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app.py

@@ -1,28 +1,130 @@
 import gradio as gr
 from PIL import Image
+import torch
+from diffusers import DiffusionPipeline, AutoencoderTiny
+import os
 
-def predict(merge_ratio, guidance, steps, sharpness, prompt1, prompt2, seed):
-    result_image = Image.new('RGB', [512,512], (seed))
-    print(merge_ratio, guidance, steps, sharpness, prompt1, prompt2, seed)
-    return result_image
-
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            image = gr.Image(type="pil")
-        with gr.Column():
-            merge_ratio = gr.Slider(minimum=0, maximum=50, step=1, label="Merge Ratio")
-            guidance = gr.Slider(label="Guidance")
-            steps = gr.Slider(label="Steps")
-            sharpness = gr.Slider(minimum=0, maximum=50, step=1, label="sharpness")
-            seed = gr.Slider(randomize=True, minimum=0, maximum=12013012031030)
-            prompt1 = gr.Textbox(label="Prompt 1")
-            prompt2 = gr.Textbox(label="Prompt 2")
-            generate_bt = gr.Button("Generate")
-    
-    inputs = [merge_ratio, guidance, steps, sharpness, prompt1, prompt2, seed]
-    generate_bt.click(predict, inputs=inputs, outputs=image, show_progress=False)
-    seed.change(predict, inputs=inputs, outputs=image, show_progress=False)
+SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
+TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None)
+
+if SAFETY_CHECKER:
+    pipe = DiffusionPipeline.from_pretrained(
+        "SimianLuo/LCM_Dreamshaper_v7",
+        custom_pipeline="lcm_txt2img",
+        scheduler=None,
+    )
+else:
+    pipe = DiffusionPipeline.from_pretrained(
+        "SimianLuo/LCM_Dreamshaper_v7",
+        custom_pipeline="lcm_txt2img",
+        scheduler=None,
+        safety_checker=None,
+    )
+pipe.to(device="cuda", dtype=torch.float16)
+pipe.vae = AutoencoderTiny.from_pretrained(
+    "madebyollin/taesd", device="cuda", torch_dtype=torch.float16
+)
+pipe.vae = pipe.vae.cuda()
+pipe.unet.to(memory_format=torch.channels_last)
+pipe.set_progress_bar_config(disable=True)
+
+if TORCH_COMPILE:
+    pipe.text_encoder = torch.compile(pipe.text_encoder, mode="max-autotune")
+    pipe.tokenizer = torch.compile(pipe.tokenizer, mode="max-autotune")
+    pipe.unet = torch.compile(pipe.unet, mode="max-autotune")
+    pipe.vae = torch.compile(pipe.vae, mode="max-autotune")
+
+
+def predict(prompt1, prompt2, merge_ratio, guidance, steps, sharpness, seed=1231231):
+    torch.manual_seed(seed)
+    img = pipe(
+        prompt1=prompt1,
+        prompt2=prompt2,
+        sv=merge_ratio,
+        sharpness=sharpness,
+        width=512,
+        height=512,
+        num_inference_steps=steps,
+        guidance_scale=guidance,
+        lcm_origin_steps=50,
+        output_type="pil",
+        return_dict=False,
+    )
+    return img
+
+
+css="""
+#container{
+    margin: 0 auto;
+    max-width: 80rem;
+}
+#intro{
+    max-width: 32rem;
+    text-align: center;
+    margin: 0 auto;
+}
+"""
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="container"):
+        gr.Markdown(
+            """# SDZoom
+
+    Welcome to sdzoom, a testbed application designed for optimizing and experimenting with various 
+    configurations to achieve the fastest Stable Diffusion (SD) pipelines.
+    RTSD leverages the expertise provided by Latent Consistency Models (LCM). For more information about LCM,
+    visit their website at [Latent Consistency Models](https://latent-consistency-models.github.io/).
