_order].expand(x.shape[0]) - x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order, - solver_type=solver_type) - model_prev_list.append(self.model_fn(x, vec_t)) - t_prev_list.append(vec_t) - # Compute the remaining values by `order`-th order multistep DPM-Solver. - for step in tqdm(range(order, steps + 1), desc="DPM multistep"): - vec_t = timesteps[step].expand(x.shape[0]) - if lower_order_final and steps < 15: - step_order = min(order, steps + 1 - step) - else: - step_order = order - x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order, - solver_type=solver_type) - for i in range(order - 1): - t_prev_list[i] = t_prev_list[i + 1] - model_prev_list[i] = model_prev_list[i + 1] - t_prev_list[-1] = vec_t - # We do not need to evaluate the final model value. - if step < steps: - model_prev_list[-1] = self.model_fn(x, vec_t) - elif method in ['singlestep', 'singlestep_fixed']: - if method == 'singlestep': - timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order, - skip_type=skip_type, - t_T=t_T, t_0=t_0, - device=device) - elif method == 'singlestep_fixed': - K = steps // order - orders = [order, ] * K - timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device) - for i, order in enumerate(orders): - t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1] - timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(), - N=order, device=device) - lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner) - vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0]) - h = lambda_inner[-1] - lambda_inner[0] - r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h - r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h - x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2) - if denoise_to_zero: - x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0) - return x - - -############################################################# -# other utility functions -############################################################# - -def interpolate_fn(x, xp, yp): - """ - A piecewise linear function y = f(x), using xp and yp as keypoints. - We implement f(x) in a differentiable way (i.e. applicable for autograd). - The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) - Args: - x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver). - xp: PyTorch tensor with shape [C, K], where K is the number of keypoints. - yp: PyTorch tensor with shape [C, K]. - Returns: - The function values f(x), with shape [N, C]. - """ - N, K = x.shape[0], xp.shape[1] - all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) - sorted_all_x, x_indices = torch.sort(all_x, dim=2) - x_idx = torch.argmin(x_indices, dim=2) - cand_start_idx = x_idx - 1 - start_idx = torch.where( - torch.eq(x_idx, 0), - torch.tensor(1, device=x.device), - torch.where( - torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, - ), - ) - end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) - start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) - end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) - start_idx2 = torch.where( - torch.eq(x_idx, 0), - torch.tensor(0, device=x.device), - torch.where( - torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, - ), - ) - y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) - start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) - end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) - cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) - return cand - - -def expand_dims(v, dims): - """ - Expand the tensor `v` to the dim `dims`. - Args: - `v`: a PyTorch tensor with shape [N]. - `dim`: a `int`. - Returns: - a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. - """ - return v[(...,) + (None,) * (dims - 1)] \ No newline at end of file diff --git a/ldm/models/diffusion/dpm_solver/sampler.py b/ldm/models/diffusion/dpm_solver/sampler.py deleted file mode 100644 index 7d137b8cf36718c1c58faa09f9dd919e5fb2977b..0000000000000000000000000000000000000000 --- a/ldm/models/diffusion/dpm_solver/sampler.py +++ /dev/null @@ -1,87 +0,0 @@ -"""SAMPLING ONLY.""" -import torch - -from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver - - -MODEL_TYPES = { - "eps": "noise", - "v": "v" -} - - -class DPMSolverSampler(object): - def __init__(self, model, **kwargs): - super().__init__() - self.model = model - to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device) - self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod)) - - def register_buffer(self, name, attr): - if type(attr) == torch.Tensor: - if attr.device != torch.device("cuda"): - attr = attr.to(torch.device("cuda")) - setattr(self, name, attr) - - @torch.no_grad() - def sample(self, - S, - batch_size, - shape, - conditioning=None, - callback=None, - normals_sequence=None, - img_callback=None, - quantize_x0=False, - eta=0., - mask=None, - x0=None, - temperature=1., - noise_dropout=0., - score_corrector=None, - corrector_kwargs=None, - verbose=True, - x_T=None, - log_every_t=100, - unconditional_guidance_scale=1., - unconditional_conditioning=None, - # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... - **kwargs - ): - if conditioning is not None: - if isinstance(conditioning, dict): - cbs = conditioning[list(conditioning.keys())[0]].shape[0] - if cbs != batch_size: - print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") - else: - if conditioning.shape[0] != batch_size: - print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") - - # sampling - C, H, W = shape - size = (batch_size, C, H, W) - - print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}') - - device = self.model.betas.device - if x_T is None: - img = torch.randn(size, device=device) - else: - img = x_T - - ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod) - - model_fn = model_wrapper( - lambda x, t, c: self.model.apply_model(x, t, c), - ns, - model_type=MODEL_TYPES[self.model.parameterization], - guidance_type="classifier-free", - condition=conditioning, - unconditional_condition=unconditional_conditioning, - guidance_scale=unconditional_guidance_scale, - ) - - dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False) - x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True) - - return x.to(device), None \ No newline at end of file diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py deleted file mode 100644 index 7002a365d27168ced0a04e9a4d83e088f8284eae..0000000000000000000000000000000000000000 --- a/ldm/models/diffusion/plms.py +++ /dev/null @@ -1,244 +0,0 @@ -"""SAMPLING ONLY.""" - -import torch -import numpy as np -from tqdm import tqdm -from functools import partial - -from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like -from ldm.models.diffusion.sampling_util import norm_thresholding - - -class PLMSSampler(object): - def __init__(self, model, schedule="linear", **kwargs): - super().__init__() - self.model = model - self.ddpm_num_timesteps = model.num_timesteps - self.schedule = schedule - - def register_buffer(self, name, attr): - if type(attr) == torch.Tensor: - if attr.device != torch.device("cuda"): - attr = attr.to(torch.device("cuda")) - setattr(self, name, attr) - - def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): - if ddim_eta != 0: - raise ValueError('ddim_eta must be 0 for PLMS') - self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, - num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) - alphas_cumprod = self.model.alphas_cumprod - assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' - to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) - - self.register_buffer('betas', to_torch(self.model.betas)) - self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) - self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) - - # calculations for diffusion q(x_t | x_{t-1}) and others - self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) - self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) - self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) - self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) - self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) - - # ddim sampling parameters - ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), - ddim_timesteps=self.ddim_timesteps, - eta=ddim_eta,verbose=verbose) - self.register_buffer('ddim_sigmas', ddim_sigmas) - self.register_buffer('ddim_alphas', ddim_alphas) - self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) - self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) - sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( - (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( - 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) - self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) - - @torch.no_grad() - def sample(self, - S, - batch_size, - shape, - conditioning=None, - callback=None, - normals_sequence=None, - img_callback=None, - quantize_x0=False, - eta=0., - mask=None, - x0=None, - temperature=1., - noise_dropout=0., - score_corrector=None, - corrector_kwargs=None, - verbose=True, - x_T=None, - log_every_t=100, - unconditional_guidance_scale=1., - unconditional_conditioning=None, - # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... - dynamic_threshold=None, - **kwargs - ): - if conditioning is not None: - if isinstance(conditioning, dict): - cbs = conditioning[list(conditioning.keys())[0]].shape[0] - if cbs != batch_size: - print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") - else: - if conditioning.shape[0] != batch_size: - print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") - - self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) - # sampling - C, H, W = shape - size = (batch_size, C, H, W) - print(f'Data shape for PLMS sampling is {size}') - - samples, intermediates = self.plms_sampling(conditioning, size, - callback=callback, - img_callback=img_callback, - quantize_denoised=quantize_x0, - mask=mask, x0=x0, - ddim_use_original_steps=False, - noise_dropout=noise_dropout, - temperature=temperature, - score_corrector=score_corrector, - corrector_kwargs=corrector_kwargs, - x_T=x_T, - log_every_t=log_every_t, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning, - dynamic_threshold=dynamic_threshold, - ) - return samples, intermediates - - @torch.no_grad() - def plms_sampling(self, cond, shape, - x_T=None, ddim_use_original_steps=False, - callback=None, timesteps=None, quantize_denoised=False, - mask=None, x0=None, img_callback=None, log_every_t=100, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None, - dynamic_threshold=None): - device = self.model.betas.device - b = shape[0] - if x_T is None: - img = torch.randn(shape, device=device) - else: - img = x_T - - if timesteps is None: - timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps - elif timesteps is not None and not ddim_use_original_steps: - subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 - timesteps = self.ddim_timesteps[:subset_end] - - intermediates = {'x_inter': [img], 'pred_x0': [img]} - time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps) - total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] - print(f"Running PLMS Sampling with {total_steps} timesteps") - - iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps) - old_eps = [] - - for i, step in enumerate(iterator): - index = total_steps - i - 1 - ts = torch.full((b,), step, device=device, dtype=torch.long) - ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long) - - if mask is not None: - assert x0 is not None - img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? - img = img_orig * mask + (1. - mask) * img - - outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, - quantize_denoised=quantize_denoised, temperature=temperature, - noise_dropout=noise_dropout, score_corrector=score_corrector, - corrector_kwargs=corrector_kwargs, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning, - old_eps=old_eps, t_next=ts_next, - dynamic_threshold=dynamic_threshold) - img, pred_x0, e_t = outs - old_eps.append(e_t) - if len(old_eps) >= 4: - old_eps.pop(0) - if callback: callback(i) - if img_callback: img_callback(pred_x0, i) - - if index % log_every_t == 0 or index == total_steps - 1: - intermediates['x_inter'].append(img) - intermediates['pred_x0'].append(pred_x0) - - return img, intermediates - - @torch.no_grad() - def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, - dynamic_threshold=None): - b, *_, device = *x.shape, x.device - - def get_model_output(x, t): - if unconditional_conditioning is None or unconditional_guidance_scale == 1.: - e_t = self.model.apply_model(x, t, c) - else: - x_in = torch.cat([x] * 2) - t_in = torch.cat([t] * 2) - c_in = torch.cat([unconditional_conditioning, c]) - e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) - e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) - - if score_corrector is not None: - assert self.model.parameterization == "eps" - e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) - - return e_t - - alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas - alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev - sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas - sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas - - def get_x_prev_and_pred_x0(e_t, index): - # select parameters corresponding to the currently considered timestep - a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) - a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) - sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) - sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) - - # current prediction for x_0 - pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() - if quantize_denoised: - pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) - if dynamic_threshold is not None: - pred_x0 = norm_thresholding(pred_x0, dynamic_threshold) - # direction pointing to x_t - dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t - noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature - if noise_dropout > 0.: - noise = torch.nn.functional.dropout(noise, p=noise_dropout) - x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise - return x_prev, pred_x0 - - e_t = get_model_output(x, t) - if len(old_eps) == 0: - # Pseudo Improved Euler (2nd order) - x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) - e_t_next = get_model_output(x_prev, t_next) - e_t_prime = (e_t + e_t_next) / 2 - elif len(old_eps) == 1: - # 2nd order Pseudo Linear Multistep (Adams-Bashforth) - e_t_prime = (3 * e_t - old_eps[-1]) / 2 - elif len(old_eps) == 2: - # 3nd order Pseudo Linear Multistep (Adams-Bashforth) - e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 - elif len(old_eps) >= 3: - # 4nd order Pseudo Linear Multistep (Adams-Bashforth) - e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 - - x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) - - return x_prev, pred_x0, e_t diff --git a/ldm/models/diffusion/sampling_util.py b/ldm/models/diffusion/sampling_util.py deleted file mode 100644 index 7eff02be6d7c54d43ee6680636ac0698dd3b3f33..0000000000000000000000000000000000000000 --- a/ldm/models/diffusion/sampling_util.py +++ /dev/null @@ -1,22 +0,0 @@ -import torch -import numpy as np - - -def append_dims(x, target_dims): - """Appends dimensions to the end of a tensor until it has target_dims dimensions. - From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py""" - dims_to_append = target_dims - x.ndim - if dims_to_append < 0: - raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') - return x[(...,) + (None,) * dims_to_append] - - -def norm_thresholding(x0, value): - s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim) - return x0 * (value / s) - - -def spatial_norm_thresholding(x0, value): - # b c h w - s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value) - return x0 * (value / s) \ No newline at end of file diff --git a/ldm/modules/attention.py b/ldm/modules/attention.py deleted file mode 100644 index d504d939f6a02cf45f028799d7d73b84500cee06..0000000000000000000000000000000000000000 --- a/ldm/modules/attention.py +++ /dev/null @@ -1,331 +0,0 @@ -from inspect import isfunction -import math -import torch -import torch.nn.functional as F -from torch import nn, einsum -from einops import rearrange, repeat -from typing import Optional, Any - -from ldm.modules.diffusionmodules.util import checkpoint - - -try: - import xformers - import xformers.ops - XFORMERS_IS_AVAILBLE = True -except: - XFORMERS_IS_AVAILBLE = False - - -def exists(val): - return val is not None - - -def uniq(arr): - return{el: True for el in arr}.keys() - - -def default(val, d): - if exists(val): - return val - return d() if isfunction(d) else d - - -def max_neg_value(t): - return -torch.finfo(t.dtype).max - - -def init_(tensor): - dim = tensor.shape[-1] - std = 1 / math.sqrt(dim) - tensor.uniform_(-std, std) - return tensor - - -# feedforward -class GEGLU(nn.Module): - def __init__(self, dim_in, dim_out): - super().__init__() - self.proj = nn.Linear(dim_in, dim_out * 2) - - def forward(self, x): - x, gate = self.proj(x).chunk(2, dim=-1) - return x * F.gelu(gate) - - -class FeedForward(nn.Module): - def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): - super().__init__() - inner_dim = int(dim * mult) - dim_out = default(dim_out, dim) - project_in = nn.Sequential( - nn.Linear(dim, inner_dim), - nn.GELU() - ) if not glu else GEGLU(dim, inner_dim) - - self.net = nn.Sequential( - project_in, - nn.Dropout(dropout), - nn.Linear(inner_dim, dim_out) - ) - - def forward(self, x): - return self.net(x) - - -def zero_module(module): - """ - Zero out the parameters of a module and return it. - """ - for p in module.parameters(): - p.detach().zero_() - return module - - -def Normalize(in_channels): - return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) - - -class SpatialSelfAttention(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.in_channels = in_channels - - self.norm = Normalize(in_channels) - self.q = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - self.k = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - self.v = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - self.proj_out = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - - def forward(self, x): - h_ = x - h_ = self.norm(h_) - q = self.q(h_) - k = self.k(h_) - v = self.v(h_) - - # compute attention - b,c,h,w = q.shape - q = rearrange(q, 'b c h w -> b (h w) c') - k = rearrange(k, 'b c h w -> b c (h w)') - w_ = torch.einsum('bij,bjk->bik', q, k) - - w_ = w_ * (int(c)**(-0.5)) - w_ = torch.nn.functional.softmax(w_, dim=2) - - # attend to values - v = rearrange(v, 'b c h w -> b c (h w)') - w_ = rearrange(w_, 'b i j -> b j i') - h_ = torch.einsum('bij,bjk->bik', v, w_) - h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) - h_ = self.proj_out(h_) - - return x+h_ - - -class CrossAttention(nn.Module): - def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.): - super().__init__() - inner_dim = dim_head * heads - context_dim = default(context_dim, query_dim) - - self.scale = dim_head ** -0.5 - self.heads = heads - - self.to_q = nn.Linear(query_dim, inner_dim, bias=False) - self.to_k = nn.Linear(context_dim, inner_dim, bias=False) - self.to_v = nn.Linear(context_dim, inner_dim, bias=False) - - self.to_out = nn.Sequential( - nn.Linear(inner_dim, query_dim), - nn.Dropout(dropout) - ) - - def forward(self, x, context=None, mask=None): - h = self.heads - - q = self.to_q(x) - context = default(context, x) - k = self.to_k(context) - v = self.to_v(context) - - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) - - sim = einsum('b i d, b j d -> b i j', q, k) * self.scale - del q, k - - if exists(mask): - mask = rearrange(mask, 'b ... -> b (...)') - max_neg_value = -torch.finfo(sim.dtype).max - mask = repeat(mask, 'b j -> (b h) () j', h=h) - sim.masked_fill_(~mask, max_neg_value) - - # attention, what we cannot get enough of - sim = sim.softmax(dim=-1) - - out = einsum('b i j, b j d -> b i d', sim, v) - out = rearrange(out, '(b h) n d -> b n (h d)', h=h) - return self.to_out(out) - - -class MemoryEfficientCrossAttention(nn.Module): - # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 - def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0): - super().__init__() - print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using " - f"{heads} heads.") - inner_dim = dim_head * heads - context_dim = default(context_dim, query_dim) - - self.heads = heads - self.dim_head = dim_head - - self.to_q = nn.Linear(query_dim, inner_dim, bias=False) - self.to_k = nn.Linear(context_dim, inner_dim, bias=False) - self.to_v = nn.Linear(context_dim, inner_dim, bias=False) - - self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)) - self.attention_op: Optional[Any] = None - - def forward(self, x, context=None, mask=None): - q = self.to_q(x) - context = default(context, x) - k = self.to_k(context) - v = self.to_v(context) - - b, _, _ = q.shape - q, k, v = map( - lambda t: t.unsqueeze(3) - .reshape(b, t.shape[1], self.heads, self.dim_head) - .permute(0, 2, 1, 3) - .reshape(b * self.heads, t.shape[1], self.dim_head) - .contiguous(), - (q, k, v), - ) - - # actually compute the attention, what we cannot get enough of - out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op) - - if exists(mask): - raise NotImplementedError - out = ( - out.unsqueeze(0) - .reshape(b, self.heads, out.shape[1], self.dim_head) - .permute(0, 2, 1, 3) - .reshape(b, out.shape[1], self.heads * self.dim_head) - ) - return self.to_out(out) - - -class BasicTransformerBlock(nn.Module): - ATTENTION_MODES = { - "softmax": CrossAttention, # vanilla attention - "softmax-xformers": MemoryEfficientCrossAttention - } - def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, - disable_self_attn=False): - super().__init__() - attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax" - assert attn_mode in self.ATTENTION_MODES - attn_cls = self.ATTENTION_MODES[attn_mode] - self.disable_self_attn = disable_self_attn - self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, - context_dim=context_dim if self.disable_self_attn else None) # is a self-attention if not self.disable_self_attn - self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) - self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim, - heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none - self.norm1 = nn.LayerNorm(dim) - self.norm2 = nn.LayerNorm(dim) - self.norm3 = nn.LayerNorm(dim) - self.checkpoint = checkpoint - - def forward(self, x, context=None): - return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) - - def _forward(self, x, context=None): - x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x - x = self.attn2(self.norm2(x), context=context) + x - x = self.ff(self.norm3(x)) + x - return x - - -class SpatialTransformer(nn.Module): - """ - Transformer block for image-like data. - First, project the input (aka embedding) - and reshape to b, t, d. - Then apply standard transformer action. - Finally, reshape to image - NEW: use_linear for more efficiency instead of the 1x1 convs - """ - def __init__(self, in_channels, n_heads, d_head, - depth=1, dropout=0., context_dim=None, - disable_self_attn=False, use_linear=False, - use_checkpoint=True): - super().__init__() - if exists(context_dim) and not isinstance(context_dim, list): - context_dim = [context_dim] - self.in_channels = in_channels - inner_dim = n_heads * d_head - self.norm = Normalize(in_channels) - if not use_linear: - self.proj_in = nn.Conv2d(in_channels, - inner_dim, - kernel_size=1, - stride=1, - padding=0) - else: - self.proj_in = nn.Linear(in_channels, inner_dim) - - self.transformer_blocks = nn.ModuleList( - [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], - disable_self_attn=disable_self_attn, checkpoint=use_checkpoint) - for d in range(depth)] - ) - if not use_linear: - self.proj_out = zero_module(nn.Conv2d(inner_dim, - in_channels, - kernel_size=1, - stride=1, - padding=0)) - else: - self.proj_out = zero_module(nn.Linear(in_channels, inner_dim)) - self.use_linear = use_linear - - def forward(self, x, context=None): - # note: if no context is given, cross-attention defaults to self-attention - if not isinstance(context, list): - context = [context] - b, c, h, w = x.shape - x_in = x - x = self.norm(x) - if not self.use_linear: - x = self.proj_in(x) - x = rearrange(x, 'b c h w -> b (h w) c').contiguous() - if self.use_linear: - x = self.proj_in(x) - for i, block in enumerate(self.transformer_blocks): - x = block(x, context=context[i]) - if self.use_linear: - x = self.proj_out(x) - x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous() - if not self.use_linear: - x = self.proj_out(x) - return x + x_in - diff --git a/ldm/modules/diffusionmodules/__init__.py b/ldm/modules/diffusionmodules/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/ldm/modules/diffusionmodules/model.py b/ldm/modules/diffusionmodules/model.py deleted file mode 100644 index b089eebbe1676d8249005bb9def002ff5180715b..0000000000000000000000000000000000000000 --- a/ldm/modules/diffusionmodules/model.py +++ /dev/null @@ -1,852 +0,0 @@ -# pytorch_diffusion + derived encoder decoder -import math -import torch -import torch.nn as nn -import numpy as np -from einops import rearrange -from typing import Optional, Any - -from ldm.modules.attention import MemoryEfficientCrossAttention - -try: - import xformers - import xformers.ops - XFORMERS_IS_AVAILBLE = True -except: - XFORMERS_IS_AVAILBLE = False - print("No module 'xformers'. Proceeding without it.") - - -def get_timestep_embedding(timesteps, embedding_dim): - """ - This matches the implementation in Denoising Diffusion Probabilistic Models: - From Fairseq. - Build sinusoidal embeddings. - This matches the implementation in tensor2tensor, but differs slightly - from the description in Section 3.5 of "Attention Is All You Need". - """ - assert len(timesteps.shape) == 1 - - half_dim = embedding_dim // 2 - emb = math.log(10000) / (half_dim - 1) - emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) - emb = emb.to(device=timesteps.device) - emb = timesteps.float()[:, None] * emb[None, :] - emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) - if embedding_dim % 2 == 1: # zero pad - emb = torch.nn.functional.pad(emb, (0,1,0,0)) - return emb - - -def nonlinearity(x): - # swish - return x*torch.sigmoid(x) - - -def Normalize(in_channels, num_groups=32): - return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) - - -class Upsample(nn.Module): - def __init__(self, in_channels, with_conv): - super().__init__() - self.with_conv = with_conv - if self.with_conv: - self.conv = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=3, - stride=1, - padding=1) - - def forward(self, x): - x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") - if self.with_conv: - x = self.conv(x) - return x - - -class Downsample(nn.Module): - def __init__(self, in_channels, with_conv): - super().__init__() - self.with_conv = with_conv - if self.with_conv: - # no asymmetric padding in torch conv, must do it ourselves - self.conv = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=3, - stride=2, - padding=0) - - def forward(self, x): - if self.with_conv: - pad = (0,1,0,1) - x = torch.nn.functional.pad(x, pad, mode="constant", value=0) - x = self.conv(x) - else: - x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) - return x - - -class ResnetBlock(nn.Module): - def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, - dropout, temb_channels=512): - super().__init__() - self.in_channels = in_channels - out_channels = in_channels if out_channels is None else out_channels - self.out_channels = out_channels - self.use_conv_shortcut = conv_shortcut - - self.norm1 = Normalize(in_channels) - self.conv1 = torch.nn.Conv2d(in_channels, - out_channels, - kernel_size=3, - stride=1, - padding=1) - if temb_channels > 0: - self.temb_proj = torch.nn.Linear(temb_channels, - out_channels) - self.norm2 = Normalize(out_channels) - self.dropout = torch.nn.Dropout(dropout) - self.conv2 = torch.nn.Conv2d(out_channels, - out_channels, - kernel_size=3, - stride=1, - padding=1) - if self.in_channels != self.out_channels: - if self.use_conv_shortcut: - self.conv_shortcut = torch.nn.Conv2d(in_channels, - out_channels, - kernel_size=3, - stride=1, - padding=1) - else: - self.nin_shortcut = torch.nn.Conv2d(in_channels, - out_channels, - kernel_size=1, - stride=1, - padding=0) - - def forward(self, x, temb): - h = x - h = self.norm1(h) - h = nonlinearity(h) - h = self.conv1(h) - - if temb is not None: - h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None] - - h = self.norm2(h) - h = nonlinearity(h) - h = self.dropout(h) - h = self.conv2(h) - - if self.in_channels != self.out_channels: - if self.use_conv_shortcut: - x = self.conv_shortcut(x) - else: - x = self.nin_shortcut(x) - - return x+h - - -class AttnBlock(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.in_channels = in_channels - - self.norm = Normalize(in_channels) - self.q = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - self.k = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - self.v = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - self.proj_out = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - - def forward(self, x): - h_ = x - h_ = self.norm(h_) - q = self.q(h_) - k = self.k(h_) - v = self.v(h_) - - # compute attention - b,c,h,w = q.shape - q = q.reshape(b,c,h*w) - q = q.permute(0,2,1) # b,hw,c - k = k.reshape(b,c,h*w) # b,c,hw - w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] - w_ = w_ * (int(c)**(-0.5)) - w_ = torch.nn.functional.softmax(w_, dim=2) - - # attend to values - v = v.reshape(b,c,h*w) - w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q) - h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] - h_ = h_.reshape(b,c,h,w) - - h_ = self.proj_out(h_) - - return x+h_ - -class MemoryEfficientAttnBlock(nn.Module): - """ - Uses xformers efficient implementation, - see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 - Note: this is a single-head self-attention operation - """ - # - def __init__(self, in_channels): - super().