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import gc
import PIL.Image
import cv2
import numpy as np
import torch
from diffusers import (
ControlNetModel,
)
from loguru import logger
from lama_cleaner.model.base import DiffusionInpaintModel
from lama_cleaner.model.utils import torch_gc, get_scheduler
from lama_cleaner.schema import Config
class CPUTextEncoderWrapper:
def __init__(self, text_encoder, torch_dtype):
self.config = text_encoder.config
self.text_encoder = text_encoder.to(torch.device("cpu"), non_blocking=True)
self.text_encoder = self.text_encoder.to(torch.float32, non_blocking=True)
self.torch_dtype = torch_dtype
del text_encoder
torch_gc()
def __call__(self, x, **kwargs):
input_device = x.device
return [
self.text_encoder(x.to(self.text_encoder.device), **kwargs)[0]
.to(input_device)
.to(self.torch_dtype)
]
@property
def dtype(self):
return self.torch_dtype
NAMES_MAP = {
"sd1.5": "runwayml/stable-diffusion-inpainting",
"anything4": "Sanster/anything-4.0-inpainting",
"realisticVision1.4": "Sanster/Realistic_Vision_V1.4-inpainting",
}
def load_from_local_model(local_model_path, torch_dtype, controlnet):
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
load_pipeline_from_original_stable_diffusion_ckpt,
)
from .pipeline import StableDiffusionControlNetInpaintPipeline
logger.info(f"Converting {local_model_path} to diffusers controlnet pipeline")
pipe = load_pipeline_from_original_stable_diffusion_ckpt(
local_model_path,
num_in_channels=9,
from_safetensors=local_model_path.endswith("safetensors"),
device="cpu",
)
inpaint_pipe = StableDiffusionControlNetInpaintPipeline(
vae=pipe.vae,
text_encoder=pipe.text_encoder,
tokenizer=pipe.tokenizer,
unet=pipe.unet,
controlnet=controlnet,
scheduler=pipe.scheduler,
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
)
del pipe
gc.collect()
return inpaint_pipe.to(torch_dtype)
class ControlNet(DiffusionInpaintModel):
name = "controlnet"
pad_mod = 8
min_size = 512
def init_model(self, device: torch.device, **kwargs):
from .pipeline import StableDiffusionControlNetInpaintPipeline
model_id = NAMES_MAP[kwargs["name"]]
fp16 = not kwargs.get("no_half", False)
model_kwargs = {
"local_files_only": kwargs.get("local_files_only", kwargs["sd_run_local"])
}
if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False):
logger.info("Disable Stable Diffusion Model NSFW checker")
model_kwargs.update(
dict(
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
)
)
use_gpu = device == torch.device("cuda") and torch.cuda.is_available()
torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
controlnet = ControlNetModel.from_pretrained(
f"lllyasviel/sd-controlnet-canny", torch_dtype=torch_dtype
)
if kwargs.get("sd_local_model_path", None):
self.model = load_from_local_model(
kwargs["sd_local_model_path"],
torch_dtype=torch_dtype,
controlnet=controlnet,
)
else:
self.model = StableDiffusionControlNetInpaintPipeline.from_pretrained(
model_id,
controlnet=controlnet,
revision="fp16" if use_gpu and fp16 else "main",
torch_dtype=torch_dtype,
**model_kwargs,
)
# https://huggingface.co/docs/diffusers/v0.7.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
self.model.enable_attention_slicing()
# https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention
if kwargs.get("enable_xformers", False):
self.model.enable_xformers_memory_efficient_attention()
if kwargs.get("cpu_offload", False) and use_gpu:
logger.info("Enable sequential cpu offload")
self.model.enable_sequential_cpu_offload(gpu_id=0)
else:
self.model = self.model.to(device)
if kwargs["sd_cpu_textencoder"]:
logger.info("Run Stable Diffusion TextEncoder on CPU")
self.model.text_encoder = CPUTextEncoderWrapper(
self.model.text_encoder, torch_dtype
)
self.callback = kwargs.pop("callback", None)
def forward(self, image, mask, config: Config):
"""Input image and output image have same size
image: [H, W, C] RGB
mask: [H, W, 1] 255 means area to repaint
return: BGR IMAGE
"""
scheduler_config = self.model.scheduler.config
scheduler = get_scheduler(config.sd_sampler, scheduler_config)
self.model.scheduler = scheduler
if config.sd_mask_blur != 0:
k = 2 * config.sd_mask_blur + 1
mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
img_h, img_w = image.shape[:2]
canny_image = cv2.Canny(image, 100, 200)
canny_image = canny_image[:, :, None]
canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
canny_image = PIL.Image.fromarray(canny_image)
mask_image = PIL.Image.fromarray(mask[:, :, -1], mode="L")
image = PIL.Image.fromarray(image)
output = self.model(
image=image,
control_image=canny_image,
prompt=config.prompt,
negative_prompt=config.negative_prompt,
mask_image=mask_image,
num_inference_steps=config.sd_steps,
guidance_scale=config.sd_guidance_scale,
output_type="np.array",
callback=self.callback,
height=img_h,
width=img_w,
generator=torch.manual_seed(config.sd_seed),
controlnet_conditioning_scale=config.controlnet_conditioning_scale,
).images[0]
output = (output * 255).round().astype("uint8")
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
def forward_post_process(self, result, image, mask, config):
if config.sd_match_histograms:
result = self._match_histograms(result, image[:, :, ::-1], mask)
if config.sd_mask_blur != 0:
k = 2 * config.sd_mask_blur + 1
mask = cv2.GaussianBlur(mask, (k, k), 0)
return result, image, mask
@staticmethod
def is_downloaded() -> bool:
# model will be downloaded when app start, and can't switch in frontend settings
return True
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