+
+    """, elem_id="intro"
+        )
+        with gr.Row():
+            with gr.Column():
+                image = gr.Image(type="pil")
+            with gr.Column():
+                merge_ratio = gr.Slider(
+                    value=50, minimum=1, maximum=100, step=1, label="Merge Ratio"
+                )
+                guidance = gr.Slider(
+                    label="Guidance", minimum=1, maximum=50, value=10.0, step=0.01
+                )
+                steps = gr.Slider(label="Steps", value=4, minimum=2, maximum=20, step=1)
+                sharpness = gr.Slider(
+                    value=1.0, minimum=0, maximum=1, step=0.001, label="Sharpness"
+                )
+                seed = gr.Slider(randomize=True, minimum=0, maximum=12013012031030, label="Seed")
+                prompt1 = gr.Textbox(label="Prompt 1")
+                prompt2 = gr.Textbox(label="Prompt 2")
+                generate_bt = gr.Button("Generate")
+
+        inputs = [prompt1, prompt2, merge_ratio, guidance, steps, sharpness, seed]
+        gr.Examples(
+            examples=[
+                ["Elon Musk", "Mark Zuckerberg", 50, 10.0, 4, 1.0, 1231231],
+                ["Elon Musk", "Bill Gates", 50, 10.0, 4, 1.0, 53453],
+                [
+                    "Asian women, intricate jewlery in her hair, 8k",
+                    "Tom Cruise, intricate jewlery in her hair, 8k",
+                    50,
+                    10.0,
+                    4,
+                    1.0,
+                    542343,
+                ],
+            ],
+            fn=predict,
+            inputs=inputs,
+            outputs=image,
+        )
+        generate_bt.click(fn=predict, inputs=inputs, outputs=image, show_progress=False)
+        seed.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
+        merge_ratio.change(
+            fn=predict, inputs=inputs, outputs=image, show_progress=False
+        )
+        guidance.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
+        steps.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
+        sharpness.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
+        prompt1.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
+        prompt2.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
+
+demo.queue()
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()

lcm_txt2img/pipeline.py

@@ -0,0 +1,760 @@
+# Copyright 2023 Stanford University Team and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
+# and https://github.com/hojonathanho/diffusion
+
+import math
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from diffusers import AutoencoderKL, ConfigMixin, DiffusionPipeline, SchedulerMixin, UNet2DConditionModel, logging
+from diffusers.configuration_utils import register_to_config
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.utils import BaseOutput
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class LatentConsistencyModelPipeline(DiffusionPipeline):
+    _optional_components = ["scheduler"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: "LCMScheduler",
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        scheduler = (
+            scheduler
+            if scheduler is not None
+            else LCMScheduler(
+                beta_start=0.00085, beta_end=0.0120, beta_schedule="scaled_linear", prediction_type="epsilon"
+            )
+        )
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        prompt_embeds: None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+        """
+
+        if prompt is not None and isinstance(prompt, str):
+            pass
+        elif prompt is not None and isinstance(prompt, list):
+            len(prompt)
+        else:
+            prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(
+                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = text_inputs.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            prompt_embeds = self.text_encoder(
+                text_input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            prompt_embeds = prompt_embeds[0]
+
+        if self.text_encoder is not None:
+            prompt_embeds_dtype = self.text_encoder.dtype
+        elif self.unet is not None:
+            prompt_embeds_dtype = self.unet.dtype
+        else:
+            prompt_embeds_dtype = prompt_embeds.dtype
+
+        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # Don't need to get uncond prompt embedding because of LCM Guided Distillation
+        return prompt_embeds
+
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is None:
+            has_nsfw_concept = None
+        else:
+            if torch.is_tensor(image):
+                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
+            else:
+                feature_extractor_input = self.image_processor.numpy_to_pil(image)
+            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        return image, has_nsfw_concept
+
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, latents=None):
+        #print(f"{batch_size}, {num_channels_latents}, {height // self.vae_scale_factor}, {width // self.vae_scale_factor}, {latents}")
+        #print(f"init_noise_sigma = {self.scheduler.init_noise_sigma}")
+        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        if latents is None:
+            latents = torch.randn(shape, dtype=dtype).to(device)
+        else:
+            latents = latents.to(device)
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def get_w_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+        """
+        see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
+        Args:
+        timesteps: torch.Tensor: generate embedding vectors at these timesteps
+        embedding_dim: int: dimension of the embeddings to generate
+        dtype: data type of the generated embeddings
+        Returns:
+        embedding vectors with shape `(len(timesteps), embedding_dim)`
+        """
+        assert len(w.shape) == 1
+        w = w * 1000.0
+
+        half_dim = embedding_dim // 2
+        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+        emb = w.to(dtype)[:, 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))