__init__() - self.in_channels = in_channels - - self.norm = Normalize(in_channels) - self.q = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - self.k = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - self.v = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - self.proj_out = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - self.attention_op: Optional[Any] = None - - def forward(self, x): - h_ = x - h_ = self.norm(h_) - q = self.q(h_) - k = self.k(h_) - v = self.v(h_) - - # compute attention - B, C, H, W = q.shape - q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v)) - - q, k, v = map( - lambda t: t.unsqueeze(3) - .reshape(B, t.shape[1], 1, C) - .permute(0, 2, 1, 3) - .reshape(B * 1, t.shape[1], C) - .contiguous(), - (q, k, v), - ) - out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op) - - out = ( - out.unsqueeze(0) - .reshape(B, 1, out.shape[1], C) - .permute(0, 2, 1, 3) - .reshape(B, out.shape[1], C) - ) - out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C) - out = self.proj_out(out) - return x+out - - -class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention): - def forward(self, x, context=None, mask=None): - b, c, h, w = x.shape - x = rearrange(x, 'b c h w -> b (h w) c') - out = super().forward(x, context=context, mask=mask) - out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c) - return x + out - - -def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None): - assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown' - if XFORMERS_IS_AVAILBLE and attn_type == "vanilla": - attn_type = "vanilla-xformers" - print(f"making attention of type '{attn_type}' with {in_channels} in_channels") - if attn_type == "vanilla": - assert attn_kwargs is None - return AttnBlock(in_channels) - elif attn_type == "vanilla-xformers": - print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...") - return MemoryEfficientAttnBlock(in_channels) - elif type == "memory-efficient-cross-attn": - attn_kwargs["query_dim"] = in_channels - return MemoryEfficientCrossAttentionWrapper(**attn_kwargs) - elif attn_type == "none": - return nn.Identity(in_channels) - else: - raise NotImplementedError() - - -class Model(nn.Module): - def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, - attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, - resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): - super().__init__() - if use_linear_attn: attn_type = "linear" - self.ch = ch - self.temb_ch = self.ch*4 - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.in_channels = in_channels - - self.use_timestep = use_timestep - if self.use_timestep: - # timestep embedding - self.temb = nn.Module() - self.temb.dense = nn.ModuleList([ - torch.nn.Linear(self.ch, - self.temb_ch), - torch.nn.Linear(self.temb_ch, - self.temb_ch), - ]) - - # downsampling - self.conv_in = torch.nn.Conv2d(in_channels, - self.ch, - kernel_size=3, - stride=1, - padding=1) - - curr_res = resolution - in_ch_mult = (1,)+tuple(ch_mult) - self.down = nn.ModuleList() - for i_level in range(self.num_resolutions): - block = nn.ModuleList() - attn = nn.ModuleList() - block_in = ch*in_ch_mult[i_level] - block_out = ch*ch_mult[i_level] - for i_block in range(self.num_res_blocks): - block.append(ResnetBlock(in_channels=block_in, - out_channels=block_out, - temb_channels=self.temb_ch, - dropout=dropout)) - block_in = block_out - if curr_res in attn_resolutions: - attn.append(make_attn(block_in, attn_type=attn_type)) - down = nn.Module() - down.block = block - down.attn = attn - if i_level != self.num_resolutions-1: - down.downsample = Downsample(block_in, resamp_with_conv) - curr_res = curr_res // 2 - self.down.append(down) - - # middle - self.mid = nn.Module() - self.mid.block_1 = ResnetBlock(in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout) - self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) - self.mid.block_2 = ResnetBlock(in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout) - - # upsampling - self.up = nn.ModuleList() - for i_level in reversed(range(self.num_resolutions)): - block = nn.ModuleList() - attn = nn.ModuleList() - block_out = ch*ch_mult[i_level] - skip_in = ch*ch_mult[i_level] - for i_block in range(self.num_res_blocks+1): - if i_block == self.num_res_blocks: - skip_in = ch*in_ch_mult[i_level] - block.append(ResnetBlock(in_channels=block_in+skip_in, - out_channels=block_out, - temb_channels=self.temb_ch, - dropout=dropout)) - block_in = block_out - if curr_res in attn_resolutions: - attn.append(make_attn(block_in, attn_type=attn_type)) - up = nn.Module() - up.block = block - up.attn = attn - if i_level != 0: - up.upsample = Upsample(block_in, resamp_with_conv) - curr_res = curr_res * 2 - self.up.insert(0, up) # prepend to get consistent order - - # end - self.norm_out = Normalize(block_in) - self.conv_out = torch.nn.Conv2d(block_in, - out_ch, - kernel_size=3, - stride=1, - padding=1) - - def forward(self, x, t=None, context=None): - #assert x.shape[2] == x.shape[3] == self.resolution - if context is not None: - # assume aligned context, cat along channel axis - x = torch.cat((x, context), dim=1) - if self.use_timestep: - # timestep embedding - assert t is not None - temb = get_timestep_embedding(t, self.ch) - temb = self.temb.dense[0](temb) - temb = nonlinearity(temb) - temb = self.temb.dense[1](temb) - else: - temb = None - - # downsampling - hs = [self.conv_in(x)] - for i_level in range(self.num_resolutions): - for i_block in range(self.num_res_blocks): - h = self.down[i_level].block[i_block](hs[-1], temb) - if len(self.down[i_level].attn) > 0: - h = self.down[i_level].attn[i_block](h) - hs.append(h) - if i_level != self.num_resolutions-1: - hs.append(self.down[i_level].downsample(hs[-1])) - - # middle - h = hs[-1] - h = self.mid.block_1(h, temb) - h = self.mid.attn_1(h) - h = self.mid.block_2(h, temb) - - # upsampling - for i_level in reversed(range(self.num_resolutions)): - for i_block in range(self.num_res_blocks+1): - h = self.up[i_level].block[i_block]( - torch.cat([h, hs.pop()], dim=1), temb) - if len(self.up[i_level].attn) > 0: - h = self.up[i_level].attn[i_block](h) - if i_level != 0: - h = self.up[i_level].upsample(h) - - # end - h = self.norm_out(h) - h = nonlinearity(h) - h = self.conv_out(h) - return h - - def get_last_layer(self): - return self.conv_out.weight - - -class Encoder(nn.Module): - def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, - attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, - resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", - **ignore_kwargs): - super().__init__() - if use_linear_attn: attn_type = "linear" - self.ch = ch - self.temb_ch = 0 - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.in_channels = in_channels - - # downsampling - self.conv_in = torch.nn.Conv2d(in_channels, - self.ch, - kernel_size=3, - stride=1, - padding=1) - - curr_res = resolution - in_ch_mult = (1,)+tuple(ch_mult) - self.in_ch_mult = in_ch_mult - self.down = nn.ModuleList() - for i_level in range(self.num_resolutions): - block = nn.ModuleList() - attn = nn.ModuleList() - block_in = ch*in_ch_mult[i_level] - block_out = ch*ch_mult[i_level] - for i_block in range(self.num_res_blocks): - block.append(ResnetBlock(in_channels=block_in, - out_channels=block_out, - temb_channels=self.temb_ch, - dropout=dropout)) - block_in = block_out - if curr_res in attn_resolutions: - attn.append(make_attn(block_in, attn_type=attn_type)) - down = nn.Module() - down.block = block - down.attn = attn - if i_level != self.num_resolutions-1: - down.downsample = Downsample(block_in, resamp_with_conv) - curr_res = curr_res // 2 - self.down.append(down) - - # middle - self.mid = nn.Module() - self.mid.block_1 = ResnetBlock(in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout) - self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) - self.mid.block_2 = ResnetBlock(in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout) - - # end - self.norm_out = Normalize(block_in) - self.conv_out = torch.nn.Conv2d(block_in, - 2*z_channels if double_z else z_channels, - kernel_size=3, - stride=1, - padding=1) - - def forward(self, x): - # timestep embedding - temb = None - - # downsampling - hs = [self.conv_in(x)] - for i_level in range(self.num_resolutions): - for i_block in range(self.num_res_blocks): - h = self.down[i_level].block[i_block](hs[-1], temb) - if len(self.down[i_level].attn) > 0: - h = self.down[i_level].attn[i_block](h) - hs.append(h) - if i_level != self.num_resolutions-1: - hs.append(self.down[i_level].downsample(hs[-1])) - - # middle - h = hs[-1] - h = self.mid.block_1(h, temb) - h = self.mid.attn_1(h) - h = self.mid.block_2(h, temb) - - # end - h = self.norm_out(h) - h = nonlinearity(h) - h = self.conv_out(h) - return h - - -class Decoder(nn.Module): - def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, - attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, - resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, - attn_type="vanilla", **ignorekwargs): - super().__init__() - if use_linear_attn: attn_type = "linear" - self.ch = ch - self.temb_ch = 0 - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.in_channels = in_channels - self.give_pre_end = give_pre_end - self.tanh_out = tanh_out - - # compute in_ch_mult, block_in and curr_res at lowest res - in_ch_mult = (1,)+tuple(ch_mult) - block_in = ch*ch_mult[self.num_resolutions-1] - curr_res = resolution // 2**(self.num_resolutions-1) - self.z_shape = (1,z_channels,curr_res,curr_res) - print("Working with z of shape {} = {} dimensions.".format( - self.z_shape, np.prod(self.z_shape))) - - # z to block_in - self.conv_in = torch.nn.Conv2d(z_channels, - block_in, - kernel_size=3, - stride=1, - padding=1) - - # middle - self.mid = nn.Module() - self.mid.block_1 = ResnetBlock(in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout) - self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) - self.mid.block_2 = ResnetBlock(in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout) - - # upsampling - self.up = nn.ModuleList() - for i_level in reversed(range(self.num_resolutions)): - block = nn.ModuleList() - attn = nn.ModuleList() - block_out = ch*ch_mult[i_level] - for i_block in range(self.num_res_blocks+1): - block.append(ResnetBlock(in_channels=block_in, - out_channels=block_out, - temb_channels=self.temb_ch, - dropout=dropout)) - block_in = block_out - if curr_res in attn_resolutions: - attn.append(make_attn(block_in, attn_type=attn_type)) - up = nn.Module() - up.block = block - up.attn = attn - if i_level != 0: - up.upsample = Upsample(block_in, resamp_with_conv) - curr_res = curr_res * 2 - self.up.insert(0, up) # prepend to get consistent order - - # end - self.norm_out = Normalize(block_in) - self.conv_out = torch.nn.Conv2d(block_in, - out_ch, - kernel_size=3, - stride=1, - padding=1) - - def forward(self, z): - #assert z.shape[1:] == self.z_shape[1:] - self.last_z_shape = z.shape - - # timestep embedding - temb = None - - # z to block_in - h = self.conv_in(z) - - # middle - h = self.mid.block_1(h, temb) - h = self.mid.attn_1(h) - h = self.mid.block_2(h, temb) - - # upsampling - for i_level in reversed(range(self.num_resolutions)): - for i_block in range(self.num_res_blocks+1): - h = self.up[i_level].block[i_block](h, temb) - if len(self.up[i_level].attn) > 0: - h = self.up[i_level].attn[i_block](h) - if i_level != 0: - h = self.up[i_level].upsample(h) - - # end - if self.give_pre_end: - return h - - h = self.norm_out(h) - h = nonlinearity(h) - h = self.conv_out(h) - if self.tanh_out: - h = torch.tanh(h) - return h - - -class SimpleDecoder(nn.Module): - def __init__(self, in_channels, out_channels, *args, **kwargs): - super().__init__() - self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1), - ResnetBlock(in_channels=in_channels, - out_channels=2 * in_channels, - temb_channels=0, dropout=0.0), - ResnetBlock(in_channels=2 * in_channels, - out_channels=4 * in_channels, - temb_channels=0, dropout=0.0), - ResnetBlock(in_channels=4 * in_channels, - out_channels=2 * in_channels, - temb_channels=0, dropout=0.0), - nn.Conv2d(2*in_channels, in_channels, 1), - Upsample(in_channels, with_conv=True)]) - # end - self.norm_out = Normalize(in_channels) - self.conv_out = torch.nn.Conv2d(in_channels, - out_channels, - kernel_size=3, - stride=1, - padding=1) - - def forward(self, x): - for i, layer in enumerate(self.model): - if i in [1,2,3]: - x = layer(x, None) - else: - x = layer(x) - - h = self.norm_out(x) - h = nonlinearity(h) - x = self.conv_out(h) - return x - - -class UpsampleDecoder(nn.Module): - def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution, - ch_mult=(2,2), dropout=0.0): - super().__init__() - # upsampling - self.temb_ch = 0 - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - block_in = in_channels - curr_res = resolution // 2 ** (self.num_resolutions - 1) - self.res_blocks = nn.ModuleList() - self.upsample_blocks = nn.ModuleList() - for i_level in range(self.num_resolutions): - res_block = [] - block_out = ch * ch_mult[i_level] - for i_block in range(self.num_res_blocks + 1): - res_block.append(ResnetBlock(in_channels=block_in, - out_channels=block_out, - temb_channels=self.temb_ch, - dropout=dropout)) - block_in = block_out - self.res_blocks.append(nn.ModuleList(res_block)) - if i_level != self.num_resolutions - 1: - self.upsample_blocks.append(Upsample(block_in, True)) - curr_res = curr_res * 2 - - # end - self.norm_out = Normalize(block_in) - self.conv_out = torch.nn.Conv2d(block_in, - out_channels, - kernel_size=3, - stride=1, - padding=1) - - def forward(self, x): - # upsampling - h = x - for k, i_level in enumerate(range(self.num_resolutions)): - for i_block in range(self.num_res_blocks + 1): - h = self.res_blocks[i_level][i_block](h, None) - if i_level != self.num_resolutions - 1: - h = self.upsample_blocks[k](h) - h = self.norm_out(h) - h = nonlinearity(h) - h = self.conv_out(h) - return h - - -class LatentRescaler(nn.Module): - def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): - super().__init__() - # residual block, interpolate, residual block - self.factor = factor - self.conv_in = nn.Conv2d(in_channels, - mid_channels, - kernel_size=3, - stride=1, - padding=1) - self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, - out_channels=mid_channels, - temb_channels=0, - dropout=0.0) for _ in range(depth)]) - self.attn = AttnBlock(mid_channels) - self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, - out_channels=mid_channels, - temb_channels=0, - dropout=0.0) for _ in range(depth)]) - - self.conv_out = nn.Conv2d(mid_channels, - out_channels, - kernel_size=1, - ) - - def forward(self, x): - x = self.conv_in(x) - for block in self.res_block1: - x = block(x, None) - x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor)))) - x = self.attn(x) - for block in self.res_block2: - x = block(x, None) - x = self.conv_out(x) - return x - - -class MergedRescaleEncoder(nn.Module): - def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks, - attn_resolutions, dropout=0.0, resamp_with_conv=True, - ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1): - super().__init__() - intermediate_chn = ch * ch_mult[-1] - self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult, - z_channels=intermediate_chn, double_z=False, resolution=resolution, - attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, - out_ch=None) - self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn, - mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth) - - def forward(self, x): - x = self.encoder(x) - x = self.rescaler(x) - return x - - -class MergedRescaleDecoder(nn.Module): - def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8), - dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1): - super().__init__() - tmp_chn = z_channels*ch_mult[-1] - self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout, - resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks, - ch_mult=ch_mult, resolution=resolution, ch=ch) - self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn, - out_channels=tmp_chn, depth=rescale_module_depth) - - def forward(self, x): - x = self.rescaler(x) - x = self.decoder(x) - return x - - -class Upsampler(nn.Module): - def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): - super().__init__() - assert out_size >= in_size - num_blocks = int(np.log2(out_size//in_size))+1 - factor_up = 1.+ (out_size % in_size) - print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}") - self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels, - out_channels=in_channels) - self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2, - attn_resolutions=[], in_channels=None, ch=in_channels, - ch_mult=[ch_mult for _ in range(num_blocks)]) - - def forward(self, x): - x = self.rescaler(x) - x = self.decoder(x) - return x - - -class Resize(nn.Module): - def __init__(self, in_channels=None, learned=False, mode="bilinear"): - super().__init__() - self.with_conv = learned - self.mode = mode - if self.with_conv: - print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode") - raise NotImplementedError() - assert in_channels is not None - # no asymmetric padding in torch conv, must do it ourselves - self.conv = torch.nn.Conv2d(in_channels, - in_channels, - kernel_size=4, - stride=2, - padding=1) - - def forward(self, x, scale_factor=1.0): - if scale_factor==1.0: - return x - else: - x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor) - return x diff --git a/ldm/modules/diffusionmodules/openaimodel.py b/ldm/modules/diffusionmodules/openaimodel.py deleted file mode 100644 index 7df6b5abfe8eff07f0c8e8703ba8aee90d45984b..0000000000000000000000000000000000000000 --- a/ldm/modules/diffusionmodules/openaimodel.py +++ /dev/null @@ -1,786 +0,0 @@ -from abc import abstractmethod -import math - -import numpy as np -import torch as th -import torch.nn as nn -import torch.nn.functional as F - -from ldm.modules.diffusionmodules.util import ( - checkpoint, - conv_nd, - linear, - avg_pool_nd, - zero_module, - normalization, - timestep_embedding, -) -from ldm.modules.attention import SpatialTransformer -from ldm.util import exists - - -# dummy replace -def convert_module_to_f16(x): - pass - -def convert_module_to_f32(x): - pass - - -## go -class AttentionPool2d(nn.Module): - """ - Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py - """ - - def __init__( - self, - spacial_dim: int, - embed_dim: int, - num_heads_channels: int, - output_dim: int = None, - ): - super().__init__() - self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5) - self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1) - self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1) - self.num_heads = embed_dim // num_heads_channels - self.attention = QKVAttention(self.num_heads) - - def forward(self, x): - b, c, *_spatial = x.shape - x = x.reshape(b, c, -1) # NC(HW) - x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1) - x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1) - x = self.qkv_proj(x) - x = self.attention(x) - x = self.c_proj(x) - return x[:, :, 0] - - -class TimestepBlock(nn.Module): - """ - Any module where forward() takes timestep embeddings as a second argument. - """ - - @abstractmethod - def forward(self, x, emb): - """ - Apply the module to `x` given `emb` timestep embeddings. - """ - - -class TimestepEmbedSequential(nn.Sequential, TimestepBlock): - """ - A sequential module that passes timestep embeddings to the children that - support it as an extra input. - """ - - def forward(self, x, emb, context=None): - for layer in self: - if isinstance(layer, TimestepBlock): - x = layer(x, emb) - elif isinstance(layer, SpatialTransformer): - x = layer(x, context) - else: - x = layer(x) - return x - - -class Upsample(nn.Module): - """ - An upsampling layer with an optional convolution. - :param channels: channels in the inputs and outputs. - :param use_conv: a bool determining if a convolution is applied. - :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then - upsampling occurs in the inner-two dimensions. - """ - - def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.dims = dims - if use_conv: - self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding) - - def forward(self, x): - assert x.shape[1] == self.channels - if self.dims == 3: - x = F.interpolate( - x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" - ) - else: - x = F.interpolate(x, scale_factor=2, mode="nearest") - if self.use_conv: - x = self.conv(x) - return x - -class TransposedUpsample(nn.Module): - 'Learned 2x upsampling without padding' - def __init__(self, channels, out_channels=None, ks=5): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - - self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2) - - def forward(self,x): - return self.up(x) - - -class Downsample(nn.Module): - """ - A downsampling layer with an optional convolution. - :param channels: channels in the inputs and outputs. - :param use_conv: a bool determining if a convolution is applied. - :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then - downsampling occurs in the inner-two dimensions. - """ - - def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.dims = dims - stride = 2 if dims != 3 else (1, 2, 2) - if use_conv: - self.op = conv_nd( - dims, self.channels, self.out_channels, 3, stride=stride, padding=padding - ) - else: - assert self.channels == self.out_channels - self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) - - def forward(self, x): - assert x.shape[1] == self.channels - return self.op(x) - - -class ResBlock(TimestepBlock): - """ - A residual block that can optionally change the number of channels. - :param channels: the number of input channels. - :param emb_channels: the number of timestep embedding channels. - :param dropout: the rate of dropout. - :param out_channels: if specified, the number of out channels. - :param use_conv: if True and out_channels is specified, use a spatial - convolution instead of a smaller 1x1 convolution to change the - channels in the skip connection. - :param dims: determines if the signal is 1D, 2D, or 3D. - :param use_checkpoint: if True, use gradient checkpointing on this module. - :param up: if True, use this block for upsampling. - :param down: if True, use this block for downsampling. - """ - - def __init__( - self, - channels, - emb_channels, - dropout, - out_channels=None, - use_conv=False, - use_scale_shift_norm=False, - dims=2, - use_checkpoint=False, - up=False, - down=False, - ): - super().__init__() - self.channels = channels - self.emb_channels = emb_channels - self.dropout = dropout - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.use_checkpoint = use_checkpoint - self.use_scale_shift_norm = use_scale_shift_norm - - self.in_layers = nn.Sequential( - normalization(channels), - nn.SiLU(), - conv_nd(dims, channels, self.out_channels, 3, padding=1), - ) - - self.updown = up or down - - if up: - self.h_upd = Upsample(channels, False, dims) - self.x_upd = Upsample(channels, False, dims) - elif down: - self.h_upd = Downsample(channels, False, dims) - self.x_upd = Downsample(channels, False, dims) - else: - self.h_upd = self.x_upd = nn.Identity() - - self.emb_layers = nn.Sequential( - nn.SiLU(), - linear( - emb_channels, - 2 * self.out_channels if use_scale_shift_norm else self.out_channels, - ), - ) - self.out_layers = nn.Sequential( - normalization(self.out_channels), - nn.SiLU(), - nn.Dropout(p=dropout), - zero_module( - conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1) - ), - ) - - if self.out_channels == channels: - self.skip_connection = nn.Identity() - elif use_conv: - self.skip_connection = conv_nd( - dims, channels, self.out_channels, 3, padding=1 - ) - else: - self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) - - def forward(self, x, emb): - """ - Apply the block to a Tensor, conditioned on a timestep embedding. - :param x: an [N x C x ...] Tensor of features. - :param emb: an [N x emb_channels] Tensor of timestep embeddings. - :return: an [N x C x ...] Tensor of outputs. - """ - return checkpoint( - self._forward, (x, emb), self.parameters(), self.use_checkpoint - ) - - - def _forward(self, x, emb): - if self.updown: - in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] - h = in_rest(x) - h = self.h_upd(h) - x = self.x_upd(x) - h = in_conv(h) - else: - h = self.in_layers(x) - emb_out = self.emb_layers(emb).type(h.dtype) - while len(emb_out.shape) < len(h.shape): - emb_out = emb_out[..., None] - if self.use_scale_shift_norm: - out_norm, out_rest = self.out_layers[0], self.out_layers[1:] - scale, shift = th.chunk(emb_out, 2, dim=1) - h = out_norm(h) * (1 + scale) + shift - h = out_rest(h) - else: - h = h + emb_out - h = self.out_layers(h) - return self.skip_connection(x) + h - - -class AttentionBlock(nn.Module): - """ - An attention block that allows spatial positions to attend to each other. - Originally ported from here, but adapted to the N-d case. - https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. - """ - - def __init__( - self, - channels, - num_heads=1, - num_head_channels=-1, - use_checkpoint=False, - use_new_attention_order=False, - ): - super().__init__() - self.channels = channels - if num_head_channels == -1: - self.num_heads = num_heads - else: - assert ( - channels % num_head_channels == 0 - ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" - self.num_heads = channels // num_head_channels - self.use_checkpoint = use_checkpoint - self.norm = normalization(channels) - self.qkv = conv_nd(1, channels, channels * 3, 1) - if use_new_attention_order: - # split qkv before split heads - self.attention = QKVAttention(self.num_heads) - else: - # split heads before split qkv - self.attention = QKVAttentionLegacy(self.num_heads) - - self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) - - def forward(self, x): - return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!! - #return pt_checkpoint(self._forward, x) # pytorch - - def _forward(self, x): - b, c, *spatial = x.shape - x = x.reshape(b, c, -1) - qkv = self.qkv(self.norm(x)) - h = self.attention(qkv) - h = self.proj_out(h) - return (x + h).reshape(b, c, *spatial) - - -def count_flops_attn(model, _x, y): - """ - A counter for the `thop` package to count the operations in an - attention operation. - Meant to be used like: - macs, params = thop.profile( - model, - inputs=(inputs, timestamps), - custom_ops={QKVAttention: QKVAttention.count_flops}, - ) - """ - b, c, *spatial = y[0].shape - num_spatial = int(np.prod(spatial)) - # We perform two matmuls with the same number of ops. - # The first computes the weight matrix, the second computes - # the combination of the value vectors. - matmul_ops = 2 * b * (num_spatial ** 2) * c - model.total_ops += th.DoubleTensor([matmul_ops]) - - -class QKVAttentionLegacy(nn.Module): - """ - A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping - """ - - def __init__(self, n_heads): - super().__init__() - self.n_heads = n_heads - - def forward(self, qkv): - """ - Apply QKV attention. - :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. - :return: an [N x (H * C) x T] tensor after attention. - """ - bs, width, length = qkv.shape - assert width % (3 * self.n_heads) == 0 - ch = width // (3 * self.n_heads) - q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) - scale = 1 / math.sqrt(math.sqrt(ch)) - weight = th.einsum( - "bct,bcs->bts", q * scale, k * scale - ) # More stable with f16 than dividing afterwards - weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) - a = th.einsum("bts,bcs->bct", weight, v) - return a.reshape(bs, -1, length) - - @staticmethod - def count_flops(model, _x, y): - return count_flops_attn(model, _x, y) - - -class QKVAttention(nn.Module): - """ - A module which performs QKV attention and splits in a different order. - """ - - def __init__(self, n_heads): - super().__init__() - self.n_heads = n_heads - - def forward(self, qkv): - """ - Apply QKV attention. - :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. - :return: an [N x (H * C) x T] tensor after attention. - """ - bs, width, length = qkv.shape - assert width % (3 * self.n_heads) == 0 - ch = width // (3 * self.n_heads) - q, k, v = qkv.chunk(3, dim=1) - scale = 1 / math.sqrt(math.sqrt(ch)) - weight = th.einsum( - "bct,bcs->bts", - (q * scale).view(bs * self.n_heads, ch, length), - (k * scale).view(bs * self.n_heads, ch, length), - ) # More stable with f16 than dividing afterwards - weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) - a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) - return a.reshape(bs, -1, length) - - @staticmethod - def count_flops(model, _x, y): - return count_flops_attn(model, _x, y) - - -class UNetModel(nn.Module): - """ - The full UNet model with attention and timestep embedding. - :param in_channels: channels in the input Tensor. - :param model_channels: base channel count for the model. - :param out_channels: channels in the output Tensor. - :param num_res_blocks: number of residual blocks per downsample. - :param attention_resolutions: a collection of downsample rates at which - attention will take place. May be a set, list, or tuple. - For example, if this contains 4, then at 4x downsampling, attention - will be used. - :param dropout: the dropout probability. - :param channel_mult: channel multiplier for each level of the UNet. - :param conv_resample: if True, use learned convolutions for upsampling and - downsampling. - :param dims: determines if the signal is 1D, 2D, or 3D. - :param num_classes: if specified (as an int), then this model will be - class-conditional with `num_classes` classes. - :param use_checkpoint: use gradient checkpointing to reduce memory usage. - :param num_heads: the number of attention heads in each attention layer. - :param num_heads_channels: if specified, ignore num_heads and instead use - a fixed channel width per attention head. - :param num_heads_upsample: works with num_heads to set a different number - of heads for upsampling. Deprecated. - :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. - :param resblock_updown: use residual blocks for up/downsampling. - :param use_new_attention_order: use a different attention pattern for potentially - increased efficiency. - """ - - def __init__( - self, - image_size, - in_channels, - model_channels, - out_channels, - num_res_blocks, - attention_resolutions, - dropout=0, - channel_mult=(1, 2, 4, 8), - conv_resample=True, - dims=2, - num_classes=None, - use_checkpoint=False, - use_fp16=False, - num_heads=-1, - num_head_channels=-1, - num_heads_upsample=-1, - use_scale_shift_norm=False, - resblock_updown=False, - use_new_attention_order=False, - use_spatial_transformer=False, # custom transformer support - transformer_depth=1, # custom transformer support - context_dim=None, # custom transformer support - n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model - legacy=True, - disable_self_attentions=None, - num_attention_blocks=None, - disable_middle_self_attn=False, - use_linear_in_transformer=False, - ): - super().__init__() - if use_spatial_transformer: - assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' - - if context_dim is not None: - assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' - from omegaconf.listconfig import ListConfig - if type(context_dim) == ListConfig: - context_dim = list(context_dim) - - if num_heads_upsample == -1: - num_heads_upsample = num_heads - - if num_heads == -1: - assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' - - if num_head_channels == -1: - assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' - - self.image_size = image_size - self.in_channels = in_channels - self.model_channels = model_channels - self.out_channels = out_channels - if isinstance(num_res_blocks, int): - self.num_res_blocks = len(channel_mult) * [num_res_blocks] - else: - if len(num_res_blocks) != len(channel_mult): - raise ValueError("provide num_res_blocks either as an int (globally constant) or " - "as a list/tuple (per-level) with the same length as channel_mult") - self.num_res_blocks = num_res_blocks - if disable_self_attentions is not None: - # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not - assert len(disable_self_attentions) == len(channel_mult) - if num_attention_blocks is not None: - assert len(num_attention_blocks) == len(self.num_res_blocks) - assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks)))) - print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. " - f"This option has LESS priority than attention_resolutions {attention_resolutions}, " - f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, " - f"attention will still not be set.") - - self.attention_resolutions = attention_resolutions - self.dropout = dropout - self.channel_mult = channel_mult - self.conv_resample = conv_resample - self.num_classes = num_classes - self.use_checkpoint = use_checkpoint - self.dtype = th.float16 if use_fp16 else th.float32 - self.num_heads = num_heads - self.num_head_channels = num_head_channels - self.num_heads_upsample = num_heads_upsample - self.predict_codebook_ids = n_embed is not None - - time_embed_dim = model_channels * 4 - self.time_embed = nn.Sequential( - linear(model_channels, time_embed_dim), - nn.SiLU(), - linear(time_embed_dim, time_embed_dim), - ) - - if self.num_classes is not None: - if isinstance(self.num_classes, int): - self.label_emb = nn.Embedding(num_classes, time_embed_dim) - elif self.num_classes == "continuous": - print("setting up linear c_adm embedding layer") - self.label_emb = nn.Linear(1, time_embed_dim) - else: - raise ValueError() - - self.input_blocks = nn.ModuleList( - [ - TimestepEmbedSequential( - conv_nd(dims, in_channels, model_channels, 3, padding=1) - ) - ] - ) - self._feature_size = model_channels - input_block_chans = [model_channels] - ch = model_channels - ds = 1 - for level, mult in enumerate(channel_mult): - for nr in range(self.num_res_blocks[level]): - layers = [ - ResBlock( - ch, - time_embed_dim, - dropout, - out_channels=mult * model_channels, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - ) - ] - ch = mult * model_channels - if ds in attention_resolutions: - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - #num_heads = 1 - dim_head = ch // num_heads if use_spatial_transformer else num_head_channels - if exists(disable_self_attentions): - disabled_sa = disable_self_attentions[level] - else: - disabled_sa = False - - if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: - layers.append( - AttentionBlock( - ch, - use_checkpoint=use_checkpoint, - num_heads=num_heads, - num_head_channels=dim_head, - use_new_attention_order=use_new_attention_order, - ) if not use_spatial_transformer else SpatialTransformer( - ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, - disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint - ) - ) - self.input_blocks.append(TimestepEmbedSequential(*layers)) - self._feature_size += ch - input_block_chans.append(ch) - if level != len(channel_mult) - 1: - out_ch = ch - self.input_blocks.append( - TimestepEmbedSequential( - ResBlock( - ch, - time_embed_dim, - dropout, - out_channels=out_ch, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - down=True, - ) - if resblock_updown - else Downsample( - ch, conv_resample, dims=dims, out_channels=out_ch - ) - ) - ) - ch = out_ch - input_block_chans.append(ch) - ds *= 2 - self._feature_size += ch - - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - #num_heads = 1 - dim_head = ch // num_heads if use_spatial_transformer else num_head_channels - self.middle_block = TimestepEmbedSequential( - ResBlock( - ch, - time_embed_dim, - dropout, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - ), - AttentionBlock( - ch, - use_checkpoint=use_checkpoint, - num_heads=num_heads, - num_head_channels=dim_head, - use_new_attention_order=use_new_attention_order, - ) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn - ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, - disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint - ), - ResBlock( - ch, - time_embed_dim, - dropout, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - ), - ) - self._feature_size += ch - - self.output_blocks = nn.ModuleList([]) - for level, mult in list(enumerate(channel_mult))[::-1]: - for i in range(self.num_res_blocks[level] + 1): - ich = input_block_chans.pop() - layers = [ - ResBlock( - ch + ich, - time_embed_dim, - dropout, - out_channels=model_channels * mult, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - ) - ] - ch = model_channels * mult - if ds in attention_resolutions: - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - #num_heads = 1 - dim_head = ch // num_heads if use_spatial_transformer else num_head_channels - if exists(disable_self_attentions): - disabled_sa = disable_self_attentions[level] - else: - disabled_sa = False - - if not exists(num_attention_blocks) or i < num_attention_blocks[level]: - layers.append( - AttentionBlock( - ch, - use_checkpoint=use_checkpoint, - num_heads=num_heads_upsample, - num_head_channels=dim_head, - use_new_attention_order=use_new_attention_order, - ) if not use_spatial_transformer else SpatialTransformer( - ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, - disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint - ) - ) - if level and i == self.num_res_blocks[level]: - out_ch = ch - layers.append( - ResBlock( - ch, - time_embed_dim, - dropout, - out_channels=out_ch, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - up=True, - ) - if resblock_updown - else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) - ) - ds //= 2 - self.output_blocks.append(TimestepEmbedSequential(*layers)) - self._feature_size += ch - - self.out = nn.Sequential( - normalization(ch), - nn.SiLU(), - zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), - ) - if self.predict_codebook_ids: - self.id_predictor = nn.Sequential( - normalization(ch), - conv_nd(dims, model_channels, n_embed, 1), - #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits - ) - - def convert_to_fp16(self): - """ - Convert the torso of the model to float16. - """ - self.input_blocks.apply(convert_module_to_f16) - self.middle_block.apply(convert_module_to_f16) - self.output_blocks.apply(convert_module_to_f16) - - def convert_to_fp32(self): - """ - Convert the torso of the model to float32. - """ - self.input_blocks.apply(convert_module_to_f32) - self.middle_block.apply(convert_module_to_f32) - self.output_blocks.apply(convert_module_to_f32) - - def forward(self, x, timesteps=None, context=None, y=None,**kwargs): - """ - Apply the model to an input batch. - :param x: an [N x C x ...] Tensor of inputs. - :param timesteps: a 1-D batch of timesteps. - :param context: conditioning plugged in via crossattn - :param y: an [N] Tensor of labels, if class-conditional. - :return: an [N x C x ...] Tensor of outputs. - """ - assert (y is not None) == ( - self.num_classes is not None - ), "must specify y if and only if the model is class-conditional" - hs = [] - t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) - emb = self.time_embed(t_emb) - - if self.num_classes is not None: - assert y.shape[0] == x.shape[0] - emb = emb + self.label_emb(y) - - h = x.type(self.dtype) - for module in self.input_blocks: - h = module(h, emb, context) - hs.append(h) - h = self.middle_block(h, emb, context) - for module in self.output_blocks: - h = th.cat([h, hs.pop()], dim=1) - h = module(h, emb, context) - h = h.type(x.dtype) - if self.predict_codebook_ids: - return self.id_predictor(h) - else: - return self.out(h) diff --git a/ldm/modules/diffusionmodules/upscaling.py b/ldm/modules/diffusionmodules/upscaling.py deleted file mode 100644 index 03816662098ce1ffac79bd939b892e867ab91988..0000000000000000000000000000000000000000 --- a/ldm/modules/diffusionmodules/upscaling.py +++ /dev/null @@ -1,81 +0,0 @@ -import torch -import torch.nn as nn -import numpy as np -from functools import partial - -from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule -from ldm.util import default - - -class AbstractLowScaleModel(nn.Module): - # for concatenating a downsampled image to the latent representation - def __init__(self, noise_schedule_config=None): - super(AbstractLowScaleModel, self).__init__() - if noise_schedule_config is not None: - self.register_schedule(**noise_schedule_config) - - def register_schedule(self, beta_schedule="linear", timesteps=1000, - linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): - betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, - cosine_s=cosine_s) - alphas = 1. - betas - alphas_cumprod = np.cumprod(alphas, axis=0) - alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) - - timesteps, = betas.shape - self.num_timesteps = int(timesteps) - self.linear_start = linear_start - self.linear_end = linear_end - assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' - - to_torch = partial(torch.tensor, dtype=torch.float32) - - self.register_buffer('betas', to_torch(betas)) - self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) - self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) - - # calculations for diffusion q(x_t | x_{t-1}) and others - self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) - self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) - self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) - self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) - self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) - - def q_sample(self, x_start, t, noise=None): - noise = default(noise, lambda: torch.randn_like(x_start)) - return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) - - def forward(self, x): - return x, None - - def decode(self, x): - return x - - -class SimpleImageConcat(AbstractLowScaleModel): - # no noise level conditioning - def __init__(self): - super(SimpleImageConcat, self).__init__(noise_schedule_config=None) - self.max_noise_level = 0 - - def forward(self, x): - # fix to constant noise level - return x, torch.zeros(x.shape[0], device=x.device).long() - - -class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel): - def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False): - super().__init__(noise_schedule_config=noise_schedule_config) - self.max_noise_level = max_noise_level - - def forward(self, x, noise_level=None): - if noise_level is None: - noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() - else: - assert isinstance(noise_level, torch.Tensor) - z = self.q_sample(x, noise_level) - return z, noise_level - - - diff --git a/ldm/modules/diffusionmodules/util.py b/ldm/modules/diffusionmodules/util.py deleted file mode 100644 index 637363dfe34799e70cfdbcd11445212df9d9ca1f..0000000000000000000000000000000000000000 --- a/ldm/modules/diffusionmodules/util.py +++ /dev/null @@ -1,270 +0,0 @@ -# adopted from -# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py -# and -# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py -# and -# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py -# -# thanks! - - -import os -import math -import torch -import torch.nn as nn -import numpy as np -from einops import repeat - -from ldm.util import instantiate_from_config - - -def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): - if schedule == "linear": - betas = ( - torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 - ) - - elif schedule == "cosine": - timesteps = ( - torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s - ) - alphas = timesteps / (1 + cosine_s) * np.pi / 2 - alphas = torch.cos(alphas).pow(2) - alphas = alphas / alphas[0] - betas = 1 - alphas[1:] / alphas[:-1] - betas = np.clip(betas, a_min=0, a_max=0.999) - - elif schedule == "sqrt_linear": - betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) - elif schedule == "sqrt": - betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 - else: - raise ValueError(f"schedule '{schedule}' unknown.") - return betas.numpy() - - -def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): - if ddim_discr_method == 'uniform': - c = num_ddpm_timesteps // num_ddim_timesteps - ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) - elif ddim_discr_method == 'quad': - ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) - else: - raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') - - # assert ddim_timesteps.shape[0] == num_ddim_timesteps - # add one to get the final alpha values right (the ones from first scale to data during sampling) - steps_out = ddim_timesteps + 1 - if verbose: - print(f'Selected timesteps for ddim sampler: {steps_out}') - return steps_out - - -def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): - # select alphas for computing the variance schedule - alphas = alphacums[ddim_timesteps] - alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) - - # according the the formula provided in https://arxiv.org/abs/2010.02502 - sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) - if verbose: - print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') - print(f'For the chosen value of eta, which is {eta}, ' - f'this results in the following sigma_t schedule for ddim sampler {sigmas}') - return sigmas, alphas, alphas_prev - - -def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): - """ - Create a beta schedule that discretizes the given alpha_t_bar function, - which defines the cumulative product of (1-beta) over time from t = [0,1]. - :param num_diffusion_timesteps: the number of betas to produce. - :param alpha_bar: a lambda that takes an argument t from 0 to 1 and - produces the cumulative product of (1-beta) up to that - part of the diffusion process. - :param max_beta: the maximum beta to use; use values lower than 1 to - prevent singularities. - """ - betas = [] - for i in range(num_diffusion_timesteps): - t1 = i / num_diffusion_timesteps - t2 = (i + 1) / num_diffusion_timesteps - betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) - return np.array(betas) - - -def extract_into_tensor(a, t, x_shape): - b, *_ = t.shape - out = a.gather(-1, t) - return out.reshape(b, *((1,) * (len(x_shape) - 1))) - - -def checkpoint(func, inputs, params, flag): - """ - Evaluate a function without caching intermediate activations, allowing for - reduced memory at the expense of extra compute in the backward pass. - :param func: the function to evaluate. - :param inputs: the argument sequence to pass to `func`. - :param params: a sequence of parameters `func` depends on but does not - explicitly take as arguments. - :param flag: if False, disable gradient checkpointing. - """ - if flag: - args = tuple(inputs) + tuple(params) - return CheckpointFunction.apply(func, len(inputs), *args) - else: - return func(*inputs) - - -class CheckpointFunction(torch.autograd.Function): - @staticmethod - def forward(ctx, run_function, length, *args): - ctx.run_function = run_function - ctx.input_tensors = list(args[:length]) - ctx.input_params = list(args[length:]) - ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(), - "dtype": torch.get_autocast_gpu_dtype(), - "cache_enabled": torch.is_autocast_cache_enabled()} - with torch.no_grad(): - output_tensors = ctx.run_function(*ctx.input_tensors) - return output_tensors - - @staticmethod - def backward(ctx, *output_grads): - ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] - with torch.enable_grad(), \ - torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): - # Fixes a bug where the first op in run_function modifies the - # Tensor storage in place, which is not allowed for detach()'d - # Tensors. - shallow_copies = [x.view_as(x) for x in ctx.input_tensors] - output_tensors = ctx.run_function(*shallow_copies) - input_grads = torch.autograd.grad( - output_tensors, - ctx.input_tensors + ctx.input_params, - output_grads, - allow_unused=True, - ) - del ctx.input_tensors - del ctx.input_params - del output_tensors - return (None, None) + input_grads - - -def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): - """ - Create sinusoidal timestep embeddings. - :param timesteps: a 1-D Tensor of N indices, one per batch element. - These may be fractional. - :param dim: the dimension of the output. - :param max_period: controls the minimum frequency of the embeddings. - :return: an [N x dim] Tensor of positional embeddings. - """ - if not repeat_only: - half = dim // 2 - freqs = torch.exp( - -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half - ).to(device=timesteps.device) - args = timesteps[:, None].float() * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - if dim % 2: - embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) - else: - embedding = repeat(timesteps, 'b -> b d', d=dim) - return embedding - - -def zero_module(module): - """ - Zero out the parameters of a module and return it. - """ - for p in module.parameters(): - p.detach().zero_() - return module - - -def scale_module(module, scale): - """ - Scale the parameters of a module and return it. - """ - for p in module.parameters(): - p.detach().mul_(scale) - return module - - -def mean_flat(tensor): - """ - Take the mean over all non-batch dimensions. - """ - return tensor.mean(dim=list(range(1, len(tensor.shape)))) - - -def normalization(channels): - """ - Make a standard normalization layer. - :param channels: number of input channels. - :return: an nn.Module for normalization. - """ - return GroupNorm32(32, channels) - - -# PyTorch 1.7 has SiLU, but we support PyTorch 1.5. -class SiLU(nn.Module): - def forward(self, x): - return x * torch.sigmoid(x) - - -class GroupNorm32(nn.GroupNorm): - def forward(self, x): - return super().forward(x.float()).type(x.dtype) - -def conv_nd(dims, *args, **kwargs): - """ - Create a 1D, 2D, or 3D convolution module. - """ - if dims == 1: - return nn.Conv1d(*args, **kwargs) - elif dims == 2: - return nn.Conv2d(*args, **kwargs) - elif dims == 3: - return nn.Conv3d(*args, **kwargs) - raise ValueError(f"unsupported dimensions: {dims}") - - -def linear(*args, **kwargs): - """ - Create a linear module. - """ - return nn.Linear(*args, **kwargs) - - -def avg_pool_nd(dims, *args, **kwargs): - """ - Create a 1D, 2D, or 3D average pooling module. - """ - if dims == 1: - return nn.AvgPool1d(*args, **kwargs) - elif dims == 2: - return nn.AvgPool2d(*args, **kwargs) - elif dims == 3: - return nn.AvgPool3d(*args, **kwargs) - raise ValueError(f"unsupported dimensions: {dims}") - - -class HybridConditioner(nn.Module): - - def __init__(self, c_concat_config, c_crossattn_config): - super().__init__() - self.concat_conditioner = instantiate_from_config(c_concat_config) - self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) - - def forward(self, c_concat, c_crossattn): - c_concat = self.concat_conditioner(c_concat) - c_crossattn = self.crossattn_conditioner(c_crossattn) - return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} - - -def noise_like(shape, device, repeat=False): - repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) - noise = lambda: torch.randn(shape, device=device) - return repeat_noise() if repeat else noise() \ No newline at end of file diff --git a/ldm/modules/distributions/__init__.py b/ldm/modules/distributions/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/ldm/modules/distributions/distributions.py b/ldm/modules/distributions/distributions.py deleted file mode 100644 index f2b8ef901130efc171aa69742ca0244d94d3f2e9..0000000000000000000000000000000000000000 --- a/ldm/modules/distributions/distributions.py +++ /dev/null @@ -1,92 +0,0 @@ -import torch -import numpy as np - - -class AbstractDistribution: - def sample(self): - raise NotImplementedError() - - def mode(self): - raise NotImplementedError() - - -class DiracDistribution(AbstractDistribution): - def __init__(self, value): - self.value = value - - def sample(self): - return self.value - - def mode(self): - return self.value - - -class DiagonalGaussianDistribution(object): - def __init__(self, parameters, deterministic=False): - self.parameters = parameters - self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) - self.logvar = torch.clamp(self.logvar, -30.0, 20.0) - self.deterministic = deterministic - self.std = torch.exp(0.5 * self.logvar) - self.var = torch.exp(self.logvar) - if self.deterministic: - self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) - - def sample(self): - x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) - return x - - def kl(self, other=None): - if self.deterministic: - return torch.Tensor([0.]) - else: - if other is None: - return 0.5 * torch.sum(torch.pow(self.mean, 2) - + self.var - 1.0 - self.logvar, - dim=[1, 2, 3]) - else: - return 0.5 * torch.sum( - torch.pow(self.mean - other.mean, 2) / other.var - + self.var / other.var - 1.0 - self.logvar + other.logvar, - dim=[1, 2, 3]) - - def nll(self, sample, dims=[1,2,3]): - if self.deterministic: - return torch.Tensor([0.]) - logtwopi = np.log(2.0 * np.pi) - return 0.5 * torch.sum( - logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, - dim=dims) - - def mode(self): - return self.mean - - -def normal_kl(mean1, logvar1, mean2, logvar2): - """ - source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 - Compute the KL divergence between two gaussians. - Shapes are automatically broadcasted, so batches can be compared to - scalars, among other use cases. - """ - tensor = None - for obj in (mean1, logvar1, mean2, logvar2): - if isinstance(obj, torch.Tensor): - tensor = obj - break - assert tensor is not None, "at least one argument must be a Tensor" - - # Force variances to be Tensors. Broadcasting helps convert scalars to - # Tensors, but it does not work for torch.exp(). - logvar1, logvar2 = [ - x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) - for x in (logvar1, logvar2) - ] - - return 0.5 * ( - -1.0 - + logvar2 - - logvar1 - + torch.exp(logvar1 - logvar2) - + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) - ) diff --git a/ldm/modules/ema.py b/ldm/modules/ema.py deleted file mode 100644 index bded25019b9bcbcd0260f0b8185f8c7859ca58c4..0000000000000000000000000000000000000000 --- a/ldm/modules/ema.py +++ /dev/null @@ -1,80 +0,0 @@ -import torch -from torch import nn - - -class LitEma(nn.Module): - def __init__(self, model, decay=0.9999, use_num_upates=True): - super().__init__() - if decay < 0.0 or decay > 1.0: - raise ValueError('Decay must be between 0 and 1') - - self.m_name2s_name = {} - self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) - self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates - else torch.tensor(-1, dtype=torch.int)) - - for name, p in model.named_parameters(): - if p.requires_grad: - # remove as '.'-character is not allowed in buffers - s_name = name.replace('.', '') - self.m_name2s_name.update({name: s_name}) - self.register_buffer(s_name, p.clone().detach().data) - - self.collected_params = [] - - def reset_num_updates(self): - del self.num_updates - self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int)) - - def forward(self, model): - decay = self.decay - - if self.num_updates >= 0: - self.num_updates += 1 - decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates)) - - one_minus_decay = 1.0 - decay - - with torch.no_grad(): - m_param = dict(model.named_parameters()) - shadow_params = dict(self.named_buffers()) - - for key in m_param: - if m_param[key].requires_grad: - sname = self.m_name2s_name[key] - shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) - shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) - else: - assert not key in self.m_name2s_name - - def copy_to(self, model): - m_param = dict(model.named_parameters()) - shadow_params = dict(self.named_buffers()) - for key in m_param: - if m_param[key].requires_grad: - m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) - else: - assert not key in self.m_name2s_name - - def store(self, parameters): - """ - Save the current parameters for restoring later. - Args: - parameters: Iterable of `torch.nn.Parameter`; the parameters to be - temporarily stored. - """ - self.collected_params = [param.clone() for param in parameters] - - def restore(self, parameters): - """ - Restore the parameters stored with the `store` method. - Useful to validate the model with EMA parameters without affecting the - original optimization process. Store the parameters before the - `copy_to` method. After validation (or model saving), use this to - restore the former parameters. - Args: - parameters: Iterable of `torch.nn.Parameter`; the parameters to be - updated with the stored parameters. - """ - for c_param, param in zip(self.collected_params, parameters): - param.data.copy_(c_param.data) diff --git a/ldm/modules/encoders/__init__.py b/ldm/modules/encoders/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py deleted file mode 100644 index 4edd5496b9e668ea72a5be39db9cca94b6a42f9b..0000000000000000000000000000000000000000 --- a/ldm/modules/encoders/modules.py +++ /dev/null @@ -1,213 +0,0 @@ -import torch -import torch.nn as nn -from torch.utils.checkpoint import checkpoint - -from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel - -import open_clip -from ldm.util import default, count_params - - -class AbstractEncoder(nn.Module): - def __init__(self): - super().__init__() - - def encode(self, *args, **kwargs): - raise NotImplementedError - - -class IdentityEncoder(AbstractEncoder): - - def encode(self, x): - return x - - -class ClassEmbedder(nn.Module): - def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1): - super().__init__() - self.key = key - self.embedding = nn.Embedding(n_classes, embed_dim) - self.n_classes = n_classes - self.ucg_rate = ucg_rate - - def forward(self, batch, key=None, disable_dropout=False): - if key is None: - key = self.key - # this is for use in crossattn - c = batch[key][:, None] - if self.ucg_rate > 0. and not disable_dropout: - mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate) - c = mask * c + (1-mask) * torch.ones_like(c)*(self.n_classes-1) - c = c.long() - c = self.embedding(c) - return c - - def get_unconditional_conditioning(self, bs, device="cuda"): - uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000) - uc = torch.ones((bs,), device=device) * uc_class - uc = {self.key: uc} - return uc - - -def disabled_train(self, mode=True): - """Overwrite model.train with this function to make sure train/eval mode - does not change anymore.""" - return self - - -class FrozenT5Embedder(AbstractEncoder): - """Uses the T5 transformer encoder for text""" - def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl - super().__init__() - self.tokenizer = T5Tokenizer.from_pretrained(version) - self.transformer = T5EncoderModel.from_pretrained(version) - self.device = device - self.max_length = max_length # TODO: typical value? - if freeze: - self.freeze() - - def freeze(self): - self.transformer = self.transformer.eval() - #self.train = disabled_train - for param in self.parameters(): - param.requires_grad = False - - def forward(self, text): - batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, - return_overflowing_tokens=False, padding="max_length", return_tensors="pt") - tokens = batch_encoding["input_ids"].to(self.device) - outputs = self.transformer(input_ids=tokens) - - z = outputs.last_hidden_state - return z - - def encode(self, text): - return self(text) - - -class FrozenCLIPEmbedder(AbstractEncoder): - """Uses the CLIP transformer encoder for text (from huggingface)""" - LAYERS = [ - "last", - "pooled", - "hidden" - ] - def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77, - freeze=True, layer="last", layer_idx=None): # clip-vit-base-patch32 - super().__init__() - assert layer in self.LAYERS - self.tokenizer = CLIPTokenizer.from_pretrained(version) - self.transformer = CLIPTextModel.from_pretrained(version) - self.device = device - self.max_length = max_length - if freeze: - self.freeze() - self.layer = layer - self.layer_idx = layer_idx - if layer == "hidden": - assert layer_idx is not None - assert 0 <= abs(layer_idx) <= 12 - - def freeze(self): - self.transformer = self.transformer.eval() - #self.train = disabled_train - for param in self.parameters(): - param.requires_grad = False - - def forward(self, text): - batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, - return_overflowing_tokens=False, padding="max_length", return_tensors="pt") - tokens = batch_encoding["input_ids"].to(self.device) - outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden") - if self.layer == "last": - z = outputs.last_hidden_state - elif self.layer == "pooled": - z = outputs.pooler_output[:, None, :] - else: - z = outputs.hidden_states[self.layer_idx] - return z - - def encode(self, text): - return self(text) - - -class FrozenOpenCLIPEmbedder(AbstractEncoder): - """ - Uses the OpenCLIP transformer encoder for text - """ - LAYERS = [ - #"pooled", - "last", - "penultimate" - ] - def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77, - freeze=True, layer="last"): - super().