+        assert emb.shape == (w.shape[0], embedding_dim)
+        return emb
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt1: Union[str, List[str]] = None,
+        prompt2: Union[str, List[str]] = None,
+        sv: float = .5,
+        sharpness: float = 1.,
+        height: Optional[int] = 768,
+        width: Optional[int] = 768,
+        guidance_scale: float = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        latents: Optional[torch.FloatTensor] = None,
+        num_inference_steps: int = 4,
+        lcm_origin_steps: int = 50,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # 2. Define call parameters
+        #if prompt is not None and isinstance(prompt, str):
+        #    batch_size = 1
+        #elif prompt is not None and isinstance(prompt, list):
+        #    batch_size = len(prompt)
+        #else:
+        #    batch_size = prompt_embeds.shape[0]
+
+        batch_size = 1
+
+        device = self._execution_device
+        # do_classifier_free_guidance = guidance_scale > 0.0  # In LCM Implementation:  cfg_noise = noise_cond + cfg_scale * (noise_cond - noise_uncond) , (cfg_scale > 0.0 using CFG)
+
+        # 3. Encode input prompt
+        print(f"prompt1 = {prompt1}")
+        pe1 = self._encode_prompt(
+            prompt1,
+            device,
+            num_images_per_prompt,
+            prompt_embeds=prompt_embeds,
+        )
+
+        print(f"prompt2 = {prompt2}")
+        pe2 = self._encode_prompt(
+            prompt2,
+            device,
+            num_images_per_prompt,
+            prompt_embeds=None,
+        )
+
+        prompt_embeds = (100-sv)/100 * pe1 + sv/100 * pe2
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, lcm_origin_steps)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variable
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            latents,
+        )
+        bs = batch_size * num_images_per_prompt
+
+        # 6. Get Guidance Scale Embedding
+        w = torch.tensor(guidance_scale).repeat(bs)
+        w_embedding = self.get_w_embedding(w, embedding_dim=256).to(device=device, dtype=latents.dtype)
+
+        # 7. LCM MultiStep Sampling Loop:
+        #import time
+        #tsLenm1 = len(timesteps) - 1
+        #tm0 = time.time()
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                ts = torch.full((bs,), t, device=device, dtype=torch.long)
+                latents = latents.to(prompt_embeds.dtype)
+
+                # model prediction (v-prediction, eps, x)
+                model_pred = self.unet(
+                    latents,
+                    ts,
+                    timestep_cond=w_embedding,
+                    encoder_hidden_states=prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents, denoised = self.scheduler.step(model_pred, i, t, latents, return_dict=False)
+
+                # # call the callback, if provided
+                # if i == len(timesteps) - 1:
+                #if i == tsLenm1:
+                #    print('SYNC')
+                #    torch.cuda.synchronize()
+                progress_bar.update()
+        #print(f"unet time = {time.time() - tm0}")
+
+        denoised /= sharpness
+
+        #denoised = denoised.to(prompt_embeds.dtype)
+        if not output_type == "latent":
+            image = self.vae.decode(denoised / self.vae.config.scaling_factor, return_dict=False)[0]
+            #image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+            has_nsfw_concept = None
+        else:
+            image = denoised
+            has_nsfw_concept = None
+
+        if has_nsfw_concept is None:
+            do_denormalize = [True] * image.shape[0]
+        else:
+            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
+
+        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
+
+        if not return_dict:
+            #return (image, has_nsfw_concept)
+            print(f"image[0] isa {type(image[0])}")
+            return image[0]
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+
+
+@dataclass
+# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
+class LCMSchedulerOutput(BaseOutput):
+    """
+    Output class for the scheduler's `step` function output.
+    Args:
+        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
+            Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
+            denoising loop.
+        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
+            The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
+            `pred_original_sample` can be used to preview progress or for guidance.
+    """
+
+    prev_sample: torch.FloatTensor
+    denoised: Optional[torch.FloatTensor] = None
+
+
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
+def betas_for_alpha_bar(
+    num_diffusion_timesteps,
+    max_beta=0.999,
+    alpha_transform_type="cosine",
+):
+    """
+    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].
+    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
+    to that part of the diffusion process.
+    Args:
+        num_diffusion_timesteps (`int`): the number of betas to produce.
+        max_beta (`float`): the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+        alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
+                     Choose from `cosine` or `exp`
+    Returns:
+        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
+    """
+    if alpha_transform_type == "cosine":
+
+        def alpha_bar_fn(t):
+            return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
+
+    elif alpha_transform_type == "exp":
+
+        def alpha_bar_fn(t):
+            return math.exp(t * -12.0)
+
+    else:
+        raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")
+
+    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_fn(t2) / alpha_bar_fn(t1), max_beta))
+    return torch.tensor(betas, dtype=torch.float32)
+
+
+def rescale_zero_terminal_snr(betas):
+    """
+    Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)
+    Args:
+        betas (`torch.FloatTensor`):
+            the betas that the scheduler is being initialized with.