__init__() - assert layer in self.LAYERS - model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version) - del model.visual - self.model = model - - self.device = device - self.max_length = max_length - if freeze: - self.freeze() - self.layer = layer - if self.layer == "last": - self.layer_idx = 0 - elif self.layer == "penultimate": - self.layer_idx = 1 - else: - raise NotImplementedError() - - def freeze(self): - self.model = self.model.eval() - for param in self.parameters(): - param.requires_grad = False - - def forward(self, text): - tokens = open_clip.tokenize(text) - z = self.encode_with_transformer(tokens.to(self.device)) - return z - - def encode_with_transformer(self, text): - x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model] - x = x + self.model.positional_embedding - x = x.permute(1, 0, 2) # NLD -> LND - x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) - x = x.permute(1, 0, 2) # LND -> NLD - x = self.model.ln_final(x) - return x - - def text_transformer_forward(self, x: torch.Tensor, attn_mask = None): - for i, r in enumerate(self.model.transformer.resblocks): - if i == len(self.model.transformer.resblocks) - self.layer_idx: - break - if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting(): - x = checkpoint(r, x, attn_mask) - else: - x = r(x, attn_mask=attn_mask) - return x - - def encode(self, text): - return self(text) - - -class FrozenCLIPT5Encoder(AbstractEncoder): - def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda", - clip_max_length=77, t5_max_length=77): - super().__init__() - self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length) - self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length) - print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, " - f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params.") - - def encode(self, text): - return self(text) - - def forward(self, text): - clip_z = self.clip_encoder.encode(text) - t5_z = self.t5_encoder.encode(text) - return [clip_z, t5_z] - - diff --git a/ldm/modules/image_degradation/__init__.py b/ldm/modules/image_degradation/__init__.py deleted file mode 100644 index 7836cada81f90ded99c58d5942eea4c3477f58fc..0000000000000000000000000000000000000000 --- a/ldm/modules/image_degradation/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr -from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light diff --git a/ldm/modules/image_degradation/bsrgan.py b/ldm/modules/image_degradation/bsrgan.py deleted file mode 100644 index 32ef56169978e550090261cddbcf5eb611a6173b..0000000000000000000000000000000000000000 --- a/ldm/modules/image_degradation/bsrgan.py +++ /dev/null @@ -1,730 +0,0 @@ -# -*- coding: utf-8 -*- -""" -# -------------------------------------------- -# Super-Resolution -# -------------------------------------------- -# -# Kai Zhang (cskaizhang@gmail.com) -# https://github.com/cszn -# From 2019/03--2021/08 -# -------------------------------------------- -""" - -import numpy as np -import cv2 -import torch - -from functools import partial -import random -from scipy import ndimage -import scipy -import scipy.stats as ss -from scipy.interpolate import interp2d -from scipy.linalg import orth -import albumentations - -import ldm.modules.image_degradation.utils_image as util - - -def modcrop_np(img, sf): - ''' - Args: - img: numpy image, WxH or WxHxC - sf: scale factor - Return: - cropped image - ''' - w, h = img.shape[:2] - im = np.copy(img) - return im[:w - w % sf, :h - h % sf, ...] - - -""" -# -------------------------------------------- -# anisotropic Gaussian kernels -# -------------------------------------------- -""" - - -def analytic_kernel(k): - """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" - k_size = k.shape[0] - # Calculate the big kernels size - big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) - # Loop over the small kernel to fill the big one - for r in range(k_size): - for c in range(k_size): - big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k - # Crop the edges of the big kernel to ignore very small values and increase run time of SR - crop = k_size // 2 - cropped_big_k = big_k[crop:-crop, crop:-crop] - # Normalize to 1 - return cropped_big_k / cropped_big_k.sum() - - -def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): - """ generate an anisotropic Gaussian kernel - Args: - ksize : e.g., 15, kernel size - theta : [0, pi], rotation angle range - l1 : [0.1,50], scaling of eigenvalues - l2 : [0.1,l1], scaling of eigenvalues - If l1 = l2, will get an isotropic Gaussian kernel. - Returns: - k : kernel - """ - - v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.])) - V = np.array([[v[0], v[1]], [v[1], -v[0]]]) - D = np.array([[l1, 0], [0, l2]]) - Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) - k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) - - return k - - -def gm_blur_kernel(mean, cov, size=15): - center = size / 2.0 + 0.5 - k = np.zeros([size, size]) - for y in range(size): - for x in range(size): - cy = y - center + 1 - cx = x - center + 1 - k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) - - k = k / np.sum(k) - return k - - -def shift_pixel(x, sf, upper_left=True): - """shift pixel for super-resolution with different scale factors - Args: - x: WxHxC or WxH - sf: scale factor - upper_left: shift direction - """ - h, w = x.shape[:2] - shift = (sf - 1) * 0.5 - xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) - if upper_left: - x1 = xv + shift - y1 = yv + shift - else: - x1 = xv - shift - y1 = yv - shift - - x1 = np.clip(x1, 0, w - 1) - y1 = np.clip(y1, 0, h - 1) - - if x.ndim == 2: - x = interp2d(xv, yv, x)(x1, y1) - if x.ndim == 3: - for i in range(x.shape[-1]): - x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) - - return x - - -def blur(x, k): - ''' - x: image, NxcxHxW - k: kernel, Nx1xhxw - ''' - n, c = x.shape[:2] - p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 - x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate') - k = k.repeat(1, c, 1, 1) - k = k.view(-1, 1, k.shape[2], k.shape[3]) - x = x.view(1, -1, x.shape[2], x.shape[3]) - x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) - x = x.view(n, c, x.shape[2], x.shape[3]) - - return x - - -def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0): - """" - # modified version of https://github.com/assafshocher/BlindSR_dataset_generator - # Kai Zhang - # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var - # max_var = 2.5 * sf - """ - # Set random eigen-vals (lambdas) and angle (theta) for COV matrix - lambda_1 = min_var + np.random.rand() * (max_var - min_var) - lambda_2 = min_var + np.random.rand() * (max_var - min_var) - theta = np.random.rand() * np.pi # random theta - noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 - - # Set COV matrix using Lambdas and Theta - LAMBDA = np.diag([lambda_1, lambda_2]) - Q = np.array([[np.cos(theta), -np.sin(theta)], - [np.sin(theta), np.cos(theta)]]) - SIGMA = Q @ LAMBDA @ Q.T - INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] - - # Set expectation position (shifting kernel for aligned image) - MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) - MU = MU[None, None, :, None] - - # Create meshgrid for Gaussian - [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) - Z = np.stack([X, Y], 2)[:, :, :, None] - - # Calcualte Gaussian for every pixel of the kernel - ZZ = Z - MU - ZZ_t = ZZ.transpose(0, 1, 3, 2) - raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) - - # shift the kernel so it will be centered - # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) - - # Normalize the kernel and return - # kernel = raw_kernel_centered / np.sum(raw_kernel_centered) - kernel = raw_kernel / np.sum(raw_kernel) - return kernel - - -def fspecial_gaussian(hsize, sigma): - hsize = [hsize, hsize] - siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] - std = sigma - [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) - arg = -(x * x + y * y) / (2 * std * std) - h = np.exp(arg) - h[h < scipy.finfo(float).eps * h.max()] = 0 - sumh = h.sum() - if sumh != 0: - h = h / sumh - return h - - -def fspecial_laplacian(alpha): - alpha = max([0, min([alpha, 1])]) - h1 = alpha / (alpha + 1) - h2 = (1 - alpha) / (alpha + 1) - h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] - h = np.array(h) - return h - - -def fspecial(filter_type, *args, **kwargs): - ''' - python code from: - https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py - ''' - if filter_type == 'gaussian': - return fspecial_gaussian(*args, **kwargs) - if filter_type == 'laplacian': - return fspecial_laplacian(*args, **kwargs) - - -""" -# -------------------------------------------- -# degradation models -# -------------------------------------------- -""" - - -def bicubic_degradation(x, sf=3): - ''' - Args: - x: HxWxC image, [0, 1] - sf: down-scale factor - Return: - bicubicly downsampled LR image - ''' - x = util.imresize_np(x, scale=1 / sf) - return x - - -def srmd_degradation(x, k, sf=3): - ''' blur + bicubic downsampling - Args: - x: HxWxC image, [0, 1] - k: hxw, double - sf: down-scale factor - Return: - downsampled LR image - Reference: - @inproceedings{zhang2018learning, - title={Learning a single convolutional super-resolution network for multiple degradations}, - author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, - booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, - pages={3262--3271}, - year={2018} - } - ''' - x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror' - x = bicubic_degradation(x, sf=sf) - return x - - -def dpsr_degradation(x, k, sf=3): - ''' bicubic downsampling + blur - Args: - x: HxWxC image, [0, 1] - k: hxw, double - sf: down-scale factor - Return: - downsampled LR image - Reference: - @inproceedings{zhang2019deep, - title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, - author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, - booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, - pages={1671--1681}, - year={2019} - } - ''' - x = bicubic_degradation(x, sf=sf) - x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') - return x - - -def classical_degradation(x, k, sf=3): - ''' blur + downsampling - Args: - x: HxWxC image, [0, 1]/[0, 255] - k: hxw, double - sf: down-scale factor - Return: - downsampled LR image - ''' - x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') - # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) - st = 0 - return x[st::sf, st::sf, ...] - - -def add_sharpening(img, weight=0.5, radius=50, threshold=10): - """USM sharpening. borrowed from real-ESRGAN - Input image: I; Blurry image: B. - 1. K = I + weight * (I - B) - 2. Mask = 1 if abs(I - B) > threshold, else: 0 - 3. Blur mask: - 4. Out = Mask * K + (1 - Mask) * I - Args: - img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. - weight (float): Sharp weight. Default: 1. - radius (float): Kernel size of Gaussian blur. Default: 50. - threshold (int): - """ - if radius % 2 == 0: - radius += 1 - blur = cv2.GaussianBlur(img, (radius, radius), 0) - residual = img - blur - mask = np.abs(residual) * 255 > threshold - mask = mask.astype('float32') - soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) - - K = img + weight * residual - K = np.clip(K, 0, 1) - return soft_mask * K + (1 - soft_mask) * img - - -def add_blur(img, sf=4): - wd2 = 4.0 + sf - wd = 2.0 + 0.2 * sf - if random.random() < 0.5: - l1 = wd2 * random.random() - l2 = wd2 * random.random() - k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2) - else: - k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random()) - img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror') - - return img - - -def add_resize(img, sf=4): - rnum = np.random.rand() - if rnum > 0.8: # up - sf1 = random.uniform(1, 2) - elif rnum < 0.7: # down - sf1 = random.uniform(0.5 / sf, 1) - else: - sf1 = 1.0 - img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) - img = np.clip(img, 0.0, 1.0) - - return img - - -# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): -# noise_level = random.randint(noise_level1, noise_level2) -# rnum = np.random.rand() -# if rnum > 0.6: # add color Gaussian noise -# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) -# elif rnum < 0.4: # add grayscale Gaussian noise -# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) -# else: # add noise -# L = noise_level2 / 255. -# D = np.diag(np.random.rand(3)) -# U = orth(np.random.rand(3, 3)) -# conv = np.dot(np.dot(np.transpose(U), D), U) -# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) -# img = np.clip(img, 0.0, 1.0) -# return img - -def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): - noise_level = random.randint(noise_level1, noise_level2) - rnum = np.random.rand() - if rnum > 0.6: # add color Gaussian noise - img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) - elif rnum < 0.4: # add grayscale Gaussian noise - img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) - else: # add noise - L = noise_level2 / 255. - D = np.diag(np.random.rand(3)) - U = orth(np.random.rand(3, 3)) - conv = np.dot(np.dot(np.transpose(U), D), U) - img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) - img = np.clip(img, 0.0, 1.0) - return img - - -def add_speckle_noise(img, noise_level1=2, noise_level2=25): - noise_level = random.randint(noise_level1, noise_level2) - img = np.clip(img, 0.0, 1.0) - rnum = random.random() - if rnum > 0.6: - img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) - elif rnum < 0.4: - img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) - else: - L = noise_level2 / 255. - D = np.diag(np.random.rand(3)) - U = orth(np.random.rand(3, 3)) - conv = np.dot(np.dot(np.transpose(U), D), U) - img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) - img = np.clip(img, 0.0, 1.0) - return img - - -def add_Poisson_noise(img): - img = np.clip((img * 255.0).round(), 0, 255) / 255. - vals = 10 ** (2 * random.random() + 2.0) # [2, 4] - if random.random() < 0.5: - img = np.random.poisson(img * vals).astype(np.float32) / vals - else: - img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) - img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255. - noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray - img += noise_gray[:, :, np.newaxis] - img = np.clip(img, 0.0, 1.0) - return img - - -def add_JPEG_noise(img): - quality_factor = random.randint(30, 95) - img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) - result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]) - img = cv2.imdecode(encimg, 1) - img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) - return img - - -def random_crop(lq, hq, sf=4, lq_patchsize=64): - h, w = lq.shape[:2] - rnd_h = random.randint(0, h - lq_patchsize) - rnd_w = random.randint(0, w - lq_patchsize) - lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :] - - rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) - hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :] - return lq, hq - - -def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): - """ - This is the degradation model of BSRGAN from the paper - "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" - ---------- - img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) - sf: scale factor - isp_model: camera ISP model - Returns - ------- - img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] - hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] - """ - isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 - sf_ori = sf - - h1, w1 = img.shape[:2] - img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop - h, w = img.shape[:2] - - if h < lq_patchsize * sf or w < lq_patchsize * sf: - raise ValueError(f'img size ({h1}X{w1}) is too small!') - - hq = img.copy() - - if sf == 4 and random.random() < scale2_prob: # downsample1 - if np.random.rand() < 0.5: - img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), - interpolation=random.choice([1, 2, 3])) - else: - img = util.imresize_np(img, 1 / 2, True) - img = np.clip(img, 0.0, 1.0) - sf = 2 - - shuffle_order = random.sample(range(7), 7) - idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) - if idx1 > idx2: # keep downsample3 last - shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] - - for i in shuffle_order: - - if i == 0: - img = add_blur(img, sf=sf) - - elif i == 1: - img = add_blur(img, sf=sf) - - elif i == 2: - a, b = img.shape[1], img.shape[0] - # downsample2 - if random.random() < 0.75: - sf1 = random.uniform(1, 2 * sf) - img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), - interpolation=random.choice([1, 2, 3])) - else: - k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) - k_shifted = shift_pixel(k, sf) - k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel - img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror') - img = img[0::sf, 0::sf, ...] # nearest downsampling - img = np.clip(img, 0.0, 1.0) - - elif i == 3: - # downsample3 - img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) - img = np.clip(img, 0.0, 1.0) - - elif i == 4: - # add Gaussian noise - img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) - - elif i == 5: - # add JPEG noise - if random.random() < jpeg_prob: - img = add_JPEG_noise(img) - - elif i == 6: - # add processed camera sensor noise - if random.random() < isp_prob and isp_model is not None: - with torch.no_grad(): - img, hq = isp_model.forward(img.copy(), hq) - - # add final JPEG compression noise - img = add_JPEG_noise(img) - - # random crop - img, hq = random_crop(img, hq, sf_ori, lq_patchsize) - - return img, hq - - -# todo no isp_model? -def degradation_bsrgan_variant(image, sf=4, isp_model=None): - """ - This is the degradation model of BSRGAN from the paper - "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" - ---------- - sf: scale factor - isp_model: camera ISP model - Returns - ------- - img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] - hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] - """ - image = util.uint2single(image) - isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 - sf_ori = sf - - h1, w1 = image.shape[:2] - image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop - h, w = image.shape[:2] - - hq = image.copy() - - if sf == 4 and random.random() < scale2_prob: # downsample1 - if np.random.rand() < 0.5: - image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), - interpolation=random.choice([1, 2, 3])) - else: - image = util.imresize_np(image, 1 / 2, True) - image = np.clip(image, 0.0, 1.0) - sf = 2 - - shuffle_order = random.sample(range(7), 7) - idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) - if idx1 > idx2: # keep downsample3 last - shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] - - for i in shuffle_order: - - if i == 0: - image = add_blur(image, sf=sf) - - elif i == 1: - image = add_blur(image, sf=sf) - - elif i == 2: - a, b = image.shape[1], image.shape[0] - # downsample2 - if random.random() < 0.75: - sf1 = random.uniform(1, 2 * sf) - image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), - interpolation=random.choice([1, 2, 3])) - else: - k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) - k_shifted = shift_pixel(k, sf) - k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel - image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror') - image = image[0::sf, 0::sf, ...] # nearest downsampling - image = np.clip(image, 0.0, 1.0) - - elif i == 3: - # downsample3 - image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) - image = np.clip(image, 0.0, 1.0) - - elif i == 4: - # add Gaussian noise - image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25) - - elif i == 5: - # add JPEG noise - if random.random() < jpeg_prob: - image = add_JPEG_noise(image) - - # elif i == 6: - # # add processed camera sensor noise - # if random.random() < isp_prob and isp_model is not None: - # with torch.no_grad(): - # img, hq = isp_model.forward(img.copy(), hq) - - # add final JPEG compression noise - image = add_JPEG_noise(image) - image = util.single2uint(image) - example = {"image":image} - return example - - -# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc... -def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None): - """ - This is an extended degradation model by combining - the degradation models of BSRGAN and Real-ESRGAN - ---------- - img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) - sf: scale factor - use_shuffle: the degradation shuffle - use_sharp: sharpening the img - Returns - ------- - img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] - hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] - """ - - h1, w1 = img.shape[:2] - img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop - h, w = img.shape[:2] - - if h < lq_patchsize * sf or w < lq_patchsize * sf: - raise ValueError(f'img size ({h1}X{w1}) is too small!') - - if use_sharp: - img = add_sharpening(img) - hq = img.copy() - - if random.random() < shuffle_prob: - shuffle_order = random.sample(range(13), 13) - else: - shuffle_order = list(range(13)) - # local shuffle for noise, JPEG is always the last one - shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6))) - shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13))) - - poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1 - - for i in shuffle_order: - if i == 0: - img = add_blur(img, sf=sf) - elif i == 1: - img = add_resize(img, sf=sf) - elif i == 2: - img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) - elif i == 3: - if random.random() < poisson_prob: - img = add_Poisson_noise(img) - elif i == 4: - if random.random() < speckle_prob: - img = add_speckle_noise(img) - elif i == 5: - if random.random() < isp_prob and isp_model is not None: - with torch.no_grad(): - img, hq = isp_model.forward(img.copy(), hq) - elif i == 6: - img = add_JPEG_noise(img) - elif i == 7: - img = add_blur(img, sf=sf) - elif i == 8: - img = add_resize(img, sf=sf) - elif i == 9: - img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) - elif i == 10: - if random.random() < poisson_prob: - img = add_Poisson_noise(img) - elif i == 11: - if random.random() < speckle_prob: - img = add_speckle_noise(img) - elif i == 12: - if random.random() < isp_prob and isp_model is not None: - with torch.no_grad(): - img, hq = isp_model.forward(img.copy(), hq) - else: - print('check the shuffle!') - - # resize to desired size - img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])), - interpolation=random.choice([1, 2, 3])) - - # add final JPEG compression noise - img = add_JPEG_noise(img) - - # random crop - img, hq = random_crop(img, hq, sf, lq_patchsize) - - return img, hq - - -if __name__ == '__main__': - print("hey") - img = util.imread_uint('utils/test.png', 3) - print(img) - img = util.uint2single(img) - print(img) - img = img[:448, :448] - h = img.shape[0] // 4 - print("resizing to", h) - sf = 4 - deg_fn = partial(degradation_bsrgan_variant, sf=sf) - for i in range(20): - print(i) - img_lq = deg_fn(img) - print(img_lq) - img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"] - print(img_lq.shape) - print("bicubic", img_lq_bicubic.shape) - print(img_hq.shape) - lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), - interpolation=0) - lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), - interpolation=0) - img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1) - util.imsave(img_concat, str(i) + '.png') - - diff --git a/ldm/modules/image_degradation/bsrgan_light.py b/ldm/modules/image_degradation/bsrgan_light.py deleted file mode 100644 index 808c7f882cb75e2ba2340d5b55881d11927351f0..0000000000000000000000000000000000000000 --- a/ldm/modules/image_degradation/bsrgan_light.py +++ /dev/null @@ -1,651 +0,0 @@ -# -*- coding: utf-8 -*- -import numpy as np -import cv2 -import torch - -from functools import partial -import random -from scipy import ndimage -import scipy -import scipy.stats as ss -from scipy.interpolate import interp2d -from scipy.linalg import orth -import albumentations - -import ldm.modules.image_degradation.utils_image as util - -""" -# -------------------------------------------- -# Super-Resolution -# -------------------------------------------- -# -# Kai Zhang (cskaizhang@gmail.com) -# https://github.com/cszn -# From 2019/03--2021/08 -# -------------------------------------------- -""" - -def modcrop_np(img, sf): - ''' - Args: - img: numpy image, WxH or WxHxC - sf: scale factor - Return: - cropped image - ''' - w, h = img.shape[:2] - im = np.copy(img) - return im[:w - w % sf, :h - h % sf, ...] - - -""" -# -------------------------------------------- -# anisotropic Gaussian kernels -# -------------------------------------------- -""" - - -def analytic_kernel(k): - """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" - k_size = k.shape[0] - # Calculate the big kernels size - big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) - # Loop over the small kernel to fill the big one - for r in range(k_size): - for c in range(k_size): - big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k - # Crop the edges of the big kernel to ignore very small values and increase run time of SR - crop = k_size // 2 - cropped_big_k = big_k[crop:-crop, crop:-crop] - # Normalize to 1 - return cropped_big_k / cropped_big_k.sum() - - -def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): - """ generate an anisotropic Gaussian kernel - Args: - ksize : e.g., 15, kernel size - theta : [0, pi], rotation angle range - l1 : [0.1,50], scaling of eigenvalues - l2 : [0.1,l1], scaling of eigenvalues - If l1 = l2, will get an isotropic Gaussian kernel. - Returns: - k : kernel - """ - - v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.])) - V = np.array([[v[0], v[1]], [v[1], -v[0]]]) - D = np.array([[l1, 0], [0, l2]]) - Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) - k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) - - return k - - -def gm_blur_kernel(mean, cov, size=15): - center = size / 2.0 + 0.5 - k = np.zeros([size, size]) - for y in range(size): - for x in range(size): - cy = y - center + 1 - cx = x - center + 1 - k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) - - k = k / np.sum(k) - return k - - -def shift_pixel(x, sf, upper_left=True): - """shift pixel for super-resolution with different scale factors - Args: - x: WxHxC or WxH - sf: scale factor - upper_left: shift direction - """ - h, w = x.shape[:2] - shift = (sf - 1) * 0.5 - xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) - if upper_left: - x1 = xv + shift - y1 = yv + shift - else: - x1 = xv - shift - y1 = yv - shift - - x1 = np.clip(x1, 0, w - 1) - y1 = np.clip(y1, 0, h - 1) - - if x.ndim == 2: - x = interp2d(xv, yv, x)(x1, y1) - if x.ndim == 3: - for i in range(x.shape[-1]): - x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) - - return x - - -def blur(x, k): - ''' - x: image, NxcxHxW - k: kernel, Nx1xhxw - ''' - n, c = x.shape[:2] - p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 - x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate') - k = k.repeat(1, c, 1, 1) - k = k.view(-1, 1, k.shape[2], k.shape[3]) - x = x.view(1, -1, x.shape[2], x.shape[3]) - x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) - x = x.view(n, c, x.shape[2], x.shape[3]) - - return x - - -def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0): - """" - # modified version of https://github.com/assafshocher/BlindSR_dataset_generator - # Kai Zhang - # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var - # max_var = 2.5 * sf - """ - # Set random eigen-vals (lambdas) and angle (theta) for COV matrix - lambda_1 = min_var + np.random.rand() * (max_var - min_var) - lambda_2 = min_var + np.random.rand() * (max_var - min_var) - theta = np.random.rand() * np.pi # random theta - noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 - - # Set COV matrix using Lambdas and Theta - LAMBDA = np.diag([lambda_1, lambda_2]) - Q = np.array([[np.cos(theta), -np.sin(theta)], - [np.sin(theta), np.cos(theta)]]) - SIGMA = Q @ LAMBDA @ Q.T - INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] - - # Set expectation position (shifting kernel for aligned image) - MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) - MU = MU[None, None, :, None] - - # Create meshgrid for Gaussian - [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) - Z = np.stack([X, Y], 2)[:, :, :, None] - - # Calcualte Gaussian for every pixel of the kernel - ZZ = Z - MU - ZZ_t = ZZ.transpose(0, 1, 3, 2) - raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) - - # shift the kernel so it will be centered - # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) - - # Normalize the kernel and return - # kernel = raw_kernel_centered / np.sum(raw_kernel_centered) - kernel = raw_kernel / np.sum(raw_kernel) - return kernel - - -def fspecial_gaussian(hsize, sigma): - hsize = [hsize, hsize] - siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] - std = sigma - [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) - arg = -(x * x + y * y) / (2 * std * std) - h = np.exp(arg) - h[h < scipy.finfo(float).eps * h.max()] = 0 - sumh = h.sum() - if sumh != 0: - h = h / sumh - return h - - -def fspecial_laplacian(alpha): - alpha = max([0, min([alpha, 1])]) - h1 = alpha / (alpha + 1) - h2 = (1 - alpha) / (alpha + 1) - h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] - h = np.array(h) - return h - - -def fspecial(filter_type, *args, **kwargs): - ''' - python code from: - https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py - ''' - if filter_type == 'gaussian': - return fspecial_gaussian(*args, **kwargs) - if filter_type == 'laplacian': - return fspecial_laplacian(*args, **kwargs) - - -""" -# -------------------------------------------- -# degradation models -# -------------------------------------------- -""" - - -def bicubic_degradation(x, sf=3): - ''' - Args: - x: HxWxC image, [0, 1] - sf: down-scale factor - Return: - bicubicly downsampled LR image - ''' - x = util.imresize_np(x, scale=1 / sf) - return x - - -def srmd_degradation(x, k, sf=3): - ''' blur + bicubic downsampling - Args: - x: HxWxC image, [0, 1] - k: hxw, double - sf: down-scale factor - Return: - downsampled LR image - Reference: - @inproceedings{zhang2018learning, - title={Learning a single convolutional super-resolution network for multiple degradations}, - author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, - booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, - pages={3262--3271}, - year={2018} - } - ''' - x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror' - x = bicubic_degradation(x, sf=sf) - return x - - -def dpsr_degradation(x, k, sf=3): - ''' bicubic downsampling + blur - Args: - x: HxWxC image, [0, 1] - k: hxw, double - sf: down-scale factor - Return: - downsampled LR image - Reference: - @inproceedings{zhang2019deep, - title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, - author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, - booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, - pages={1671--1681}, - year={2019} - } - ''' - x = bicubic_degradation(x, sf=sf) - x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap') - return x - - -def classical_degradation(x, k, sf=3): - ''' blur + downsampling - Args: - x: HxWxC image, [0, 1]/[0, 255] - k: hxw, double - sf: down-scale factor - Return: - downsampled LR image - ''' - x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap') - # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) - st = 0 - return x[st::sf, st::sf, ...] - - -def add_sharpening(img, weight=0.5, radius=50, threshold=10): - """USM sharpening. borrowed from real-ESRGAN - Input image: I; Blurry image: B. - 1. K = I + weight * (I - B) - 2. Mask = 1 if abs(I - B) > threshold, else: 0 - 3. Blur mask: - 4. Out = Mask * K + (1 - Mask) * I - Args: - img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. - weight (float): Sharp weight. Default: 1. - radius (float): Kernel size of Gaussian blur. Default: 50. - threshold (int): - """ - if radius % 2 == 0: - radius += 1 - blur = cv2.GaussianBlur(img, (radius, radius), 0) - residual = img - blur - mask = np.abs(residual) * 255 > threshold - mask = mask.astype('float32') - soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) - - K = img + weight * residual - K = np.clip(K, 0, 1) - return soft_mask * K + (1 - soft_mask) * img - - -def add_blur(img, sf=4): - wd2 = 4.0 + sf - wd = 2.0 + 0.2 * sf - - wd2 = wd2/4 - wd = wd/4 - - if random.random() < 0.5: - l1 = wd2 * random.random() - l2 = wd2 * random.random() - k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2) - else: - k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random()) - img = ndimage.convolve(img, np.expand_dims(k, axis=2), mode='mirror') - - return img - - -def add_resize(img, sf=4): - rnum = np.random.rand() - if rnum > 0.8: # up - sf1 = random.uniform(1, 2) - elif rnum < 0.7: # down - sf1 = random.uniform(0.5 / sf, 1) - else: - sf1 = 1.0 - img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) - img = np.clip(img, 0.0, 1.0) - - return img - - -# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): -# noise_level = random.randint(noise_level1, noise_level2) -# rnum = np.random.rand() -# if rnum > 0.6: # add color Gaussian noise -# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) -# elif rnum < 0.4: # add grayscale Gaussian noise -# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) -# else: # add noise -# L = noise_level2 / 255. -# D = np.diag(np.random.rand(3)) -# U = orth(np.random.rand(3, 3)) -# conv = np.dot(np.dot(np.transpose(U), D), U) -# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) -# img = np.clip(img, 0.0, 1.0) -# return img - -def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): - noise_level = random.randint(noise_level1, noise_level2) - rnum = np.random.rand() - if rnum > 0.6: # add color Gaussian noise - img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) - elif rnum < 0.4: # add grayscale Gaussian noise - img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) - else: # add noise - L = noise_level2 / 255. - D = np.diag(np.random.rand(3)) - U = orth(np.random.rand(3, 3)) - conv = np.dot(np.dot(np.transpose(U), D), U) - img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) - img = np.clip(img, 0.0, 1.0) - return img - - -def add_speckle_noise(img, noise_level1=2, noise_level2=25): - noise_level = random.randint(noise_level1, noise_level2) - img = np.clip(img, 0.0, 1.0) - rnum = random.random() - if rnum > 0.6: - img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) - elif rnum < 0.4: - img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) - else: - L = noise_level2 / 255. - D = np.diag(np.random.rand(3)) - U = orth(np.random.rand(3, 3)) - conv = np.dot(np.dot(np.transpose(U), D), U) - img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) - img = np.clip(img, 0.0, 1.0) - return img - - -def add_Poisson_noise(img): - img = np.clip((img * 255.0).round(), 0, 255) / 255. - vals = 10 ** (2 * random.random() + 2.0) # [2, 4] - if random.random() < 0.5: - img = np.random.poisson(img * vals).astype(np.float32) / vals - else: - img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) - img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255. - noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray - img += noise_gray[:, :, np.newaxis] - img = np.clip(img, 0.0, 1.0) - return img - - -def add_JPEG_noise(img): - quality_factor = random.randint(80, 95) - img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) - result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]) - img = cv2.imdecode(encimg, 1) - img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) - return img - - -def random_crop(lq, hq, sf=4, lq_patchsize=64): - h, w = lq.shape[:2] - rnd_h = random.randint(0, h - lq_patchsize) - rnd_w = random.randint(0, w - lq_patchsize) - lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :] - - rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) - hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :] - return lq, hq - - -def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): - """ - This is the degradation model of BSRGAN from the paper - "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" - ---------- - img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) - sf: scale factor - isp_model: camera ISP model - Returns - ------- - img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] - hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] - """ - isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 - sf_ori = sf - - h1, w1 = img.shape[:2] - img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop - h, w = img.shape[:2] - - if h < lq_patchsize * sf or w < lq_patchsize * sf: - raise ValueError(f'img size ({h1}X{w1}) is too small!') - - hq = img.copy() - - if sf == 4 and random.random() < scale2_prob: # downsample1 - if np.random.rand() < 0.5: - img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), - interpolation=random.choice([1, 2, 3])) - else: - img = util.imresize_np(img, 1 / 2, True) - img = np.clip(img, 0.0, 1.0) - sf = 2 - - shuffle_order = random.sample(range(7), 7) - idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) - if idx1 > idx2: # keep downsample3 last - shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] - - for i in shuffle_order: - - if i == 0: - img = add_blur(img, sf=sf) - - elif i == 1: - img = add_blur(img, sf=sf) - - elif i == 2: - a, b = img.shape[1], img.shape[0] - # downsample2 - if random.random() < 0.75: - sf1 = random.uniform(1, 2 * sf) - img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), - interpolation=random.choice([1, 2, 3])) - else: - k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) - k_shifted = shift_pixel(k, sf) - k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel - img = ndimage.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror') - img = img[0::sf, 0::sf, ...] # nearest downsampling - img = np.clip(img, 0.0, 1.0) - - elif i == 3: - # downsample3 - img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) - img = np.clip(img, 0.0, 1.0) - - elif i == 4: - # add Gaussian noise - img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8) - - elif i == 5: - # add JPEG noise - if random.random() < jpeg_prob: - img = add_JPEG_noise(img) - - elif i == 6: - # add processed camera sensor noise - if random.random() < isp_prob and isp_model is not None: - with torch.no_grad(): - img, hq = isp_model.forward(img.copy(), hq) - - # add final JPEG compression noise - img = add_JPEG_noise(img) - - # random crop - img, hq = random_crop(img, hq, sf_ori, lq_patchsize) - - return img, hq - - -# todo no isp_model? -def degradation_bsrgan_variant(image, sf=4, isp_model=None, up=False): - """ - This is the degradation model of BSRGAN from the paper - "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" - ---------- - sf: scale factor - isp_model: camera ISP model - Returns - ------- - img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] - hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] - """ - image = util.uint2single(image) - isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 - sf_ori = sf - - h1, w1 = image.shape[:2] - image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop - h, w = image.shape[:2] - - hq = image.copy() - - if sf == 4 and random.random() < scale2_prob: # downsample1 - if np.random.rand() < 0.5: - image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), - interpolation=random.choice([1, 2, 3])) - else: - image = util.imresize_np(image, 1 / 2, True) - image = np.clip(image, 0.0, 1.0) - sf = 2 - - shuffle_order = random.sample(range(7), 7) - idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) - if idx1 > idx2: # keep downsample3 last - shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] - - for i in shuffle_order: - - if i == 0: - image = add_blur(image, sf=sf) - - # elif i == 1: - # image = add_blur(image, sf=sf) - - if i == 0: - pass - - elif i == 2: - a, b = image.shape[1], image.shape[0] - # downsample2 - if random.random() < 0.8: - sf1 = random.uniform(1, 2 * sf) - image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), - interpolation=random.choice([1, 2, 3])) - else: - k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) - k_shifted = shift_pixel(k, sf) - k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel - image = ndimage.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror') - image = image[0::sf, 0::sf, ...] # nearest downsampling - - image = np.clip(image, 0.0, 1.0) - - elif i == 3: - # downsample3 - image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) - image = np.clip(image, 0.0, 1.0) - - elif i == 4: - # add Gaussian noise - image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2) - - elif i == 5: - # add JPEG noise - if random.random() < jpeg_prob: - image = add_JPEG_noise(image) - # - # elif i == 6: - # # add processed camera sensor noise - # if random.random() < isp_prob and isp_model is not None: - # with torch.no_grad(): - # img, hq = isp_model.forward(img.copy(), hq) - - # add final JPEG compression noise - image = add_JPEG_noise(image) - image = util.single2uint(image) - if up: - image = cv2.resize(image, (w1, h1), interpolation=cv2.INTER_CUBIC) # todo: random, as above? want to condition on it then - example = {"image": image} - return example - - - - -if __name__ == '__main__': - print("hey") - img = util.imread_uint('utils/test.png', 3) - img = img[:448, :448] - h = img.shape[0] // 4 - print("resizing to", h) - sf = 4 - deg_fn = partial(degradation_bsrgan_variant, sf=sf) - for i in range(20): - print(i) - img_hq = img - img_lq = deg_fn(img)["image"] - img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq) - print(img_lq) - img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"] - print(img_lq.shape) - print("bicubic", img_lq_bicubic.shape) - print(img_hq.shape) - lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), - interpolation=0) - lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), - (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), - interpolation=0) - img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1) - util.imsave(img_concat, str(i) + '.png') diff --git a/ldm/modules/image_degradation/utils/test.png b/ldm/modules/image_degradation/utils/test.png deleted file mode 100644 index 4249b43de0f22707758d13c240268a401642f6e6..0000000000000000000000000000000000000000 Binary files a/ldm/modules/image_degradation/utils/test.png and /dev/null differ diff --git a/ldm/modules/image_degradation/utils_image.py b/ldm/modules/image_degradation/utils_image.py deleted file mode 100644 index 0175f155ad900ae33c3c46ed87f49b352e3faf98..0000000000000000000000000000000000000000 --- a/ldm/modules/image_degradation/utils_image.py +++ /dev/null @@ -1,916 +0,0 @@ -import os -import math -import random -import numpy as np -import torch -import cv2 -from torchvision.utils import make_grid -from datetime import datetime -#import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py - - -os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" - - -''' -# -------------------------------------------- -# Kai Zhang (github: https://github.com/cszn) -# 03/Mar/2019 -# -------------------------------------------- -# https://github.com/twhui/SRGAN-pyTorch -# https://github.com/xinntao/BasicSR -# -------------------------------------------- -''' - - -IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif'] - - -def is_image_file(filename): - return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) - - -def get_timestamp(): - return datetime.now().strftime('%y%m%d-%H%M%S') - - -def imshow(x, title=None, cbar=False, figsize=None): - plt.figure(figsize=figsize) - plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray') - if title: - plt.title(title) - if cbar: - plt.colorbar() - plt.show() - - -def surf(Z, cmap='rainbow', figsize=None): - plt.figure(figsize=figsize) - ax3 = plt.axes(projection='3d') - - w, h = Z.shape[:2] - xx = np.arange(0,w,1) - yy = np.arange(0,h,1) - X, Y = np.meshgrid(xx, yy) - ax3.plot_surface(X,Y,Z,cmap=cmap) - #ax3.contour(X,Y,Z, zdim='z',offset=-2๏ผŒcmap=cmap) - plt.show() - - -''' -# -------------------------------------------- -# get image pathes -# -------------------------------------------- -''' - - -def get_image_paths(dataroot): - paths = None # return None if dataroot is None - if dataroot is not None: - paths = sorted(_get_paths_from_images(dataroot)) - return paths - - -def _get_paths_from_images(path): - assert os.path.isdir(path), '{:s} is not a valid directory'.format(path) - images = [] - for dirpath, _, fnames in sorted(os.walk(path)): - for fname in sorted(fnames): - if is_image_file(fname): - img_path = os.path.join(dirpath, fname) - images.append(img_path) - assert images, '{:s} has no valid image file'.format(path) - return images - - -''' -# -------------------------------------------- -# split large images into small images -# -------------------------------------------- -''' - - -def patches_from_image(img, p_size=512, p_overlap=64, p_max=800): - w, h = img.shape[:2] - patches = [] - if w > p_max and h > p_max: - w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int)) - h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int)) - w1.append(w-p_size) - h1.append(h-p_size) -# print(w1) -# print(h1) - for i in w1: - for j in h1: - patches.append(img[i:i+p_size, j:j+p_size,:]) - else: - patches.append(img) - - return patches - - -def imssave(imgs, img_path): - """ - imgs: list, N images of size WxHxC - """ - img_name, ext = os.path.splitext(os.path.basename(img_path)) - - for i, img in enumerate(imgs): - if img.ndim == 3: - img = img[:, :, [2, 1, 0]] - new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png') - cv2.imwrite(new_path, img) - - -def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000): - """ - split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size), - and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max) - will be splitted. - Args: - original_dataroot: - taget_dataroot: - p_size: size of small images - p_overlap: patch size in training is a good choice - p_max: images with smaller size than (p_max)x(p_max) keep unchanged. - """ - paths = get_image_paths(original_dataroot) - for img_path in paths: - # img_name, ext = os.path.splitext(os.path.basename(img_path)) - img = imread_uint(img_path, n_channels=n_channels) - patches = patches_from_image(img, p_size, p_overlap, p_max) - imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path))) - #if original_dataroot == taget_dataroot: - #del img_path - -''' -# -------------------------------------------- -# makedir -# -------------------------------------------- -''' - - -def mkdir(path): - if not os.path.exists(path): - os.makedirs(path) - - -def mkdirs(paths): - if isinstance(paths, str): - mkdir(paths) - else: - for path in paths: - mkdir(path) - - -def mkdir_and_rename(path): - if os.path.exists(path): - new_name = path + '_archived_' + get_timestamp() - print('Path already exists. Rename it to [{:s}]'.format(new_name)) - os.rename(path, new_name) - os.makedirs(path) - - -''' -# -------------------------------------------- -# read image from path -# opencv is fast, but read BGR numpy image -# -------------------------------------------- -''' - - -# -------------------------------------------- -# get uint8 image of size HxWxn_channles (RGB) -# -------------------------------------------- -def imread_uint(path, n_channels=3): - # input: path - # output: HxWx3(RGB or GGG), or HxWx1 (G) - if n_channels == 1: - img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE - img = np.expand_dims(img, axis=2) # HxWx1 - elif n_channels == 3: - img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G - if img.ndim == 2: - img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG - else: - img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB - return img - - -# -------------------------------------------- -# matlab's imwrite -# -------------------------------------------- -def imsave(img, img_path): - img = np.squeeze(img) - if img.ndim == 3: - img = img[:, :, [2, 1, 0]] - cv2.imwrite(img_path, img) - -def imwrite(img, img_path): - img = np.squeeze(img) - if img.ndim == 3: - img = img[:, :, [2, 1, 0]] - cv2.imwrite(img_path, img) - - - -# -------------------------------------------- -# get single image of size HxWxn_channles (BGR) -# -------------------------------------------- -def read_img(path): - # read image by cv2 - # return: Numpy float32, HWC, BGR, [0,1] - img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE - img = img.astype(np.float32) / 255. - if img.ndim == 2: - img = np.expand_dims(img, axis=2) - # some images have 4 channels - if img.shape[2] > 3: - img = img[:, :, :3] - return img - - -''' -# -------------------------------------------- -# image format conversion -# -------------------------------------------- -# numpy(single) <---> numpy(unit) -# numpy(single) <---> tensor -# numpy(unit) <---> tensor -# -------------------------------------------- -''' - - -# -------------------------------------------- -# numpy(single) [0, 1] <---> numpy(unit) -# -------------------------------------------- - - -def uint2single(img): - - return np.float32(img/255.) - - -def single2uint(img): - - return np.uint8((img.clip(0, 1)*255.).round()) - - -def uint162single(img): - - return np.float32(img/65535.) - - -def single2uint16(img): - - return np.uint16((img.clip(0, 1)*65535.).round()) - - -# -------------------------------------------- -# numpy(unit) (HxWxC or HxW) <---> tensor -# -------------------------------------------- - - -# convert uint to 4-dimensional torch tensor -def uint2tensor4(img): - if img.ndim == 2: - img = np.expand_dims(img, axis=2) - return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0) - - -# convert uint to 3-dimensional torch tensor -def uint2tensor3(img): - if img.ndim == 2: - img = np.expand_dims(img, axis=2) - return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.) - - -# convert 2/3/4-dimensional torch tensor to uint -def tensor2uint(img): - img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy() - if img.ndim == 3: - img = np.transpose(img, (1, 2, 0)) - return np.uint8((img*255.0).round()) - - -# -------------------------------------------- -# numpy(single) (HxWxC) <---> tensor -# -------------------------------------------- - - -# convert single (HxWxC) to 3-dimensional torch tensor -def single2tensor3(img): - return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float() - - -# convert single (HxWxC) to 4-dimensional torch tensor -def single2tensor4(img): - return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0) - - -# convert torch tensor to single -def tensor2single(img): - img = img.data.squeeze().float().cpu().numpy() - if img.ndim == 3: - img = np.transpose(img, (1, 2, 0)) - - return img - -# convert torch tensor to single -def tensor2single3(img): - img = img.data.squeeze().float().cpu().numpy() - if img.ndim == 3: - img = np.transpose(img, (1, 2, 0)) - elif img.ndim == 2: - img = np.expand_dims(img, axis=2) - return img - - -def single2tensor5(img): - return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0) - - -def single32tensor5(img): - return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0) - - -def single42tensor4(img): - return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float() - - -# from skimage.io import imread, imsave -def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)): - ''' - Converts a torch Tensor into an image Numpy array of BGR channel order - Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order - Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default) - ''' - tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp - tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1] - n_dim = tensor.dim() - if n_dim == 4: - n_img = len(tensor) - img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy() - img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR - elif n_dim == 3: - img_np = tensor.numpy() - img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR - elif n_dim == 2: - img_np = tensor.numpy() - else: - raise TypeError( - 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim)) - if out_type == np.uint8: - img_np = (img_np * 255.0).round() - # Important. Unlike matlab, numpy.unit8() WILL NOT round by default. - return img_np.astype(out_type) - - -''' -# -------------------------------------------- -# Augmentation, flipe and/or rotate -# -------------------------------------------- -# The following two are enough. -# (1) augmet_img: numpy image of WxHxC or WxH -# (2) augment_img_tensor4: tensor image 1xCxWxH -# -------------------------------------------- -''' - - -def augment_img(img, mode=0): - '''Kai Zhang (github: https://github.com/cszn) - ''' - if mode == 0: - return img - elif mode == 1: - return np.flipud(np.rot90(img)) - elif mode == 2: - return np.flipud(img) - elif mode == 3: - return np.rot90(img, k=3) - elif mode == 4: - return np.flipud(np.rot90(img, k=2)) - elif mode == 5: - return np.rot90(img) - elif mode == 6: - return np.rot90(img, k=2) - elif mode == 7: - return np.flipud(np.rot90(img, k=3)) - - -def augment_img_tensor4(img, mode=0): - '''Kai Zhang (github: https://github.com/cszn) - ''' - if mode == 0: - return img - elif mode == 1: - return img.rot90(1, [2, 3]).flip([2]) - elif mode == 2: - return img.flip([2]) - elif mode == 3: - return img.rot90(3, [2, 3]) - elif mode == 4: - return img.rot90(2, [2, 3]).flip([2]) - elif mode == 5: - return img.rot90(1, [2, 3]) - elif mode == 6: - return img.rot90(2, [2, 3]) - elif mode == 7: - return img.rot90(3, [2, 3]).flip([2]) - - -def augment_img_tensor(img, mode=0): - '''Kai Zhang (github: https://github.com/cszn) - ''' - img_size = img.size() - img_np = img.data.cpu().numpy() - if len(img_size) == 3: - img_np = np.transpose(img_np, (1, 2, 0)) - elif len(img_size) == 4: - img_np = np.transpose(img_np, (2, 3, 1, 0)) - img_np = augment_img(img_np, mode=mode) - img_tensor = torch.from_numpy(np.ascontiguousarray(img_np)) - if len(img_size) == 3: - img_tensor = img_tensor.permute(2, 0, 1) - elif len(img_size) == 4: - img_tensor = img_tensor.permute(3, 2, 0, 1) - - return img_tensor.type_as(img) - - -def augment_img_np3(img, mode=0): - if mode == 0: - return img - elif mode == 1: - return img.transpose(1, 0, 2) - elif mode == 2: - return img[::-1, :, :] - elif mode == 3: - img = img[::-1, :, :] - img = img.transpose(1, 0, 2) - return img - elif mode == 4: - return img[:, ::-1, :] - elif mode == 5: - img = img[:, ::-1, :] - img = img.transpose(1, 0, 2) - return img - elif mode == 6: - img = img[:, ::-1, :] - img = img[::-1, :, :] - return img - elif mode == 7: - img = img[:, ::-1, :] - img = img[::-1, :, :] - img = img.transpose(1, 0, 2) - return img - - -def augment_imgs(img_list, hflip=True, rot=True): - # horizontal flip OR rotate - hflip = hflip and random.random() < 0.5 - vflip = rot and random.random() < 0.5 - rot90 = rot and random.random() < 0.5 - - def _augment(img): - if hflip: - img = img[:, ::-1, :] - if vflip: - img = img[::-1, :, :] - if rot90: - img = img.transpose(1, 0, 2) - return img - - return [_augment(img) for img in img_list] - - -''' -# -------------------------------------------- -# modcrop and shave -# -------------------------------------------- -''' - - -def modcrop(img_in, scale): - # img_in: Numpy, HWC or HW - img = np.copy(img_in) - if img.ndim == 2: - H, W = img.shape - H_r, W_r = H % scale, W % scale - img = img[:H - H_r, :W - W_r] - elif img.ndim == 3: - H, W, C = img.shape - H_r, W_r = H % scale, W % scale - img = img[:H - H_r, :W - W_r, :] - else: - raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim)) - return img - - -def shave(img_in, border=0): - # img_in: Numpy, HWC or HW - img = np.copy(img_in) - h, w = img.shape[:2] - img = img[border:h-border, border:w-border] - return img - - -''' -# -------------------------------------------- -# image processing process on numpy image -# channel_convert(in_c, tar_type, img_list): -# rgb2ycbcr(img, only_y=True): -# bgr2ycbcr(img, only_y=True): -# ycbcr2rgb(img): -# -------------------------------------------- -''' - - -def rgb2ycbcr(img, only_y=True): - '''same as matlab rgb2ycbcr - only_y: only return Y channel - Input: - uint8, [0, 255] - float, [0, 1] - ''' - in_img_type = img.dtype - img.astype(np.float32) - if in_img_type != np.uint8: - img *= 255. - # convert - if only_y: - rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0 - else: - rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], - [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128] - if in_img_type == np.uint8: - rlt = rlt.round() - else: - rlt /= 255. - return rlt.astype(in_img_type) - - -def ycbcr2rgb(img): - '''same as matlab ycbcr2rgb - Input: - uint8, [0, 255] - float, [0, 1] - ''' - in_img_type = img.dtype - img.astype(np.float32) - if in_img_type != np.uint8: - img *= 255. - # convert - rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071], - [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836] - if in_img_type == np.uint8: - rlt = rlt.round() - else: - rlt /= 255. - return rlt.astype(in_img_type) - - -def bgr2ycbcr(img, only_y=True): - '''bgr version of rgb2ycbcr - only_y: only return Y channel - Input: - uint8, [0, 255] - float, [0, 1] - ''' - in_img_type = img.dtype - img.astype(np.float32) - if in_img_type != np.uint8: - img *= 255. - # convert - if only_y: - rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0 - else: - rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], - [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128] - if in_img_type == np.uint8: - rlt = rlt.round() - else: - rlt /= 255. - return rlt.astype(in_img_type) - - -def channel_convert(in_c, tar_type, img_list): - # conversion among BGR, gray and y - if in_c == 3 and tar_type == 'gray': # BGR to gray - gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list] - return [np.expand_dims(img, axis=2) for img in gray_list] - elif in_c == 3 and tar_type == 'y': # BGR to y - y_list = [bgr2ycbcr(img, only_y=True) for img in img_list] - return [np.expand_dims(img, axis=2) for img in y_list] - elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR - return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list] - else: - return img_list - - -''' -# -------------------------------------------- -# metric, PSNR and SSIM -# -------------------------------------------- -''' - - -# -------------------------------------------- -# PSNR -# -------------------------------------------- -def calculate_psnr(img1, img2, border=0): - # img1 and img2 have range [0, 255] - #img1 = img1.squeeze() - #img2 = img2.squeeze() - if not img1.shape == img2.shape: - raise ValueError('Input images must have the same dimensions.') - h, w = img1.shape[:2] - img1 = img1[border:h-border, border:w-border] - img2 = img2[border:h-border, border:w-border] - - img1 = img1.astype(np.float64) - img2 = img2.astype(np.float64) - mse = np.mean((img1 - img2)**2) - if mse == 0: - return float('inf') - return 20 * math.log10(255.0 / math.sqrt(mse)) - - -# -------------------------------------------- -# SSIM -# -------------------------------------------- -def calculate_ssim(img1, img2, border=0): - '''calculate SSIM - the same outputs as MATLAB's - img1, img2: [0, 255] - ''' - #img1 = img1.squeeze() - #img2 = img2.squeeze() - if not img1.shape == img2.shape: - raise ValueError('Input images must have the same dimensions.') - h, w = img1.shape[:2] - img1 = img1[border:h-border, border:w-border] - img2 = img2[border:h-border, border:w-border] - - if img1.ndim == 2: - return ssim(img1, img2) - elif img1.ndim == 3: - if img1.shape[2] == 3: - ssims = [] - for i in range(3): - ssims.append(ssim(img1[:,:,i], img2[:,:,i])) - return np.array(ssims).mean() - elif img1.shape[2] == 1: - return ssim(np.squeeze(img1), np.squeeze(img2)) - else: - raise ValueError('Wrong input image dimensions.') - - -def ssim(img1, img2): - C1 = (0.01 * 255)**2 - C2 = (0.03 * 255)**2 - - img1 = img1.astype(np.float64) - img2 = img2.astype(np.float64) - kernel = cv2.getGaussianKernel(11, 1.5) - window = np.outer(kernel, kernel.transpose()) - - mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid - mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] - mu1_sq = mu1**2 - mu2_sq = mu2**2 - mu1_mu2 = mu1 * mu2 - sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq - sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq - sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 - - ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * - (sigma1_sq + sigma2_sq + C2)) - return ssim_map.mean() - - -''' -# -------------------------------------------- -# matlab's bicubic imresize (numpy and torch) [0, 1] -# -------------------------------------------- -''' - - -# matlab 'imresize' function, now only support 'bicubic' -def cubic(x): - absx = torch.abs(x) - absx2 = absx**2 - absx3 = absx**3 - return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \ - (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx)) - - -def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing): - if (scale < 1) and (antialiasing): - # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width - kernel_width = kernel_width / scale - - # Output-space coordinates - x = torch.linspace(1, out_length, out_length) - - # Input-space coordinates. Calculate the inverse mapping such that 0.5 - # in output space maps to 0.5 in input space, and 0.5+scale in output - # space maps to 1.5 in input space. - u = x / scale + 0.5 * (1 - 1 / scale) - - # What is the left-most