+    Returns:
+        `torch.FloatTensor`: rescaled betas with zero terminal SNR
+    """
+    # Convert betas to alphas_bar_sqrt
+    alphas = 1.0 - betas
+    alphas_cumprod = torch.cumprod(alphas, dim=0)
+    alphas_bar_sqrt = alphas_cumprod.sqrt()
+
+    # Store old values.
+    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
+    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
+
+    # Shift so the last timestep is zero.
+    alphas_bar_sqrt -= alphas_bar_sqrt_T
+
+    # Scale so the first timestep is back to the old value.
+    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
+
+    # Convert alphas_bar_sqrt to betas
+    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
+    alphas = alphas_bar[1:] / alphas_bar[:-1]  # Revert cumprod
+    alphas = torch.cat([alphas_bar[0:1], alphas])
+    betas = 1 - alphas
+
+    return betas
+
+
+class LCMScheduler(SchedulerMixin, ConfigMixin):
+    """
+    `LCMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
+    non-Markovian guidance.
+    This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
+    methods the library implements for all schedulers such as loading and saving.
+    Args:
+        num_train_timesteps (`int`, defaults to 1000):
+            The number of diffusion steps to train the model.
+        beta_start (`float`, defaults to 0.0001):
+            The starting `beta` value of inference.
+        beta_end (`float`, defaults to 0.02):
+            The final `beta` value.
+        beta_schedule (`str`, defaults to `"linear"`):
+            The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
+            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
+        trained_betas (`np.ndarray`, *optional*):
+            Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
+        clip_sample (`bool`, defaults to `True`):
+            Clip the predicted sample for numerical stability.
+        clip_sample_range (`float`, defaults to 1.0):
+            The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
+        set_alpha_to_one (`bool`, defaults to `True`):
+            Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
+            there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
+            otherwise it uses the alpha value at step 0.
+        steps_offset (`int`, defaults to 0):
+            An offset added to the inference steps. You can use a combination of `offset=1` and
+            `set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable
+            Diffusion.
+        prediction_type (`str`, defaults to `epsilon`, *optional*):
+            Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
+            `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
+            Video](https://imagen.research.google/video/paper.pdf) paper).
+        thresholding (`bool`, defaults to `False`):
+            Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
+            as Stable Diffusion.
+        dynamic_thresholding_ratio (`float`, defaults to 0.995):
+            The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
+        sample_max_value (`float`, defaults to 1.0):
+            The threshold value for dynamic thresholding. Valid only when `thresholding=True`.
+        timestep_spacing (`str`, defaults to `"leading"`):
+            The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
+            Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
+        rescale_betas_zero_snr (`bool`, defaults to `False`):
+            Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
+            dark samples instead of limiting it to samples with medium brightness. Loosely related to
+            [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
+    """
+
+    # _compatibles = [e.name for e in KarrasDiffusionSchedulers]
+    order = 1
+
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        beta_start: float = 0.0001,
+        beta_end: float = 0.02,
+        beta_schedule: str = "linear",
+        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
+        clip_sample: bool = True,
+        set_alpha_to_one: bool = True,
+        steps_offset: int = 0,
+        prediction_type: str = "epsilon",
+        thresholding: bool = False,
+        dynamic_thresholding_ratio: float = 0.995,
+        clip_sample_range: float = 1.0,
+        sample_max_value: float = 1.0,
+        timestep_spacing: str = "leading",
+        rescale_betas_zero_snr: bool = False,
+    ):
+        if trained_betas is not None:
+            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
+        elif beta_schedule == "linear":
+            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
+        elif beta_schedule == "scaled_linear":
+            # this schedule is very specific to the latent diffusion model.
+            self.betas = (
+                torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
+            )
+        elif beta_schedule == "squaredcos_cap_v2":
+            # Glide cosine schedule
+            self.betas = betas_for_alpha_bar(num_train_timesteps)
+        else:
+            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+
+        # Rescale for zero SNR
+        if rescale_betas_zero_snr:
+            self.betas = rescale_zero_terminal_snr(self.betas)
+
+        self.alphas = 1.0 - self.betas
+        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
+
+        # At every step in ddim, we are looking into the previous alphas_cumprod
+        # For the final step, there is no previous alphas_cumprod because we are already at 0
+        # `set_alpha_to_one` decides whether we set this parameter simply to one or
+        # whether we use the final alpha of the "non-previous" one.