pixel that can be involved in the computation? - left = torch.floor(u - kernel_width / 2) - - # What is the maximum number of pixels that can be involved in the - # computation? Note: it's OK to use an extra pixel here; if the - # corresponding weights are all zero, it will be eliminated at the end - # of this function. - P = math.ceil(kernel_width) + 2 - - # The indices of the input pixels involved in computing the k-th output - # pixel are in row k of the indices matrix. - indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view( - 1, P).expand(out_length, P) - - # The weights used to compute the k-th output pixel are in row k of the - # weights matrix. - distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices - # apply cubic kernel - if (scale < 1) and (antialiasing): - weights = scale * cubic(distance_to_center * scale) - else: - weights = cubic(distance_to_center) - # Normalize the weights matrix so that each row sums to 1. - weights_sum = torch.sum(weights, 1).view(out_length, 1) - weights = weights / weights_sum.expand(out_length, P) - - # If a column in weights is all zero, get rid of it. only consider the first and last column. - weights_zero_tmp = torch.sum((weights == 0), 0) - if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6): - indices = indices.narrow(1, 1, P - 2) - weights = weights.narrow(1, 1, P - 2) - if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6): - indices = indices.narrow(1, 0, P - 2) - weights = weights.narrow(1, 0, P - 2) - weights = weights.contiguous() - indices = indices.contiguous() - sym_len_s = -indices.min() + 1 - sym_len_e = indices.max() - in_length - indices = indices + sym_len_s - 1 - return weights, indices, int(sym_len_s), int(sym_len_e) - - -# -------------------------------------------- -# imresize for tensor image [0, 1] -# -------------------------------------------- -def imresize(img, scale, antialiasing=True): - # Now the scale should be the same for H and W - # input: img: pytorch tensor, CHW or HW [0,1] - # output: CHW or HW [0,1] w/o round - need_squeeze = True if img.dim() == 2 else False - if need_squeeze: - img.unsqueeze_(0) - in_C, in_H, in_W = img.size() - out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) - kernel_width = 4 - kernel = 'cubic' - - # Return the desired dimension order for performing the resize. The - # strategy is to perform the resize first along the dimension with the - # smallest scale factor. - # Now we do not support this. - - # get weights and indices - weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( - in_H, out_H, scale, kernel, kernel_width, antialiasing) - weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( - in_W, out_W, scale, kernel, kernel_width, antialiasing) - # process H dimension - # symmetric copying - img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W) - img_aug.narrow(1, sym_len_Hs, in_H).copy_(img) - - sym_patch = img[:, :sym_len_Hs, :] - inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(1, inv_idx) - img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv) - - sym_patch = img[:, -sym_len_He:, :] - inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(1, inv_idx) - img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) - - out_1 = torch.FloatTensor(in_C, out_H, in_W) - kernel_width = weights_H.size(1) - for i in range(out_H): - idx = int(indices_H[i][0]) - for j in range(out_C): - out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i]) - - # process W dimension - # symmetric copying - out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We) - out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1) - - sym_patch = out_1[:, :, :sym_len_Ws] - inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(2, inv_idx) - out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv) - - sym_patch = out_1[:, :, -sym_len_We:] - inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(2, inv_idx) - out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) - - out_2 = torch.FloatTensor(in_C, out_H, out_W) - kernel_width = weights_W.size(1) - for i in range(out_W): - idx = int(indices_W[i][0]) - for j in range(out_C): - out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i]) - if need_squeeze: - out_2.squeeze_() - return out_2 - - -# -------------------------------------------- -# imresize for numpy image [0, 1] -# -------------------------------------------- -def imresize_np(img, scale, antialiasing=True): - # Now the scale should be the same for H and W - # input: img: Numpy, HWC or HW [0,1] - # output: HWC or HW [0,1] w/o round - img = torch.from_numpy(img) - need_squeeze = True if img.dim() == 2 else False - if need_squeeze: - img.unsqueeze_(2) - - in_H, in_W, in_C = img.size() - out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) - kernel_width = 4 - kernel = 'cubic' - - # Return the desired dimension order for performing the resize. The - # strategy is to perform the resize first along the dimension with the - # smallest scale factor. - # Now we do not support this. - - # get weights and indices - weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( - in_H, out_H, scale, kernel, kernel_width, antialiasing) - weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( - in_W, out_W, scale, kernel, kernel_width, antialiasing) - # process H dimension - # symmetric copying - img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C) - img_aug.narrow(0, sym_len_Hs, in_H).copy_(img) - - sym_patch = img[:sym_len_Hs, :, :] - inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(0, inv_idx) - img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv) - - sym_patch = img[-sym_len_He:, :, :] - inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(0, inv_idx) - img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) - - out_1 = torch.FloatTensor(out_H, in_W, in_C) - kernel_width = weights_H.size(1) - for i in range(out_H): - idx = int(indices_H[i][0]) - for j in range(out_C): - out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i]) - - # process W dimension - # symmetric copying - out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C) - out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1) - - sym_patch = out_1[:, :sym_len_Ws, :] - inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(1, inv_idx) - out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv) - - sym_patch = out_1[:, -sym_len_We:, :] - inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(1, inv_idx) - out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) - - out_2 = torch.FloatTensor(out_H, out_W, in_C) - kernel_width = weights_W.size(1) - for i in range(out_W): - idx = int(indices_W[i][0]) - for j in range(out_C): - out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i]) - if need_squeeze: - out_2.squeeze_() - - return out_2.numpy() - - -if __name__ == '__main__': - print('---') -# img = imread_uint('test.bmp', 3) -# img = uint2single(img) -# img_bicubic = imresize_np(img, 1/4) \ No newline at end of file diff --git a/ldm/modules/midas/__init__.py b/ldm/modules/midas/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/ldm/modules/midas/api.py b/ldm/modules/midas/api.py deleted file mode 100644 index 7699a37804130b841ade53491fa1abbddbf94564..0000000000000000000000000000000000000000 --- a/ldm/modules/midas/api.py +++ /dev/null @@ -1,170 +0,0 @@ -# based on https://github.com/isl-org/MiDaS - -import cv2 -import torch -import torch.nn as nn -from torchvision.transforms import Compose - -from ldm.modules.midas.midas.dpt_depth import DPTDepthModel -from ldm.modules.midas.midas.midas_net import MidasNet -from ldm.modules.midas.midas.midas_net_custom import MidasNet_small -from ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet - - -ISL_PATHS = { - "dpt_large": "/fsx/robin/midas_models/dpt_large-midas-2f21e586.pt", # TODO: adapt - "dpt_hybrid": "/fsx/robin/midas_models/dpt_hybrid-midas-501f0c75.pt", # TODO: adapt - "midas_v21": "", - "midas_v21_small": "", -} - - -def disabled_train(self, mode=True): - """Overwrite model.train with this function to make sure train/eval mode - does not change anymore.""" - return self - - -def load_midas_transform(model_type): - # https://github.com/isl-org/MiDaS/blob/master/run.py - # load transform only - if model_type == "dpt_large": # DPT-Large - net_w, net_h = 384, 384 - resize_mode = "minimal" - normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - elif model_type == "dpt_hybrid": # DPT-Hybrid - net_w, net_h = 384, 384 - resize_mode = "minimal" - normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - elif model_type == "midas_v21": - net_w, net_h = 384, 384 - resize_mode = "upper_bound" - normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - - elif model_type == "midas_v21_small": - net_w, net_h = 256, 256 - resize_mode = "upper_bound" - normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - - else: - assert False, f"model_type '{model_type}' not implemented, use: --model_type large" - - transform = Compose( - [ - Resize( - net_w, - net_h, - resize_target=None, - keep_aspect_ratio=True, - ensure_multiple_of=32, - resize_method=resize_mode, - image_interpolation_method=cv2.INTER_CUBIC, - ), - normalization, - PrepareForNet(), - ] - ) - - return transform - - -def load_model(model_type): - # https://github.com/isl-org/MiDaS/blob/master/run.py - # load network - model_path = ISL_PATHS[model_type] - if model_type == "dpt_large": # DPT-Large - model = DPTDepthModel( - path=model_path, - backbone="vitl16_384", - non_negative=True, - ) - net_w, net_h = 384, 384 - resize_mode = "minimal" - normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - elif model_type == "dpt_hybrid": # DPT-Hybrid - model = DPTDepthModel( - path=model_path, - backbone="vitb_rn50_384", - non_negative=True, - ) - net_w, net_h = 384, 384 - resize_mode = "minimal" - normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - elif model_type == "midas_v21": - model = MidasNet(model_path, non_negative=True) - net_w, net_h = 384, 384 - resize_mode = "upper_bound" - normalization = NormalizeImage( - mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] - ) - - elif model_type == "midas_v21_small": - model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True, - non_negative=True, blocks={'expand': True}) - net_w, net_h = 256, 256 - resize_mode = "upper_bound" - normalization = NormalizeImage( - mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] - ) - - else: - print(f"model_type '{model_type}' not implemented, use: --model_type large") - assert False - - transform = Compose( - [ - Resize( - net_w, - net_h, - resize_target=None, - keep_aspect_ratio=True, - ensure_multiple_of=32, - resize_method=resize_mode, - image_interpolation_method=cv2.INTER_CUBIC, - ), - normalization, - PrepareForNet(), - ] - ) - - return model.eval(), transform - - -class MiDaSInference(nn.Module): - MODEL_TYPES_TORCH_HUB = [ - "DPT_Large", - "DPT_Hybrid", - "MiDaS_small" - ] - MODEL_TYPES_ISL = [ - "dpt_large", - "dpt_hybrid", - "midas_v21", - "midas_v21_small", - ] - - def __init__(self, model_type): - super().__init__() - assert (model_type in self.MODEL_TYPES_ISL) - model, _ = load_model(model_type) - self.model = model - self.model.train = disabled_train - - def forward(self, x): - # x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array - # NOTE: we expect that the correct transform has been called during dataloading. - with torch.no_grad(): - prediction = self.model(x) - prediction = torch.nn.functional.interpolate( - prediction.unsqueeze(1), - size=x.shape[2:], - mode="bicubic", - align_corners=False, - ) - assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3]) - return prediction - diff --git a/ldm/modules/midas/midas/__init__.py b/ldm/modules/midas/midas/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/ldm/modules/midas/midas/base_model.py b/ldm/modules/midas/midas/base_model.py deleted file mode 100644 index 5cf430239b47ec5ec07531263f26f5c24a2311cd..0000000000000000000000000000000000000000 --- a/ldm/modules/midas/midas/base_model.py +++ /dev/null @@ -1,16 +0,0 @@ -import torch - - -class BaseModel(torch.nn.Module): - def load(self, path): - """Load model from file. - - Args: - path (str): file path - """ - parameters = torch.load(path, map_location=torch.device('cpu')) - - if "optimizer" in parameters: - parameters = parameters["model"] - - self.load_state_dict(parameters) diff --git a/ldm/modules/midas/midas/blocks.py b/ldm/modules/midas/midas/blocks.py deleted file mode 100644 index 2145d18fa98060a618536d9a64fe6589e9be4f78..0000000000000000000000000000000000000000 --- a/ldm/modules/midas/midas/blocks.py +++ /dev/null @@ -1,342 +0,0 @@ -import torch -import torch.nn as nn - -from .vit import ( - _make_pretrained_vitb_rn50_384, - _make_pretrained_vitl16_384, - _make_pretrained_vitb16_384, - forward_vit, -) - -def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",): - if backbone == "vitl16_384": - pretrained = _make_pretrained_vitl16_384( - use_pretrained, hooks=hooks, use_readout=use_readout - ) - scratch = _make_scratch( - [256, 512, 1024, 1024], features, groups=groups, expand=expand - ) # ViT-L/16 - 85.0% Top1 (backbone) - elif backbone == "vitb_rn50_384": - pretrained = _make_pretrained_vitb_rn50_384( - use_pretrained, - hooks=hooks, - use_vit_only=use_vit_only, - use_readout=use_readout, - ) - scratch = _make_scratch( - [256, 512, 768, 768], features, groups=groups, expand=expand - ) # ViT-H/16 - 85.0% Top1 (backbone) - elif backbone == "vitb16_384": - pretrained = _make_pretrained_vitb16_384( - use_pretrained, hooks=hooks, use_readout=use_readout - ) - scratch = _make_scratch( - [96, 192, 384, 768], features, groups=groups, expand=expand - ) # ViT-B/16 - 84.6% Top1 (backbone) - elif backbone == "resnext101_wsl": - pretrained = _make_pretrained_resnext101_wsl(use_pretrained) - scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand) # efficientnet_lite3 - elif backbone == "efficientnet_lite3": - pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable) - scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand) # efficientnet_lite3 - else: - print(f"Backbone '{backbone}' not implemented") - assert False - - return pretrained, scratch - - -def _make_scratch(in_shape, out_shape, groups=1, expand=False): - scratch = nn.Module() - - out_shape1 = out_shape - out_shape2 = out_shape - out_shape3 = out_shape - out_shape4 = out_shape - if expand==True: - out_shape1 = out_shape - out_shape2 = out_shape*2 - out_shape3 = out_shape*4 - out_shape4 = out_shape*8 - - scratch.layer1_rn = nn.Conv2d( - in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - scratch.layer2_rn = nn.Conv2d( - in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - scratch.layer3_rn = nn.Conv2d( - in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - scratch.layer4_rn = nn.Conv2d( - in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - - return scratch - - -def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False): - efficientnet = torch.hub.load( - "rwightman/gen-efficientnet-pytorch", - "tf_efficientnet_lite3", - pretrained=use_pretrained, - exportable=exportable - ) - return _make_efficientnet_backbone(efficientnet) - - -def _make_efficientnet_backbone(effnet): - pretrained = nn.Module() - - pretrained.layer1 = nn.Sequential( - effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2] - ) - pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3]) - pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5]) - pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9]) - - return pretrained - - -def _make_resnet_backbone(resnet): - pretrained = nn.Module() - pretrained.layer1 = nn.Sequential( - resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1 - ) - - pretrained.layer2 = resnet.layer2 - pretrained.layer3 = resnet.layer3 - pretrained.layer4 = resnet.layer4 - - return pretrained - - -def _make_pretrained_resnext101_wsl(use_pretrained): - resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl") - return _make_resnet_backbone(resnet) - - - -class Interpolate(nn.Module): - """Interpolation module. - """ - - def __init__(self, scale_factor, mode, align_corners=False): - """Init. - - Args: - scale_factor (float): scaling - mode (str): interpolation mode - """ - super(Interpolate, self).__init__() - - self.interp = nn.functional.interpolate - self.scale_factor = scale_factor - self.mode = mode - self.align_corners = align_corners - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input - - Returns: - tensor: interpolated data - """ - - x = self.interp( - x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners - ) - - return x - - -class ResidualConvUnit(nn.Module): - """Residual convolution module. - """ - - def __init__(self, features): - """Init. - - Args: - features (int): number of features - """ - super().__init__() - - self.conv1 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True - ) - - self.conv2 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True - ) - - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input - - Returns: - tensor: output - """ - out = self.relu(x) - out = self.conv1(out) - out = self.relu(out) - out = self.conv2(out) - - return out + x - - -class FeatureFusionBlock(nn.Module): - """Feature fusion block. - """ - - def __init__(self, features): - """Init. - - Args: - features (int): number of features - """ - super(FeatureFusionBlock, self).__init__() - - self.resConfUnit1 = ResidualConvUnit(features) - self.resConfUnit2 = ResidualConvUnit(features) - - def forward(self, *xs): - """Forward pass. - - Returns: - tensor: output - """ - output = xs[0] - - if len(xs) == 2: - output += self.resConfUnit1(xs[1]) - - output = self.resConfUnit2(output) - - output = nn.functional.interpolate( - output, scale_factor=2, mode="bilinear", align_corners=True - ) - - return output - - - - -class ResidualConvUnit_custom(nn.Module): - """Residual convolution module. - """ - - def __init__(self, features, activation, bn): - """Init. - - Args: - features (int): number of features - """ - super().__init__() - - self.bn = bn - - self.groups=1 - - self.conv1 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups - ) - - self.conv2 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups - ) - - if self.bn==True: - self.bn1 = nn.BatchNorm2d(features) - self.bn2 = nn.BatchNorm2d(features) - - self.activation = activation - - self.skip_add = nn.quantized.FloatFunctional() - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input - - Returns: - tensor: output - """ - - out = self.activation(x) - out = self.conv1(out) - if self.bn==True: - out = self.bn1(out) - - out = self.activation(out) - out = self.conv2(out) - if self.bn==True: - out = self.bn2(out) - - if self.groups > 1: - out = self.conv_merge(out) - - return self.skip_add.add(out, x) - - # return out + x - - -class FeatureFusionBlock_custom(nn.Module): - """Feature fusion block. - """ - - def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True): - """Init. - - Args: - features (int): number of features - """ - super(FeatureFusionBlock_custom, self).__init__() - - self.deconv = deconv - self.align_corners = align_corners - - self.groups=1 - - self.expand = expand - out_features = features - if self.expand==True: - out_features = features//2 - - self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1) - - self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn) - self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn) - - self.skip_add = nn.quantized.FloatFunctional() - - def forward(self, *xs): - """Forward pass. - - Returns: - tensor: output - """ - output = xs[0] - - if len(xs) == 2: - res = self.resConfUnit1(xs[1]) - output = self.skip_add.add(output, res) - # output += res - - output = self.resConfUnit2(output) - - output = nn.functional.interpolate( - output, scale_factor=2, mode="bilinear", align_corners=self.align_corners - ) - - output = self.out_conv(output) - - return output - diff --git a/ldm/modules/midas/midas/dpt_depth.py b/ldm/modules/midas/midas/dpt_depth.py deleted file mode 100644 index 4e9aab5d2767dffea39da5b3f30e2798688216f1..0000000000000000000000000000000000000000 --- a/ldm/modules/midas/midas/dpt_depth.py +++ /dev/null @@ -1,109 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -from .base_model import BaseModel -from .blocks import ( - FeatureFusionBlock, - FeatureFusionBlock_custom, - Interpolate, - _make_encoder, - forward_vit, -) - - -def _make_fusion_block(features, use_bn): - return FeatureFusionBlock_custom( - features, - nn.ReLU(False), - deconv=False, - bn=use_bn, - expand=False, - align_corners=True, - ) - - -class DPT(BaseModel): - def __init__( - self, - head, - features=256, - backbone="vitb_rn50_384", - readout="project", - channels_last=False, - use_bn=False, - ): - - super(DPT, self).__init__() - - self.channels_last = channels_last - - hooks = { - "vitb_rn50_384": [0, 1, 8, 11], - "vitb16_384": [2, 5, 8, 11], - "vitl16_384": [5, 11, 17, 23], - } - - # Instantiate backbone and reassemble blocks - self.pretrained, self.scratch = _make_encoder( - backbone, - features, - False, # Set to true of you want to train from scratch, uses ImageNet weights - groups=1, - expand=False, - exportable=False, - hooks=hooks[backbone], - use_readout=readout, - ) - - self.scratch.refinenet1 = _make_fusion_block(features, use_bn) - self.scratch.refinenet2 = _make_fusion_block(features, use_bn) - self.scratch.refinenet3 = _make_fusion_block(features, use_bn) - self.scratch.refinenet4 = _make_fusion_block(features, use_bn) - - self.scratch.output_conv = head - - - def forward(self, x): - if self.channels_last == True: - x.contiguous(memory_format=torch.channels_last) - - layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x) - - layer_1_rn = self.scratch.layer1_rn(layer_1) - layer_2_rn = self.scratch.layer2_rn(layer_2) - layer_3_rn = self.scratch.layer3_rn(layer_3) - layer_4_rn = self.scratch.layer4_rn(layer_4) - - path_4 = self.scratch.refinenet4(layer_4_rn) - path_3 = self.scratch.refinenet3(path_4, layer_3_rn) - path_2 = self.scratch.refinenet2(path_3, layer_2_rn) - path_1 = self.scratch.refinenet1(path_2, layer_1_rn) - - out = self.scratch.output_conv(path_1) - - return out - - -class DPTDepthModel(DPT): - def __init__(self, path=None, non_negative=True, **kwargs): - features = kwargs["features"] if "features" in kwargs else 256 - - head = nn.Sequential( - nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1), - Interpolate(scale_factor=2, mode="bilinear", align_corners=True), - nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1), - nn.ReLU(True), - nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), - nn.ReLU(True) if non_negative else nn.Identity(), - nn.Identity(), - ) - - super().__init__(head, **kwargs) - - if path is not None: - self.load(path) - - def forward(self, x): - return super().forward(x).squeeze(dim=1) - diff --git a/ldm/modules/midas/midas/midas_net.py b/ldm/modules/midas/midas/midas_net.py deleted file mode 100644 index 8a954977800b0a0f48807e80fa63041910e33c1f..0000000000000000000000000000000000000000 --- a/ldm/modules/midas/midas/midas_net.py +++ /dev/null @@ -1,76 +0,0 @@ -"""MidashNet: Network for monocular depth estimation trained by mixing several datasets. -This file contains code that is adapted from -https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py -""" -import torch -import torch.nn as nn - -from .base_model import BaseModel -from .blocks import FeatureFusionBlock, Interpolate, _make_encoder - - -class MidasNet(BaseModel): - """Network for monocular depth estimation. - """ - - def __init__(self, path=None, features=256, non_negative=True): - """Init. - - Args: - path (str, optional): Path to saved model. Defaults to None. - features (int, optional): Number of features. Defaults to 256. - backbone (str, optional): Backbone network for encoder. Defaults to resnet50 - """ - print("Loading weights: ", path) - - super(MidasNet, self).__init__() - - use_pretrained = False if path is None else True - - self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained) - - self.scratch.refinenet4 = FeatureFusionBlock(features) - self.scratch.refinenet3 = FeatureFusionBlock(features) - self.scratch.refinenet2 = FeatureFusionBlock(features) - self.scratch.refinenet1 = FeatureFusionBlock(features) - - self.scratch.output_conv = nn.Sequential( - nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1), - Interpolate(scale_factor=2, mode="bilinear"), - nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1), - nn.ReLU(True), - nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), - nn.ReLU(True) if non_negative else nn.Identity(), - ) - - if path: - self.load(path) - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input data (image) - - Returns: - tensor: depth - """ - - layer_1 = self.pretrained.layer1(x) - layer_2 = self.pretrained.layer2(layer_1) - layer_3 = self.pretrained.layer3(layer_2) - layer_4 = self.pretrained.layer4(layer_3) - - layer_1_rn = self.scratch.layer1_rn(layer_1) - layer_2_rn = self.scratch.layer2_rn(layer_2) - layer_3_rn = self.scratch.layer3_rn(layer_3) - layer_4_rn = self.scratch.layer4_rn(layer_4) - - path_4 = self.scratch.refinenet4(layer_4_rn) - path_3 = self.scratch.refinenet3(path_4, layer_3_rn) - path_2 = self.scratch.refinenet2(path_3, layer_2_rn) - path_1 = self.scratch.refinenet1(path_2, layer_1_rn) - - out = self.scratch.output_conv(path_1) - - return torch.squeeze(out, dim=1) diff --git a/ldm/modules/midas/midas/midas_net_custom.py b/ldm/modules/midas/midas/midas_net_custom.py deleted file mode 100644 index 50e4acb5e53d5fabefe3dde16ab49c33c2b7797c..0000000000000000000000000000000000000000 --- a/ldm/modules/midas/midas/midas_net_custom.py +++ /dev/null @@ -1,128 +0,0 @@ -"""MidashNet: Network for monocular depth estimation trained by mixing several datasets. -This file contains code that is adapted from -https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py -""" -import torch -import torch.nn as nn - -from .base_model import BaseModel -from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder - - -class MidasNet_small(BaseModel): - """Network for monocular depth estimation. - """ - - def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True, - blocks={'expand': True}): - """Init. - - Args: - path (str, optional): Path to saved model. Defaults to None. - features (int, optional): Number of features. Defaults to 256. - backbone (str, optional): Backbone network for encoder. Defaults to resnet50 - """ - print("Loading weights: ", path) - - super(MidasNet_small, self).__init__() - - use_pretrained = False if path else True - - self.channels_last = channels_last - self.blocks = blocks - self.backbone = backbone - - self.groups = 1 - - features1=features - features2=features - features3=features - features4=features - self.expand = False - if "expand" in self.blocks and self.blocks['expand'] == True: - self.expand = True - features1=features - features2=features*2 - features3=features*4 - features4=features*8 - - self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable) - - self.scratch.activation = nn.ReLU(False) - - self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners) - self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners) - self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners) - self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners) - - - self.scratch.output_conv = nn.Sequential( - nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups), - Interpolate(scale_factor=2, mode="bilinear"), - nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1), - self.scratch.activation, - nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), - nn.ReLU(True) if non_negative else nn.Identity(), - nn.Identity(), - ) - - if path: - self.load(path) - - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input data (image) - - Returns: - tensor: depth - """ - if self.channels_last==True: - print("self.channels_last = ", self.channels_last) - x.contiguous(memory_format=torch.channels_last) - - - layer_1 = self.pretrained.layer1(x) - layer_2 = self.pretrained.layer2(layer_1) - layer_3 = self.pretrained.layer3(layer_2) - layer_4 = self.pretrained.layer4(layer_3) - - layer_1_rn = self.scratch.layer1_rn(layer_1) - layer_2_rn = self.scratch.layer2_rn(layer_2) - layer_3_rn = self.scratch.layer3_rn(layer_3) - layer_4_rn = self.scratch.layer4_rn(layer_4) - - - path_4 = self.scratch.refinenet4(layer_4_rn) - path_3 = self.scratch.refinenet3(path_4, layer_3_rn) - path_2 = self.scratch.refinenet2(path_3, layer_2_rn) - path_1 = self.scratch.refinenet1(path_2, layer_1_rn) - - out = self.scratch.output_conv(path_1) - - return torch.squeeze(out, dim=1) - - - -def fuse_model(m): - prev_previous_type = nn.Identity() - prev_previous_name = '' - previous_type = nn.Identity() - previous_name = '' - for name, module in m.named_modules(): - if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU: - # print("FUSED ", prev_previous_name, previous_name, name) - torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True) - elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d: - # print("FUSED ", prev_previous_name, previous_name) - torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True) - # elif previous_type == nn.Conv2d and type(module) == nn.ReLU: - # print("FUSED ", previous_name, name) - # torch.quantization.fuse_modules(m, [previous_name, name], inplace=True) - - prev_previous_type = previous_type - prev_previous_name = previous_name - previous_type = type(module) - previous_name = name \ No newline at end of file diff --git a/ldm/modules/midas/midas/transforms.py b/ldm/modules/midas/midas/transforms.py deleted file mode 100644 index 350cbc11662633ad7f8968eb10be2e7de6e384e9..0000000000000000000000000000000000000000 --- a/ldm/modules/midas/midas/transforms.py +++ /dev/null @@ -1,234 +0,0 @@ -import numpy as np -import cv2 -import math - - -def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA): - """Rezise the sample to ensure the given size. Keeps aspect ratio. - - Args: - sample (dict): sample - size (tuple): image size - - Returns: - tuple: new size - """ - shape = list(sample["disparity"].shape) - - if shape[0] >= size[0] and shape[1] >= size[1]: - return sample - - scale = [0, 0] - scale[0] = size[0] / shape[0] - scale[1] = size[1] / shape[1] - - scale = max(scale) - - shape[0] = math.ceil(scale * shape[0]) - shape[1] = math.ceil(scale * shape[1]) - - # resize - sample["image"] = cv2.resize( - sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method - ) - - sample["disparity"] = cv2.resize( - sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST - ) - sample["mask"] = cv2.resize( - sample["mask"].astype(np.float32), - tuple(shape[::-1]), - interpolation=cv2.INTER_NEAREST, - ) - sample["mask"] = sample["mask"].astype(bool) - - return tuple(shape) - - -class Resize(object): - """Resize sample to given size (width, height). - """ - - def __init__( - self, - width, - height, - resize_target=True, - keep_aspect_ratio=False, - ensure_multiple_of=1, - resize_method="lower_bound", - image_interpolation_method=cv2.INTER_AREA, - ): - """Init. - - Args: - width (int): desired output width - height (int): desired output height - resize_target (bool, optional): - True: Resize the full sample (image, mask, target). - False: Resize image only. - Defaults to True. - keep_aspect_ratio (bool, optional): - True: Keep the aspect ratio of the input sample. - Output sample might not have the given width and height, and - resize behaviour depends on the parameter 'resize_method'. - Defaults to False. - ensure_multiple_of (int, optional): - Output width and height is constrained to be multiple of this parameter. - Defaults to 1. - resize_method (str, optional): - "lower_bound": Output will be at least as large as the given size. - "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.) - "minimal": Scale as least as possible. (Output size might be smaller than given size.) - Defaults to "lower_bound". - """ - self.__width = width - self.__height = height - - self.__resize_target = resize_target - self.__keep_aspect_ratio = keep_aspect_ratio - self.__multiple_of = ensure_multiple_of - self.__resize_method = resize_method - self.__image_interpolation_method = image_interpolation_method - - def constrain_to_multiple_of(self, x, min_val=0, max_val=None): - y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int) - - if max_val is not None and y > max_val: - y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int) - - if y < min_val: - y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int) - - return y - - def get_size(self, width, height): - # determine new height and width - scale_height = self.__height / height - scale_width = self.__width / width - - if self.__keep_aspect_ratio: - if self.__resize_method == "lower_bound": - # scale such that output size is lower bound - if scale_width > scale_height: - # fit width - scale_height = scale_width - else: - # fit height - scale_width = scale_height - elif self.__resize_method == "upper_bound": - # scale such that output size is upper bound - if scale_width < scale_height: - # fit width - scale_height = scale_width - else: - # fit height - scale_width = scale_height - elif self.__resize_method == "minimal": - # scale as least as possbile - if abs(1 - scale_width) < abs(1 - scale_height): - # fit width - scale_height = scale_width - else: - # fit height - scale_width = scale_height - else: - raise ValueError( - f"resize_method {self.__resize_method} not implemented" - ) - - if self.__resize_method == "lower_bound": - new_height = self.constrain_to_multiple_of( - scale_height * height, min_val=self.__height - ) - new_width = self.constrain_to_multiple_of( - scale_width * width, min_val=self.__width - ) - elif self.__resize_method == "upper_bound": - new_height = self.constrain_to_multiple_of( - scale_height * height, max_val=self.__height - ) - new_width = self.constrain_to_multiple_of( - scale_width * width, max_val=self.__width - ) - elif self.__resize_method == "minimal": - new_height = self.constrain_to_multiple_of(scale_height * height) - new_width = self.constrain_to_multiple_of(scale_width * width) - else: - raise ValueError(f"resize_method {self.__resize_method} not implemented") - - return (new_width, new_height) - - def __call__(self, sample): - width, height = self.get_size( - sample["image"].shape[1], sample["image"].shape[0] - ) - - # resize sample - sample["image"] = cv2.resize( - sample["image"], - (width, height), - interpolation=self.__image_interpolation_method, - ) - - if self.__resize_target: - if "disparity" in sample: - sample["disparity"] = cv2.resize( - sample["disparity"], - (width, height), - interpolation=cv2.INTER_NEAREST, - ) - - if "depth" in sample: - sample["depth"] = cv2.resize( - sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST - ) - - sample["mask"] = cv2.resize( - sample["mask"].astype(np.float32), - (width, height), - interpolation=cv2.INTER_NEAREST, - ) - sample["mask"] = sample["mask"].astype(bool) - - return sample - - -class NormalizeImage(object): - """Normlize image by given mean and std. - """ - - def __init__(self, mean, std): - self.__mean = mean - self.__std = std - - def __call__(self, sample): - sample["image"] = (sample["image"] - self.__mean) / self.__std - - return sample - - -class PrepareForNet(object): - """Prepare sample for usage as network input. - """ - - def __init__(self): - pass - - def __call__(self, sample): - image = np.transpose(sample["image"], (2, 0, 1)) - sample["image"] = np.ascontiguousarray(image).astype(np.float32) - - if "mask" in sample: - sample["mask"] = sample["mask"].astype(np.float32) - sample["mask"] = np.ascontiguousarray(sample["mask"]) - - if "disparity" in sample: - disparity = sample["disparity"].astype(np.float32) - sample["disparity"] = np.ascontiguousarray(disparity) - - if "depth" in sample: - depth = sample["depth"].astype(np.float32) - sample["depth"] = np.ascontiguousarray(depth) - - return sample diff --git a/ldm/modules/midas/midas/vit.py b/ldm/modules/midas/midas/vit.py deleted file mode 100644 index ea46b1be88b261b0dec04f3da0256f5f66f88a74..0000000000000000000000000000000000000000 --- a/ldm/modules/midas/midas/vit.py +++ /dev/null @@ -1,491 +0,0 @@ -import torch -import torch.nn as nn -import timm -import types -import math -import torch.nn.functional as F - - -class Slice(nn.Module): - def __init__(self, start_index=1): - super(Slice, self).__init__() - self.start_index = start_index - - def forward(self, x): - return x[:, self.start_index :] - - -class AddReadout(nn.Module): - def __init__(self, start_index=1): - super(AddReadout, self).__init__() - self.start_index = start_index - - def forward(self, x): - if self.start_index == 2: - readout = (x[:, 0] + x[:, 1]) / 2 - else: - readout = x[:, 0] - return x[:, self.start_index :] + readout.unsqueeze(1) - - -class ProjectReadout(nn.Module): - def __init__(self, in_features, start_index=1): - super(ProjectReadout, self).__init__() - self.start_index = start_index - - self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU()) - - def forward(self, x): - readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :]) - features = torch.cat((x[:, self.start_index :], readout), -1) - - return self.project(features) - - -class Transpose(nn.Module): - def __init__(self, dim0, dim1): - super(Transpose, self).__init__() - self.dim0 = dim0 - self.dim1 = dim1 - - def forward(self, x): - x = x.transpose(self.dim0, self.dim1) - return x - - -def forward_vit(pretrained, x): - b, c, h, w = x.shape - - glob = pretrained.model.forward_flex(x) - - layer_1 = pretrained.activations["1"] - layer_2 = pretrained.activations["2"] - layer_3 = pretrained.activations["3"] - layer_4 = pretrained.activations["4"] - - layer_1 = pretrained.act_postprocess1[0:2](layer_1) - layer_2 = pretrained.act_postprocess2[0:2](layer_2) - layer_3 = pretrained.act_postprocess3[0:2](layer_3) - layer_4 = pretrained.act_postprocess4[0:2](layer_4) - - unflatten = nn.Sequential( - nn.Unflatten( - 2, - torch.Size( - [ - h // pretrained.model.patch_size[1], - w // pretrained.model.patch_size[0], - ] - ), - ) - ) - - if layer_1.ndim == 3: - layer_1 = unflatten(layer_1) - if layer_2.ndim == 3: - layer_2 = unflatten(layer_2) - if layer_3.ndim == 3: - layer_3 = unflatten(layer_3) - if layer_4.ndim == 3: - layer_4 = unflatten(layer_4) - - layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1) - layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2) - layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3) - layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4) - - return layer_1, layer_2, layer_3, layer_4 - - -def _resize_pos_embed(self, posemb, gs_h, gs_w): - posemb_tok, posemb_grid = ( - posemb[:, : self.start_index], - posemb[0, self.start_index :], - ) - - gs_old = int(math.sqrt(len(posemb_grid))) - - posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) - posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear") - posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1) - - posemb = torch.cat([posemb_tok, posemb_grid], dim=1) - - return posemb - - -def forward_flex(self, x): - b, c, h, w = x.shape - - pos_embed = self._resize_pos_embed( - self.pos_embed, h // self.patch_size[1], w // self.patch_size[0] - ) - - B = x.shape[0] - - if hasattr(self.patch_embed, "backbone"): - x = self.patch_embed.backbone(x) - if isinstance(x, (list, tuple)): - x = x[-1] # last feature if backbone outputs list/tuple of features - - x = self.patch_embed.proj(x).flatten(2).transpose(1, 2) - - if getattr(self, "dist_token", None) is not None: - cls_tokens = self.cls_token.expand( - B, -1, -1 - ) # stole cls_tokens impl from Phil Wang, thanks - dist_token = self.dist_token.expand(B, -1, -1) - x = torch.cat((cls_tokens, dist_token, x), dim=1) - else: - cls_tokens = self.cls_token.expand( - B, -1, -1 - ) # stole cls_tokens impl from Phil Wang, thanks - x = torch.cat((cls_tokens, x), dim=1) - - x = x + pos_embed - x = self.pos_drop(x) - - for blk in self.blocks: - x = blk(x) - - x = self.norm(x) - - return x - - -activations = {} - - -def get_activation(name): - def hook(model, input, output): - activations[name] = output - - return hook - - -def get_readout_oper(vit_features, features, use_readout, start_index=1): - if use_readout == "ignore": - readout_oper = [Slice(start_index)] * len(features) - elif use_readout == "add": - readout_oper = [AddReadout(start_index)] * len(features) - elif use_readout == "project": - readout_oper = [ - ProjectReadout(vit_features, start_index) for out_feat in features - ] - else: - assert ( - False - ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'" - - return readout_oper - - -def _make_vit_b16_backbone( - model, - features=[96, 192, 384, 768], - size=[384, 384], - hooks=[2, 5, 8, 11], - vit_features=768, - use_readout="ignore", - start_index=1, -): - pretrained = nn.Module() - - pretrained.model = model - pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1")) - pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2")) - pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3")) - pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4")) - - pretrained.activations = activations - - readout_oper = get_readout_oper(vit_features, features, use_readout, start_index) - - # 32, 48, 136, 384 - pretrained.act_postprocess1 = nn.Sequential( - readout_oper[0], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[0], - kernel_size=1, - stride=1, - padding=0, - ), - nn.ConvTranspose2d( - in_channels=features[0], - out_channels=features[0], - kernel_size=4, - stride=4, - padding=0, - bias=True, - dilation=1, - groups=1, - ), - ) - - pretrained.act_postprocess2 = nn.Sequential( - readout_oper[1], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[1], - kernel_size=1, - stride=1, - padding=0, - ), - nn.ConvTranspose2d( - in_channels=features[1], - out_channels=features[1], - kernel_size=2, - stride=2, - padding=0, - bias=True, - dilation=1, - groups=1, - ), - ) - - pretrained.act_postprocess3 = nn.Sequential( - readout_oper[2], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[2], - kernel_size=1, - stride=1, - padding=0, - ), - ) - - pretrained.act_postprocess4 = nn.Sequential( - readout_oper[3], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[3], - kernel_size=1, - stride=1, - padding=0, - ), - nn.Conv2d( - in_channels=features[3], - out_channels=features[3], - kernel_size=3, - stride=2, - padding=1, - ), - ) - - pretrained.model.start_index = start_index - pretrained.model.patch_size = [16, 16] - - # We inject this function into the VisionTransformer instances so that - # we can use it with interpolated position embeddings without modifying the library source. - pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model) - pretrained.model._resize_pos_embed = types.MethodType( - _resize_pos_embed, pretrained.model - ) - - return pretrained - - -def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None): - model = timm.create_model("vit_large_patch16_384", pretrained=pretrained) - - hooks = [5, 11, 17, 23] if hooks == None else hooks - return _make_vit_b16_backbone( - model, - features=[256, 512, 1024, 1024], - hooks=hooks, - vit_features=1024, - use_readout=use_readout, - ) - - -def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None): - model = timm.create_model("vit_base_patch16_384", pretrained=pretrained) - - hooks = [2, 5, 8, 11] if hooks == None else hooks - return _make_vit_b16_backbone( - model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout - ) - - -def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None): - model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained) - - hooks = [2, 5, 8, 11] if hooks == None else hooks - return _make_vit_b16_backbone( - model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout - ) - - -def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None): - model = timm.create_model( - "vit_deit_base_distilled_patch16_384", pretrained=pretrained - ) - - hooks = [2, 5, 8, 11] if hooks == None else hooks - return _make_vit_b16_backbone( - model, - features=[96, 192, 384, 768], - hooks=hooks, - use_readout=use_readout, - start_index=2, - ) - - -def _make_vit_b_rn50_backbone( - model, - features=[256, 512, 768, 768], - size=[384, 384], - hooks=[0, 1, 8, 11], - vit_features=768, - use_vit_only=False, - use_readout="ignore", - start_index=1, -): - pretrained = nn.Module() - - pretrained.model = model - - if use_vit_only == True: - pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1")) - pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2")) - else: - pretrained.model.patch_embed.backbone.stages[0].register_forward_hook( - get_activation("1") - ) - pretrained.model.patch_embed.backbone.stages[1].register_forward_hook( - get_activation("2") - ) - - pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3")) - pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4")) - - pretrained.activations = activations - - readout_oper = get_readout_oper(vit_features, features, use_readout, start_index) - - if use_vit_only == True: - pretrained.act_postprocess1 = nn.Sequential( - readout_oper[0], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[0], - kernel_size=1, - stride=1, - padding=0, - ), - nn.ConvTranspose2d( - in_channels=features[0], - out_channels=features[0], - kernel_size=4, - stride=4, - padding=0, - bias=True, - dilation=1, - groups=1, - ), - ) - - pretrained.act_postprocess2 = nn.Sequential( - readout_oper[1], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[1], - kernel_size=1, - stride=1, - padding=0, - ), - nn.ConvTranspose2d( - in_channels=features[1], - out_channels=features[1], - kernel_size=2, - stride=2, - padding=0, - bias=True, - dilation=1, - groups=1, - ), - ) - else: - pretrained.act_postprocess1 = nn.Sequential( - nn.Identity(), nn.Identity(), nn.Identity() - ) - pretrained.act_postprocess2 = nn.Sequential( - nn.Identity(), nn.Identity(), nn.Identity() - ) - - pretrained.act_postprocess3 = nn.Sequential( - readout_oper[2], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[2], - kernel_size=1, - stride=1, - padding=0, - ), - ) - - pretrained.act_postprocess4 = nn.Sequential( - readout_oper[3], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[3], - kernel_size=1, - stride=1, - padding=0, - ), - nn.Conv2d( - in_channels=features[3], - out_channels=features[3], - kernel_size=3, - stride=2, - padding=1, - ), - ) - - pretrained.model.start_index = start_index - pretrained.model.patch_size = [16, 16] - - # We inject this function into the VisionTransformer instances so that - # we can use it with interpolated position embeddings without modifying the library source. - pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model) - - # We inject this function into the VisionTransformer instances so that - # we can use it with interpolated position embeddings without modifying the library source. - pretrained.model._resize_pos_embed = types.MethodType( - _resize_pos_embed, pretrained.model - ) - - return pretrained - - -def _make_pretrained_vitb_rn50_384( - pretrained, use_readout="ignore", hooks=None, use_vit_only=False -): - model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained) - - hooks = [0, 1, 8, 11] if hooks == None else hooks - return _make_vit_b_rn50_backbone( - model, - features=[256, 512, 768, 768], - size=[384, 384], - hooks=hooks, - use_vit_only=use_vit_only, - use_readout=use_readout, - ) diff --git a/ldm/modules/midas/utils.py b/ldm/modules/midas/utils.py deleted file mode 100644 index 9a9d3b5b66370fa98da9e067ba53ead848ea9a59..0000000000000000000000000000000000000000 --- a/ldm/modules/midas/utils.py +++ /dev/null @@ -1,189 +0,0 @@ -"""Utils for monoDepth.""" -import sys -import re -import numpy as np -import cv2 -import torch - - -def read_pfm(path): - """Read pfm file. - - Args: - path (str): path to file - - Returns: - tuple: (data, scale) - """ - with open(path, "rb") as file: - - color = None - width = None - height = None - scale = None - endian = None - - header = file.readline().rstrip() - if header.decode("ascii") == "PF": - color = True - elif header.decode("ascii") == "Pf": - color = False - else: - raise Exception("Not a PFM file: " + path) - - dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii")) - if dim_match: - width, height = list(map(int, dim_match.groups())) - else: - raise Exception("Malformed PFM header.") - - scale = float(file.readline().decode("ascii").rstrip()) - if scale < 0: - # little-endian - endian = "<" - scale = -scale - else: - # big-endian - endian = ">" - - data = np.fromfile(file, endian + "f") - shape = (height, width, 3) if color else (height, width) - - data = np.reshape(data, shape) - data = np.flipud(data) - - return data, scale - - -def write_pfm(path, image, scale=1): - """Write pfm file. - - Args: - path (str): pathto file - image (array): data - scale (int, optional): Scale. Defaults to 1. - """ - - with open(path, "wb") as file: - color = None - - if image.dtype.name != "float32": - raise Exception("Image dtype must be float32.") - - image = np.flipud(image) - - if len(image.shape) == 3 and image.shape[2] == 3: # color image - color = True - elif ( - len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1 - ): # greyscale - color = False - else: - raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.") - - file.write("PF\n" if color else "Pf\n".encode()) - file.write("%d %d\n".encode() % (image.shape[1], image.shape[0])) - - endian = image.dtype.byteorder - - if endian == "<" or endian == "=" and sys.byteorder == "little": - scale = -scale - - file.write("%f\n".encode() % scale) - - image.tofile(file) - - -def read_image(path): - """Read image and output RGB image (0-1). - - Args: - path (str): path to file - - Returns: - array: RGB image (0-1) - """ - img = cv2.imread(path) - - if img.ndim == 2: - img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) - - img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0 - - return img - - -def resize_image(img): - """Resize image and make it fit for network. - - Args: - img (array): image - - Returns: - tensor: data ready for network - """ - height_orig = img.shape[0] - width_orig = img.shape[1] - - if width_orig > height_orig: - scale = width_orig / 384 - else: - scale = height_orig / 384 - - height = (np.ceil(height_orig / scale / 32) * 32).astype(int) - width = (np.ceil(width_orig / scale / 32) * 32).astype(int) - - img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA) - - img_resized = ( - torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float() - ) - img_resized = img_resized.unsqueeze(0) - - return img_resized - - -def resize_depth(depth, width, height): - """Resize depth map and bring to CPU (numpy). - - Args: - depth (tensor): depth - width (int): image width - height (int): image height - - Returns: - array: processed depth - """ - depth = torch.squeeze(depth[0, :, :, :]).to("cpu") - - depth_resized = cv2.resize( - depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC - ) - - return depth_resized - -def write_depth(path, depth, bits=1): - """Write depth map to pfm and png file. - - Args: - path (str): filepath without extension - depth (array): depth - """ - write_pfm(path + ".pfm", depth.astype(np.float32)) - - depth_min = depth.min() - depth_max = depth.max() - - max_val = (2**(8*bits))-1 - - if depth_max - depth_min > np.finfo("float").eps: - out = max_val * (depth - depth_min) / (depth_max - depth_min) - else: - out = np.zeros(depth.shape, dtype=depth.type) - - if bits == 1: - cv2.imwrite(path + ".png", out.astype("uint8")) - elif bits == 2: - cv2.imwrite(path + ".png", out.astype("uint16")) - - return diff --git a/ldm/util.py b/ldm/util.py deleted file mode 100644 index 8c09ca1c72f7ceb3f9d7f9546aae5561baf62b13..0000000000000000000000000000000000000000 --- a/ldm/util.py +++ /dev/null @@ -1,197 +0,0 @@ -import importlib - -import torch -from torch import optim -import numpy as np - -from inspect import isfunction -from PIL import Image, ImageDraw, ImageFont - - -def log_txt_as_img(wh, xc, size=10): - # wh a tuple of (width, height) - # xc a list of captions to plot - b = len(xc) - txts = list() - for bi in range(b): - txt = Image.new("RGB", wh, color="white") - draw = ImageDraw.Draw(txt) - font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) - nc = int(40 * (wh[0] / 256)) - lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) - - try: - draw.text((0, 0), lines, fill="black", font=font) - except UnicodeEncodeError: - print("Cant encode string for logging. Skipping.") - - txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 - txts.append(txt) - txts = np.stack(txts) - txts = torch.tensor(txts) - return txts - - -def ismap(x): - if not isinstance(x, torch.Tensor): - return False - return (len(x.shape) == 4) and (x.shape[1] > 3) - - -def isimage(x): - if not isinstance(x,torch.Tensor): - return False - return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) - - -def exists(x): - return x is not None - - -def default(val, d): - if exists(val): - return val - return d() if isfunction(d) else d - - -def mean_flat(tensor): - """ - https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 - Take the mean over all non-batch dimensions. - """ - return tensor.mean(dim=list(range(1, len(tensor.shape)))) - - -def count_params(model, verbose=False): - total_params = sum(p.numel() for p in model.parameters()) - if verbose: - print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") - return total_params - - -def instantiate_from_config(config): - if not "target" in config: - if config == '__is_first_stage__': - return None - elif config == "__is_unconditional__": - return None - raise KeyError("Expected key `target` to instantiate.") - return get_obj_from_str(config["target"])(**config.get("params", dict())) - - -def get_obj_from_str(string, reload=False): - module, cls = string.rsplit(".", 1) - if reload: - module_imp = importlib.import_module(module) - importlib.reload(module_imp) - return getattr(importlib.import_module(module, package=None), cls) - - -class AdamWwithEMAandWings(optim.Optimizer): - # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298 - def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using - weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code - ema_power=1., param_names=()): - """AdamW that saves EMA versions of the parameters.""" - if not 0.0 <= lr: - raise ValueError("Invalid learning rate: {}".format(lr)) - if not 0.0 <= eps: - raise ValueError("Invalid epsilon value: {}".format(eps)) - if not 0.0 <= betas[0] < 1.0: - raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) - if not 0.0 <= betas[1] < 1.0: - raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) - if not 0.0 <= weight_decay: - raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) - if not 0.0 <= ema_decay <= 1.0: - raise ValueError("Invalid ema_decay value: {}".format(ema_decay)) - defaults = dict(lr=lr, betas=betas, eps=eps, - weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay, - ema_power=ema_power, param_names=param_names) - super().__init__(params, defaults) - - def __setstate__(self, state): - super().__setstate__(state) - for group in self.param_groups: - group.setdefault('amsgrad', False) - - @torch.no_grad() - def step(self, closure=None): - """Performs a single optimization step. - Args: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - with torch.enable_grad(): - loss = closure() - - for group in self.param_groups: - params_with_grad = [] - grads = [] - exp_avgs = [] - exp_avg_sqs = [] - ema_params_with_grad = [] - state_sums = [] - max_exp_avg_sqs = [] - state_steps = [] - amsgrad = group['amsgrad'] - beta1, beta2 = group['betas'] - ema_decay = group['ema_decay'] - ema_power = group['ema_power'] - - for p in group['params']: - if p.grad is None: - continue - params_with_grad.append(p) - if p.grad.is_sparse: - raise RuntimeError('AdamW does not support sparse gradients') - grads.append(p.grad) - - state = self.state[p] - - # State initialization - if len(state) == 0: - state['step'] = 0 - # Exponential moving average of gradient values - state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) - # Exponential moving average of squared gradient values - state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) - if amsgrad: - # Maintains max of all exp. moving avg. of sq. grad. values - state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) - # Exponential moving average of parameter values - state['param_exp_avg'] = p.detach().float().clone() - - exp_avgs.append(state['exp_avg']) - exp_avg_sqs.append(state['exp_avg_sq']) - ema_params_with_grad.append(state['param_exp_avg']) - - if amsgrad: - max_exp_avg_sqs.append(state['max_exp_avg_sq']) - - # update the steps for each param group update - state['step'] += 1 - # record the step after step update - state_steps.append(state['step']) - - optim._functional.adamw(params_with_grad, - grads, - exp_avgs, - exp_avg_sqs, - max_exp_avg_sqs, - state_steps, - amsgrad=amsgrad, - beta1=beta1, - beta2=beta2, - lr=group['lr'], - weight_decay=group['weight_decay'], - eps=group['eps'], - maximize=False) - - cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power) - for param, ema_param in zip(params_with_grad, ema_params_with_grad): - ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay) - - return loss \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 280f6e63a48487b0900c6800afdf7f938f46f447..b1174e7c4bf2398ea54fcaa5e685e485f4657b01 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,19 +1,10 @@ --extra-index-url https://download.pytorch.org/whl/cu113 torch==1.13.0 torchvision -albumentations==0.4.3 -opencv-python -pudb==2019.2 -imageio==2.9.0 -imageio-ffmpeg==0.4.2 -pytorch-lightning==1.4.2 -torchmetrics==0.6 -omegaconf==2.1.1 -test-tube>=0.7.5 -einops==0.3.0 -transformers==4.19.2 -webdataset==0.2.5 -open_clip_torch==2.7.0 +git+https://github.com/huggingface/diffusers.git@30f6f44 +transformers +accelerate +ftfy python-dotenv invisible-watermark https://github.com/apolinario/xformers/releases/download/0.0.3/xformers-0.0.14.dev0-cp38-cp38-linux_x86_64.whl \ No newline at end of file diff --git a/scripts/img2img.py b/scripts/img2img.py deleted file mode 100644 index 9085ba9d37ea6402b9ee543e82f7d8c56a1c273a..0000000000000000000000000000000000000000 --- a/scripts/img2img.py +++ /dev/null @@ -1,279 +0,0 @@ -"""make variations of input image""" - -import argparse, os -import PIL -import torch -import numpy as np -from omegaconf import OmegaConf -from PIL import Image -from tqdm import tqdm, trange -from itertools import islice -from einops import rearrange, repeat -from torchvision.utils import make_grid -from torch import autocast -from contextlib import nullcontext -from pytorch_lightning import seed_everything -from imwatermark import WatermarkEncoder - - -from scripts.txt2img import put_watermark -from ldm.util import instantiate_from_config -from ldm.models.diffusion.ddim import DDIMSampler - - -def chunk(it, size): - it = iter(it) - return iter(lambda: tuple(islice(it, size)), ()) - - -def load_model_from_config(config, ckpt, verbose=False): - print(f"Loading model from {ckpt}") - pl_sd = torch.load(ckpt, map_location="cpu") - if "global_step" in pl_sd: - print(f"Global Step: {pl_sd['global_step']}") - sd = pl_sd["state_dict"] - model = instantiate_from_config(config.model) - m, u = model.load_state_dict(sd, strict=False) - if len(m) > 0 and verbose: - print("missing keys:") - print(m) - if len(u) > 0 and verbose: - print("unexpected keys:") - print(u) - - model.cuda() - model.eval() - return model - - -def load_img(path): - image = Image.open(path).convert("RGB") - w, h = image.size - print(f"loaded input image of size ({w}, {h}) from {path}") - w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64 - image = image.resize((w, h), resample=PIL.Image.LANCZOS) - image = np.array(image).astype(np.float32) / 255.0 - image = image[None].transpose(0, 3, 1, 2) - image = torch.from_numpy(image) - return 2. * image - 1. - - -def main(): - parser = argparse.ArgumentParser() - - parser.add_argument( - "--prompt", - type=str, - nargs="?", - default="a painting of a virus monster playing guitar", - help="the prompt to render" - ) - - parser.add_argument( - "--init-img", - type=str, - nargs="?", - help="path to the input image" - ) - - parser.add_argument( - "--outdir", - type=str, - nargs="?", - help="dir to write results to", - default="outputs/img2img-samples" - ) - - parser.add_argument( - "--ddim_steps", - type=int, - default=50, - help="number of ddim sampling steps", - ) - - parser.add_argument( - "--fixed_code", - action='store_true', - help="if enabled, uses the same starting code across all samples ", - ) - - parser.add_argument( - "--ddim_eta", - type=float, - default=0.0, - help="ddim eta (eta=0.0 corresponds to deterministic sampling", - ) - parser.add_argument( - "--n_iter", - type=int, - default=1, - help="sample this often", - ) - - parser.add_argument( - "--C", - type=int, - default=4, - help="latent channels", - ) - parser.add_argument( - "--f", - type=int, - default=8, - help="downsampling factor, most often 8 or 16", - ) - - parser.add_argument( - "--n_samples", - type=int, - default=2, - help="how many samples to produce for each given prompt. A.k.a batch size", - ) - - parser.add_argument( - "--n_rows", - type=int, - default=0, - help="rows in the grid (default: n_samples)", - ) - - parser.add_argument( - "--scale", - type=float, - default=9.0, - help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))", - ) - - parser.add_argument( - "--strength", - type=float, - default=0.8, - help="strength for noising/unnoising. 