+        self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
+
+        # standard deviation of the initial noise distribution
+        self.init_noise_sigma = 1.0
+
+        # setable values
+        self.num_inference_steps = None
+        self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))
+
+    def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
+        """
+        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
+        current timestep.
+        Args:
+            sample (`torch.FloatTensor`):
+                The input sample.
+            timestep (`int`, *optional*):
+                The current timestep in the diffusion chain.
+        Returns:
+            `torch.FloatTensor`:
+                A scaled input sample.
+        """
+        return sample
+
+    def _get_variance(self, timestep, prev_timestep):
+        alpha_prod_t = self.alphas_cumprod[timestep]
+        alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
+        beta_prod_t = 1 - alpha_prod_t
+        beta_prod_t_prev = 1 - alpha_prod_t_prev
+
+        variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
+
+        return variance
+
+    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
+    def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
+        """
+        "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
+        prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
+        s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
+        pixels from saturation at each step. We find that dynamic thresholding results in significantly better
+        photorealism as well as better image-text alignment, especially when using very large guidance weights."
+        https://arxiv.org/abs/2205.11487
+        """
+        dtype = sample.dtype
+        batch_size, channels, height, width = sample.shape
+
+        if dtype not in (torch.float32, torch.float64):
+            sample = sample.float()  # upcast for quantile calculation, and clamp not implemented for cpu half
+
+        # Flatten sample for doing quantile calculation along each image
+        sample = sample.reshape(batch_size, channels * height * width)
+
+        abs_sample = sample.abs()  # "a certain percentile absolute pixel value"
+
+        s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)
+        s = torch.clamp(
+            s, min=1, max=self.config.sample_max_value
+        )  # When clamped to min=1, equivalent to standard clipping to [-1, 1]
+
+        s = s.unsqueeze(1)  # (batch_size, 1) because clamp will broadcast along dim=0
+        sample = torch.clamp(sample, -s, s) / s  # "we threshold xt0 to the range [-s, s] and then divide by s"
+
+        sample = sample.reshape(batch_size, channels, height, width)
+        sample = sample.to(dtype)
+
+        return sample
+
+    def set_timesteps(self, num_inference_steps: int, lcm_origin_steps: int, device: Union[str, torch.device] = None):
+        """
+        Sets the discrete timesteps used for the diffusion chain (to be run before inference).
+        Args:
+            num_inference_steps (`int`):
+                The number of diffusion steps used when generating samples with a pre-trained model.
+        """
+
+        if num_inference_steps > self.config.num_train_timesteps:
+            raise ValueError(
+                f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
+                f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
+                f" maximal {self.config.num_train_timesteps} timesteps."
+            )
+
+        self.num_inference_steps = num_inference_steps
+
+        # LCM Timesteps Setting:  # Linear Spacing
+        c = self.config.num_train_timesteps // lcm_origin_steps
+        lcm_origin_timesteps = np.asarray(list(range(1, lcm_origin_steps + 1))) * c - 1  # LCM Training  Steps Schedule
+        skipping_step = len(lcm_origin_timesteps) // num_inference_steps
+        timesteps = lcm_origin_timesteps[::-skipping_step][:num_inference_steps]  # LCM Inference Steps Schedule
+
+        self.timesteps = torch.from_numpy(timesteps.copy()).to(device)
+
+    def get_scalings_for_boundary_condition_discrete(self, t):
+        self.sigma_data = 0.5  # Default: 0.5
+
+        # By dividing 0.1: This is almost a delta function at t=0.
+        c_skip = self.sigma_data**2 / ((t / 0.1) ** 2 + self.sigma_data**2)
+        c_out = (t / 0.1) / ((t / 0.1) ** 2 + self.sigma_data**2) ** 0.5
+        return c_skip, c_out
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timeindex: int,
+        timestep: int,
+        sample: torch.FloatTensor,
+        eta: float = 0.0,
+        use_clipped_model_output: bool = False,
+        generator=None,
+        variance_noise: Optional[torch.FloatTensor] = None,
+        return_dict: bool = True,
+    ) -> Union[LCMSchedulerOutput, Tuple]:
+        """
+        Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
+        process from the learned model outputs (most often the predicted noise).
+        Args:
+            model_output (`torch.FloatTensor`):
+                The direct output from learned diffusion model.
+            timestep (`float`):
+                The current discrete timestep in the diffusion chain.
+            sample (`torch.FloatTensor`):
+                A current instance of a sample created by the diffusion process.
+            eta (`float`):
+                The weight of noise for added noise in diffusion step.
+            use_clipped_model_output (`bool`, defaults to `False`):
+                If `True`, computes "corrected" `model_output` from the clipped predicted original sample. Necessary
+                because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no
+                clipping has happened, "corrected" `model_output` would coincide with the one provided as input and
+                `use_clipped_model_output` has no effect.
+            generator (`torch.Generator`, *optional*):
+                A random number generator.
+            variance_noise (`torch.FloatTensor`):
+                Alternative to generating noise with `generator` by directly providing the noise for the variance
+                itself. Useful for methods such as [`CycleDiffusion`].
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] or `tuple`.
+        Returns:
+            [`~schedulers.scheduling_utils.LCMSchedulerOutput`] or `tuple`:
+                If return_dict is `True`, [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] is returned, otherwise a
+                tuple is returned where the first element is the sample tensor.
+        """
+        if self.num_inference_steps is None:
+            raise ValueError(
+                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
+            )
+
+        # 1. get previous step value
+        prev_timeindex = timeindex + 1
+        if prev_timeindex < len(self.timesteps):
+            prev_timestep = self.timesteps[prev_timeindex]
+        else:
+            prev_timestep = timestep
+
+        # 2. compute alphas, betas
+        alpha_prod_t = self.alphas_cumprod[timestep]
+        alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
+
+        beta_prod_t = 1 - alpha_prod_t
+        beta_prod_t_prev = 1 - alpha_prod_t_prev
+
+        # 3. Get scalings for boundary conditions
+        c_skip, c_out = self.get_scalings_for_boundary_condition_discrete(timestep)
+
+        # 4. Different Parameterization:
+        parameterization = self.config.prediction_type
+
+        if parameterization == "epsilon":  # noise-prediction
+            pred_x0 = (sample - beta_prod_t.sqrt() * model_output) / alpha_prod_t.sqrt()
+
+        elif parameterization == "sample":  # x-prediction
+            pred_x0 = model_output
+
+        elif parameterization == "v_prediction":  # v-prediction
+            pred_x0 = alpha_prod_t.sqrt() * sample - beta_prod_t.sqrt() * model_output
+
+        # 4. Denoise model output using boundary conditions
+        denoised = c_out * pred_x0 + c_skip * sample
+
+        # 5. Sample z ~ N(0, I), For MultiStep Inference
+        # Noise is not used for one-step sampling.
+        if len(self.timesteps) > 1:
+            noise = torch.randn(model_output.shape).to(model_output.device)
+            prev_sample = alpha_prod_t_prev.sqrt() * denoised + beta_prod_t_prev.sqrt() * noise
+        else:
+            prev_sample = denoised
+
+        if not return_dict:
+            return (prev_sample, denoised)
+
+        return LCMSchedulerOutput(prev_sample=prev_sample, denoised=denoised)
+
+    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
+    def add_noise(
+        self,
+        original_samples: torch.FloatTensor,
+        noise: torch.FloatTensor,
+        timesteps: torch.IntTensor,
+    ) -> torch.FloatTensor:
+        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
+        alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
+        timesteps = timesteps.to(original_samples.device)
+
+        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
+        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
+        while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
+            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
+
+        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
+        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
+        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
+            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
+
+        noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
+        return noisy_samples
+
+    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
+    def get_velocity(
+        self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor
+    ) -> torch.FloatTensor:
+        # Make sure alphas_cumprod and timestep have same device and dtype as sample
+        alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype)
+        timesteps = timesteps.to(sample.device)
+
+        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
+        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
+        while len(sqrt_alpha_prod.shape) < len(sample.shape):
+            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
+
+        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
+        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
+        while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
+            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
+
+        velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
+        return velocity
+
+    def __len__(self):
+        return self.config.num_train_timesteps

requirements.txt

@@ -0,0 +1,23 @@
+wheel
+setuptools
+
+torch==2.1.0
+diffusers
+Pillow
+typing_extensions
+sympy
+mpmath
+packaging
+huggingface_hub
+tqdm
+flask
+requests
+urllib3
+PyYAML
+filelock
+numpy
+safetensors
+transformers
+regex
+accelerate
+gradio