1.0 corresponds to full destruction of information in init image", - ) - - parser.add_argument( - "--from-file", - type=str, - help="if specified, load prompts from this file", - ) - parser.add_argument( - "--config", - type=str, - default="configs/stable-diffusion/v2-inference.yaml", - help="path to config which constructs model", - ) - parser.add_argument( - "--ckpt", - type=str, - help="path to checkpoint of model", - ) - parser.add_argument( - "--seed", - type=int, - default=42, - help="the seed (for reproducible sampling)", - ) - parser.add_argument( - "--precision", - type=str, - help="evaluate at this precision", - choices=["full", "autocast"], - default="autocast" - ) - - opt = parser.parse_args() - seed_everything(opt.seed) - - config = OmegaConf.load(f"{opt.config}") - model = load_model_from_config(config, f"{opt.ckpt}") - - device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - model = model.to(device) - - sampler = DDIMSampler(model) - - os.makedirs(opt.outdir, exist_ok=True) - outpath = opt.outdir - - print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...") - wm = "SDV2" - wm_encoder = WatermarkEncoder() - wm_encoder.set_watermark('bytes', wm.encode('utf-8')) - - batch_size = opt.n_samples - n_rows = opt.n_rows if opt.n_rows > 0 else batch_size - if not opt.from_file: - prompt = opt.prompt - assert prompt is not None - data = [batch_size * [prompt]] - - else: - print(f"reading prompts from {opt.from_file}") - with open(opt.from_file, "r") as f: - data = f.read().splitlines() - data = list(chunk(data, batch_size)) - - sample_path = os.path.join(outpath, "samples") - os.makedirs(sample_path, exist_ok=True) - base_count = len(os.listdir(sample_path)) - grid_count = len(os.listdir(outpath)) - 1 - - assert os.path.isfile(opt.init_img) - init_image = load_img(opt.init_img).to(device) - init_image = repeat(init_image, '1 ... -> b ...', b=batch_size) - init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space - - sampler.make_schedule(ddim_num_steps=opt.ddim_steps, ddim_eta=opt.ddim_eta, verbose=False) - - assert 0. <= opt.strength <= 1., 'can only work with strength in [0.0, 1.0]' - t_enc = int(opt.strength * opt.ddim_steps) - print(f"target t_enc is {t_enc} steps") - - precision_scope = autocast if opt.precision == "autocast" else nullcontext - with torch.no_grad(): - with precision_scope("cuda"): - with model.ema_scope(): - all_samples = list() - for n in trange(opt.n_iter, desc="Sampling"): - for prompts in tqdm(data, desc="data"): - uc = None - if opt.scale != 1.0: - uc = model.get_learned_conditioning(batch_size * [""]) - if isinstance(prompts, tuple): - prompts = list(prompts) - c = model.get_learned_conditioning(prompts) - - # encode (scaled latent) - z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc] * batch_size).to(device)) - # decode it - samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt.scale, - unconditional_conditioning=uc, ) - - x_samples = model.decode_first_stage(samples) - x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0) - - for x_sample in x_samples: - x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c') - img = Image.fromarray(x_sample.astype(np.uint8)) - img = put_watermark(img, wm_encoder) - img.save(os.path.join(sample_path, f"{base_count:05}.png")) - base_count += 1 - all_samples.append(x_samples) - - # additionally, save as grid - grid = torch.stack(all_samples, 0) - grid = rearrange(grid, 'n b c h w -> (n b) c h w') - grid = make_grid(grid, nrow=n_rows) - - # to image - grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy() - grid = Image.fromarray(grid.astype(np.uint8)) - grid = put_watermark(grid, wm_encoder) - grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png')) - grid_count += 1 - - print(f"Your samples are ready and waiting for you here: \n{outpath} \nEnjoy.") - - -if __name__ == "__main__": - main() diff --git a/scripts/streamlit/depth2img.py b/scripts/streamlit/depth2img.py deleted file mode 100644 index ef6614e1a803eb477e09acda641118d3a9b81c24..0000000000000000000000000000000000000000 --- a/scripts/streamlit/depth2img.py +++ /dev/null @@ -1,158 +0,0 @@ -import sys -import torch -import numpy as np -import streamlit as st -from PIL import Image -from omegaconf import OmegaConf -from einops import repeat, rearrange -from pytorch_lightning import seed_everything -from imwatermark import WatermarkEncoder - -from scripts.txt2img import put_watermark -from ldm.util import instantiate_from_config -from ldm.models.diffusion.ddim import DDIMSampler -from ldm.data.util import AddMiDaS - -torch.set_grad_enabled(False) - - -@st.cache(allow_output_mutation=True) -def initialize_model(config, ckpt): - config = OmegaConf.load(config) - model = instantiate_from_config(config.model) - model.load_state_dict(torch.load(ckpt)["state_dict"], strict=False) - - device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - model = model.to(device) - sampler = DDIMSampler(model) - return sampler - - -def make_batch_sd( - image, - txt, - device, - num_samples=1, - model_type="dpt_hybrid" -): - image = np.array(image.convert("RGB")) - image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 - # sample['jpg'] is tensor hwc in [-1, 1] at this point - midas_trafo = AddMiDaS(model_type=model_type) - batch = { - "jpg": image, - "txt": num_samples * [txt], - } - batch = midas_trafo(batch) - batch["jpg"] = rearrange(batch["jpg"], 'h w c -> 1 c h w') - batch["jpg"] = repeat(batch["jpg"].to(device=device), "1 ... -> n ...", n=num_samples) - batch["midas_in"] = repeat(torch.from_numpy(batch["midas_in"][None, ...]).to(device=device), "1 ... -> n ...", n=num_samples) - return batch - - -def paint(sampler, image, prompt, t_enc, seed, scale, num_samples=1, callback=None, - do_full_sample=False): - device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - model = sampler.model - seed_everything(seed) - - print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...") - wm = "SDV2" - wm_encoder = WatermarkEncoder() - wm_encoder.set_watermark('bytes', wm.encode('utf-8')) - - with torch.no_grad(),\ - torch.autocast("cuda"): - batch = make_batch_sd(image, txt=prompt, device=device, num_samples=num_samples) - z = model.get_first_stage_encoding(model.encode_first_stage(batch[model.first_stage_key])) # move to latent space - c = model.cond_stage_model.encode(batch["txt"]) - c_cat = list() - for ck in model.concat_keys: - cc = batch[ck] - cc = model.depth_model(cc) - depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3], - keepdim=True) - display_depth = (cc - depth_min) / (depth_max - depth_min) - st.image(Image.fromarray((display_depth[0, 0, ...].cpu().numpy() * 255.).astype(np.uint8))) - cc = torch.nn.functional.interpolate( - cc, - size=z.shape[2:], - mode="bicubic", - align_corners=False, - ) - depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3], - keepdim=True) - cc = 2. * (cc - depth_min) / (depth_max - depth_min) - 1. - c_cat.append(cc) - c_cat = torch.cat(c_cat, dim=1) - # cond - cond = {"c_concat": [c_cat], "c_crossattn": [c]} - - # uncond cond - uc_cross = model.get_unconditional_conditioning(num_samples, "") - uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]} - if not do_full_sample: - # encode (scaled latent) - z_enc = sampler.stochastic_encode(z, torch.tensor([t_enc] * num_samples).to(model.device)) - else: - z_enc = torch.randn_like(z) - # decode it - samples = sampler.decode(z_enc, cond, t_enc, unconditional_guidance_scale=scale, - unconditional_conditioning=uc_full, callback=callback) - x_samples_ddim = model.decode_first_stage(samples) - result = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) - result = result.cpu().numpy().transpose(0, 2, 3, 1) * 255 - return [put_watermark(Image.fromarray(img.astype(np.uint8)), wm_encoder) for img in result] - - -def run(): - st.title("Stable Diffusion Depth2Img") - # run via streamlit run scripts/demo/depth2img.py - sampler = initialize_model(sys.argv[1], sys.argv[2]) - - image = st.file_uploader("Image", ["jpg", "png"]) - if image: - image = Image.open(image) - w, h = image.size - st.text(f"loaded input image of size ({w}, {h})") - width, height = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64 - image = image.resize((width, height)) - st.text(f"resized input image to size ({width}, {height} (w, h))") - st.image(image) - - prompt = st.text_input("Prompt") - - seed = st.number_input("Seed", min_value=0, max_value=1000000, value=0) - num_samples = st.number_input("Number of Samples", min_value=1, max_value=64, value=1) - scale = st.slider("Scale", min_value=0.1, max_value=30.0, value=9.0, step=0.1) - steps = st.slider("DDIM Steps", min_value=0, max_value=50, value=50, step=1) - strength = st.slider("Strength", min_value=0., max_value=1., value=0.9) - eta = st.sidebar.number_input("eta (DDIM)", value=0., min_value=0., max_value=1.) - - t_progress = st.progress(0) - def t_callback(t): - t_progress.progress(min((t + 1) / t_enc, 1.)) - - assert 0. <= strength <= 1., 'can only work with strength in [0.0, 1.0]' - do_full_sample = strength == 1. - t_enc = min(int(strength * steps), steps-1) - sampler.make_schedule(steps, ddim_eta=eta, verbose=True) - if st.button("Sample"): - result = paint( - sampler=sampler, - image=image, - prompt=prompt, - t_enc=t_enc, - seed=seed, - scale=scale, - num_samples=num_samples, - callback=t_callback, - do_full_sample=do_full_sample - ) - st.write("Result") - for image in result: - st.image(image, output_format='PNG') - - -if __name__ == "__main__": - run() diff --git a/scripts/streamlit/inpainting.py b/scripts/streamlit/inpainting.py deleted file mode 100644 index f13e609668997a99fc304d1e5832eb9d39539e98..0000000000000000000000000000000000000000 --- a/scripts/streamlit/inpainting.py +++ /dev/null @@ -1,194 +0,0 @@ -import sys -import cv2 -import torch -import numpy as np -import streamlit as st -from PIL import Image -from omegaconf import OmegaConf -from einops import repeat -from streamlit_drawable_canvas import st_canvas -from imwatermark import WatermarkEncoder - -from ldm.models.diffusion.ddim import DDIMSampler -from ldm.util import instantiate_from_config - - -torch.set_grad_enabled(False) - - -def put_watermark(img, wm_encoder=None): - if wm_encoder is not None: - img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) - img = wm_encoder.encode(img, 'dwtDct') - img = Image.fromarray(img[:, :, ::-1]) - return img - - -@st.cache(allow_output_mutation=True) -def initialize_model(config, ckpt): - config = OmegaConf.load(config) - model = instantiate_from_config(config.model) - - model.load_state_dict(torch.load(ckpt)["state_dict"], strict=False) - - device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - model = model.to(device) - sampler = DDIMSampler(model) - - return sampler - - -def make_batch_sd( - image, - mask, - txt, - device, - num_samples=1): - image = np.array(image.convert("RGB")) - image = image[None].transpose(0, 3, 1, 2) - image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 - - mask = np.array(mask.convert("L")) - mask = mask.astype(np.float32) / 255.0 - mask = mask[None, None] - mask[mask < 0.5] = 0 - mask[mask >= 0.5] = 1 - mask = torch.from_numpy(mask) - - masked_image = image * (mask < 0.5) - - batch = { - "image": repeat(image.to(device=device), "1 ... -> n ...", n=num_samples), - "txt": num_samples * [txt], - "mask": repeat(mask.to(device=device), "1 ... -> n ...", n=num_samples), - "masked_image": repeat(masked_image.to(device=device), "1 ... -> n ...", n=num_samples), - } - return batch - - -def inpaint(sampler, image, mask, prompt, seed, scale, ddim_steps, num_samples=1, w=512, h=512): - device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - model = sampler.model - - print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...") - wm = "SDV2" - wm_encoder = WatermarkEncoder() - wm_encoder.set_watermark('bytes', wm.encode('utf-8')) - - prng = np.random.RandomState(seed) - start_code = prng.randn(num_samples, 4, h // 8, w // 8) - start_code = torch.from_numpy(start_code).to(device=device, dtype=torch.float32) - - with torch.no_grad(), \ - torch.autocast("cuda"): - batch = make_batch_sd(image, mask, txt=prompt, device=device, num_samples=num_samples) - - c = model.cond_stage_model.encode(batch["txt"]) - - c_cat = list() - for ck in model.concat_keys: - cc = batch[ck].float() - if ck != model.masked_image_key: - bchw = [num_samples, 4, h // 8, w // 8] - cc = torch.nn.functional.interpolate(cc, size=bchw[-2:]) - else: - cc = model.get_first_stage_encoding(model.encode_first_stage(cc)) - c_cat.append(cc) - c_cat = torch.cat(c_cat, dim=1) - - # cond - cond = {"c_concat": [c_cat], "c_crossattn": [c]} - - # uncond cond - uc_cross = model.get_unconditional_conditioning(num_samples, "") - uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]} - - shape = [model.channels, h // 8, w // 8] - samples_cfg, intermediates = sampler.sample( - ddim_steps, - num_samples, - shape, - cond, - verbose=False, - eta=1.0, - unconditional_guidance_scale=scale, - unconditional_conditioning=uc_full, - x_T=start_code, - ) - x_samples_ddim = model.decode_first_stage(samples_cfg) - - result = torch.clamp((x_samples_ddim + 1.0) / 2.0, - min=0.0, max=1.0) - - result = result.cpu().numpy().transpose(0, 2, 3, 1) * 255 - return [put_watermark(Image.fromarray(img.astype(np.uint8)), wm_encoder) for img in result] - - -def run(): - st.title("Stable Diffusion Inpainting") - - sampler = initialize_model(sys.argv[1], sys.argv[2]) - - image = st.file_uploader("Image", ["jpg", "png"]) - if image: - image = Image.open(image) - w, h = image.size - print(f"loaded input image of size ({w}, {h})") - width, height = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 32 - image = image.resize((width, height)) - - prompt = st.text_input("Prompt") - - seed = st.number_input("Seed", min_value=0, max_value=1000000, value=0) - num_samples = st.number_input("Number of Samples", min_value=1, max_value=64, value=1) - scale = st.slider("Scale", min_value=0.1, max_value=30.0, value=10., step=0.1) - ddim_steps = st.slider("DDIM Steps", min_value=0, max_value=50, value=50, step=1) - - fill_color = "rgba(255, 255, 255, 0.0)" - stroke_width = st.number_input("Brush Size", - value=64, - min_value=1, - max_value=100) - stroke_color = "rgba(255, 255, 255, 1.0)" - bg_color = "rgba(0, 0, 0, 1.0)" - drawing_mode = "freedraw" - - st.write("Canvas") - st.caption( - "Draw a mask to inpaint, then click the 'Send to Streamlit' button (bottom left, with an arrow on it).") - canvas_result = st_canvas( - fill_color=fill_color, - stroke_width=stroke_width, - stroke_color=stroke_color, - background_color=bg_color, - background_image=image, - update_streamlit=False, - height=height, - width=width, - drawing_mode=drawing_mode, - key="canvas", - ) - if canvas_result: - mask = canvas_result.image_data - mask = mask[:, :, -1] > 0 - if mask.sum() > 0: - mask = Image.fromarray(mask) - - result = inpaint( - sampler=sampler, - image=image, - mask=mask, - prompt=prompt, - seed=seed, - scale=scale, - ddim_steps=ddim_steps, - num_samples=num_samples, - h=height, w=width - ) - st.write("Inpainted") - for image in result: - st.image(image, output_format='PNG') - - -if __name__ == "__main__": - run() \ No newline at end of file diff --git a/scripts/streamlit/superresolution.py b/scripts/streamlit/superresolution.py deleted file mode 100644 index c9803f7547f199ae59c20d7bd18636de31dce3de..0000000000000000000000000000000000000000 --- a/scripts/streamlit/superresolution.py +++ /dev/null @@ -1,169 +0,0 @@ -import sys -import torch -import numpy as np -import streamlit as st -from PIL import Image -from omegaconf import OmegaConf -from einops import repeat, rearrange -from pytorch_lightning import seed_everything -from imwatermark import WatermarkEncoder - -from scripts.txt2img import put_watermark -from ldm.models.diffusion.ddim import DDIMSampler -from ldm.models.diffusion.ddpm import LatentUpscaleDiffusion, LatentUpscaleFinetuneDiffusion -from ldm.util import exists, instantiate_from_config - - -torch.set_grad_enabled(False) - - -@st.cache(allow_output_mutation=True) -def initialize_model(config, ckpt): - config = OmegaConf.load(config) - model = instantiate_from_config(config.model) - model.load_state_dict(torch.load(ckpt)["state_dict"], strict=False) - - device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - model = model.to(device) - sampler = DDIMSampler(model) - return sampler - - -def make_batch_sd( - image, - txt, - device, - num_samples=1, -): - image = np.array(image.convert("RGB")) - image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 - batch = { - "lr": rearrange(image, 'h w c -> 1 c h w'), - "txt": num_samples * [txt], - } - batch["lr"] = repeat(batch["lr"].to(device=device), "1 ... -> n ...", n=num_samples) - return batch - - -def make_noise_augmentation(model, batch, noise_level=None): - x_low = batch[model.low_scale_key] - x_low = x_low.to(memory_format=torch.contiguous_format).float() - x_aug, noise_level = model.low_scale_model(x_low, noise_level) - return x_aug, noise_level - - -def paint(sampler, image, prompt, seed, scale, h, w, steps, num_samples=1, callback=None, eta=0., noise_level=None): - device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - model = sampler.model - seed_everything(seed) - prng = np.random.RandomState(seed) - start_code = prng.randn(num_samples, model.channels, h , w) - start_code = torch.from_numpy(start_code).to(device=device, dtype=torch.float32) - - print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...") - wm = "SDV2" - wm_encoder = WatermarkEncoder() - wm_encoder.set_watermark('bytes', wm.encode('utf-8')) - with torch.no_grad(),\ - torch.autocast("cuda"): - batch = make_batch_sd(image, txt=prompt, device=device, num_samples=num_samples) - c = model.cond_stage_model.encode(batch["txt"]) - c_cat = list() - if isinstance(model, LatentUpscaleFinetuneDiffusion): - for ck in model.concat_keys: - cc = batch[ck] - if exists(model.reshuffle_patch_size): - assert isinstance(model.reshuffle_patch_size, int) - cc = rearrange(cc, 'b c (p1 h) (p2 w) -> b (p1 p2 c) h w', - p1=model.reshuffle_patch_size, p2=model.reshuffle_patch_size) - c_cat.append(cc) - c_cat = torch.cat(c_cat, dim=1) - # cond - cond = {"c_concat": [c_cat], "c_crossattn": [c]} - # uncond cond - uc_cross = model.get_unconditional_conditioning(num_samples, "") - uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]} - elif isinstance(model, LatentUpscaleDiffusion): - x_augment, noise_level = make_noise_augmentation(model, batch, noise_level) - cond = {"c_concat": [x_augment], "c_crossattn": [c], "c_adm": noise_level} - # uncond cond - uc_cross = model.get_unconditional_conditioning(num_samples, "") - uc_full = {"c_concat": [x_augment], "c_crossattn": [uc_cross], "c_adm": noise_level} - else: - raise NotImplementedError() - - shape = [model.channels, h, w] - samples, intermediates = sampler.sample( - steps, - num_samples, - shape, - cond, - verbose=False, - eta=eta, - unconditional_guidance_scale=scale, - unconditional_conditioning=uc_full, - x_T=start_code, - callback=callback - ) - with torch.no_grad(): - x_samples_ddim = model.decode_first_stage(samples) - result = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) - result = result.cpu().numpy().transpose(0, 2, 3, 1) * 255 - st.text(f"upscaled image shape: {result.shape}") - return [put_watermark(Image.fromarray(img.astype(np.uint8)), wm_encoder) for img in result] - - -def run(): - st.title("Stable Diffusion Upscaling") - # run via streamlit run scripts/demo/depth2img.py - sampler = initialize_model(sys.argv[1], sys.argv[2]) - - image = st.file_uploader("Image", ["jpg", "png"]) - if image: - image = Image.open(image) - w, h = image.size - st.text(f"loaded input image of size ({w}, {h})") - width, height = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64 - image = image.resize((width, height)) - st.text(f"resized input image to size ({width}, {height} (w, h))") - st.image(image) - - st.write(f"\n Tip: Add a description of the object that should be upscaled, e.g.: 'a professional photograph of a cat'") - prompt = st.text_input("Prompt", "a high quality professional photograph") - - seed = st.number_input("Seed", min_value=0, max_value=1000000, value=0) - num_samples = st.number_input("Number of Samples", min_value=1, max_value=64, value=1) - scale = st.slider("Scale", min_value=0.1, max_value=30.0, value=9.0, step=0.1) - steps = st.slider("DDIM Steps", min_value=2, max_value=200, value=75, step=1) - eta = st.sidebar.number_input("eta (DDIM)", value=0., min_value=0., max_value=1.) - - noise_level = None - if isinstance(sampler.model, LatentUpscaleDiffusion): - # TODO: make this work for all models - noise_level = st.sidebar.number_input("Noise Augmentation", min_value=0, max_value=350, value=20) - noise_level = torch.Tensor(num_samples * [noise_level]).to(sampler.model.device).long() - - t_progress = st.progress(0) - def t_callback(t): - t_progress.progress(min((t + 1) / steps, 1.)) - - sampler.make_schedule(steps, ddim_eta=eta, verbose=True) - if st.button("Sample"): - result = paint( - sampler=sampler, - image=image, - prompt=prompt, - seed=seed, - scale=scale, - h=height, w=width, steps=steps, - num_samples=num_samples, - callback=t_callback, - noise_level=noise_level - ) - st.write("Result") - for image in result: - st.image(image, output_format='PNG') - - -if __name__ == "__main__": - run() diff --git a/scripts/tests/test_watermark.py b/scripts/tests/test_watermark.py deleted file mode 100644 index f93f8a6e70763c0e284157bc8225827520b2f5ef..0000000000000000000000000000000000000000 --- a/scripts/tests/test_watermark.py +++ /dev/null @@ -1,18 +0,0 @@ -import cv2 -import fire -from imwatermark import WatermarkDecoder - - -def testit(img_path): - bgr = cv2.imread(img_path) - decoder = WatermarkDecoder('bytes', 136) - watermark = decoder.decode(bgr, 'dwtDct') - try: - dec = watermark.decode('utf-8') - except: - dec = "null" - print(dec) - - -if __name__ == "__main__": - fire.Fire(testit) \ No newline at end of file diff --git a/scripts/txt2img.py b/scripts/txt2img.py deleted file mode 100644 index 1ed42a3cd87347998e947362e8845f28bf580fdd..0000000000000000000000000000000000000000 --- a/scripts/txt2img.py +++ /dev/null @@ -1,289 +0,0 @@ -import argparse, os -import cv2 -import torch -import numpy as np -from omegaconf import OmegaConf -from PIL import Image -from tqdm import tqdm, trange -from itertools import islice -from einops import rearrange -from torchvision.utils import make_grid -from pytorch_lightning import seed_everything -from torch import autocast -from contextlib import nullcontext -from imwatermark import WatermarkEncoder - -from ldm.util import instantiate_from_config -from ldm.models.diffusion.ddim import DDIMSampler -from ldm.models.diffusion.plms import PLMSSampler -from ldm.models.diffusion.dpm_solver import DPMSolverSampler - -torch.set_grad_enabled(False) - -def chunk(it, size): - it = iter(it) - return iter(lambda: tuple(islice(it, size)), ()) - - -def load_model_from_config(config, ckpt, verbose=False): - print(f"Loading model from {ckpt}") - pl_sd = torch.load(ckpt, map_location="cpu") - if "global_step" in pl_sd: - print(f"Global Step: {pl_sd['global_step']}") - sd = pl_sd["state_dict"] - model = instantiate_from_config(config.model) - m, u = model.load_state_dict(sd, strict=False) - if len(m) > 0 and verbose: - print("missing keys:") - print(m) - if len(u) > 0 and verbose: - print("unexpected keys:") - print(u) - - model.cuda() - model.eval() - return model - - -def parse_args(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--prompt", - type=str, - nargs="?", - default="a professional photograph of an astronaut riding a triceratops", - help="the prompt to render" - ) - parser.add_argument( - "--outdir", - type=str, - nargs="?", - help="dir to write results to", - default="outputs/txt2img-samples" - ) - parser.add_argument( - "--steps", - type=int, - default=50, - help="number of ddim sampling steps", - ) - parser.add_argument( - "--plms", - action='store_true', - help="use plms sampling", - ) - parser.add_argument( - "--dpm", - action='store_true', - help="use DPM (2) sampler", - ) - parser.add_argument( - "--fixed_code", - action='store_true', - help="if enabled, uses the same starting code across all samples ", - ) - parser.add_argument( - "--ddim_eta", - type=float, - default=0.0, - help="ddim eta (eta=0.0 corresponds to deterministic sampling", - ) - parser.add_argument( - "--n_iter", - type=int, - default=3, - help="sample this often", - ) - parser.add_argument( - "--H", - type=int, - default=512, - help="image height, in pixel space", - ) - parser.add_argument( - "--W", - type=int, - default=512, - help="image width, in pixel space", - ) - parser.add_argument( - "--C", - type=int, - default=4, - help="latent channels", - ) - parser.add_argument( - "--f", - type=int, - default=8, - help="downsampling factor, most often 8 or 16", - ) - parser.add_argument( - "--n_samples", - type=int, - default=3, - help="how many samples to produce for each given prompt. A.k.a batch size", - ) - parser.add_argument( - "--n_rows", - type=int, - default=0, - help="rows in the grid (default: n_samples)", - ) - parser.add_argument( - "--scale", - type=float, - default=9.0, - help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))", - ) - parser.add_argument( - "--from-file", - type=str, - help="if specified, load prompts from this file, separated by newlines", - ) - parser.add_argument( - "--config", - type=str, - default="configs/stable-diffusion/v2-inference.yaml", - help="path to config which constructs model", - ) - parser.add_argument( - "--ckpt", - type=str, - help="path to checkpoint of model", - ) - parser.add_argument( - "--seed", - type=int, - default=42, - help="the seed (for reproducible sampling)", - ) - parser.add_argument( - "--precision", - type=str, - help="evaluate at this precision", - choices=["full", "autocast"], - default="autocast" - ) - parser.add_argument( - "--repeat", - type=int, - default=1, - help="repeat each prompt in file this often", - ) - opt = parser.parse_args() - return opt - - -def put_watermark(img, wm_encoder=None): - if wm_encoder is not None: - img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) - img = wm_encoder.encode(img, 'dwtDct') - img = Image.fromarray(img[:, :, ::-1]) - return img - - -def main(opt): - seed_everything(opt.seed) - - config = OmegaConf.load(f"{opt.config}") - model = load_model_from_config(config, f"{opt.ckpt}") - - device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - model = model.to(device) - - if opt.plms: - sampler = PLMSSampler(model) - elif opt.dpm: - sampler = DPMSolverSampler(model) - else: - sampler = DDIMSampler(model) - - os.makedirs(opt.outdir, exist_ok=True) - outpath = opt.outdir - - print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...") - wm = "SDV2" - wm_encoder = WatermarkEncoder() - wm_encoder.set_watermark('bytes', wm.encode('utf-8')) - - batch_size = opt.n_samples - n_rows = opt.n_rows if opt.n_rows > 0 else batch_size - if not opt.from_file: - prompt = opt.prompt - assert prompt is not None - data = [batch_size * [prompt]] - - else: - print(f"reading prompts from {opt.from_file}") - with open(opt.from_file, "r") as f: - data = f.read().splitlines() - data = [p for p in data for i in range(opt.repeat)] - data = list(chunk(data, batch_size)) - - sample_path = os.path.join(outpath, "samples") - os.makedirs(sample_path, exist_ok=True) - sample_count = 0 - base_count = len(os.listdir(sample_path)) - grid_count = len(os.listdir(outpath)) - 1 - - start_code = None - if opt.fixed_code: - start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device) - - precision_scope = autocast if opt.precision == "autocast" else nullcontext - with torch.no_grad(), \ - precision_scope("cuda"), \ - model.ema_scope(): - all_samples = list() - for n in trange(opt.n_iter, desc="Sampling"): - for prompts in tqdm(data, desc="data"): - uc = None - if opt.scale != 1.0: - uc = model.get_learned_conditioning(batch_size * [""]) - if isinstance(prompts, tuple): - prompts = list(prompts) - c = model.get_learned_conditioning(prompts) - shape = [opt.C, opt.H // opt.f, opt.W // opt.f] - samples, _ = sampler.sample(S=opt.steps, - conditioning=c, - batch_size=opt.n_samples, - shape=shape, - verbose=False, - unconditional_guidance_scale=opt.scale, - unconditional_conditioning=uc, - eta=opt.ddim_eta, - x_T=start_code) - - x_samples = model.decode_first_stage(samples) - x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0) - - for x_sample in x_samples: - x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c') - img = Image.fromarray(x_sample.astype(np.uint8)) - img = put_watermark(img, wm_encoder) - img.save(os.path.join(sample_path, f"{base_count:05}.png")) - base_count += 1 - sample_count += 1 - - all_samples.append(x_samples) - - # additionally, save as grid - grid = torch.stack(all_samples, 0) - grid = rearrange(grid, 'n b c h w -> (n b) c h w') - grid = make_grid(grid, nrow=n_rows) - - # to image - grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy() - grid = Image.fromarray(grid.astype(np.uint8)) - grid = put_watermark(grid, wm_encoder) - grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png')) - grid_count += 1 - - print(f"Your samples are ready and waiting for you here: \n{outpath} \n" - f" \nEnjoy.") - - -if __name__ == "__main__": - opt = parse_args() - main(opt) diff --git a/setup.py b/setup.py deleted file mode 100644 index 00f5b4d874f0f19ece54fac2dd50b39774b86c5b..0000000000000000000000000000000000000000 --- a/setup.py +++ /dev/null @@ -1,13 +0,0 @@ -from setuptools import setup, find_packages - -setup( - name='stable-diffusion', - version='0.0.1', - description='', - packages=find_packages(), - install_requires=[ - 'torch', - 'numpy', - 'tqdm', - ], -) \ No newline at end of file