216 lines
7.0 KiB
Python
216 lines
7.0 KiB
Python
import random
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import PIL.Image
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import cv2
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import numpy as np
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import torch
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from diffusers import PNDMScheduler, DDIMScheduler
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from loguru import logger
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from transformers import FeatureExtractionMixin, ImageFeatureExtractionMixin
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from lama_cleaner.helper import norm_img
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from lama_cleaner.model.base import InpaintModel
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from lama_cleaner.schema import Config, SDSampler
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#
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#
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# def preprocess_image(image):
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# w, h = image.size
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# w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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# image = image.resize((w, h), resample=PIL.Image.LANCZOS)
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# image = np.array(image).astype(np.float32) / 255.0
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# image = image[None].transpose(0, 3, 1, 2)
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# image = torch.from_numpy(image)
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# # [-1, 1]
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# return 2.0 * image - 1.0
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#
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#
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# def preprocess_mask(mask):
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# mask = mask.convert("L")
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# w, h = mask.size
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# w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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# mask = mask.resize((w // 8, h // 8), resample=PIL.Image.NEAREST)
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# mask = np.array(mask).astype(np.float32) / 255.0
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# mask = np.tile(mask, (4, 1, 1))
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# mask = mask[None].transpose(0, 1, 2, 3) # what does this step do?
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# mask = 1 - mask # repaint white, keep black
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# mask = torch.from_numpy(mask)
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# return mask
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class DummyFeatureExtractorOutput:
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def __init__(self, pixel_values):
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self.pixel_values = pixel_values
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def to(self, device):
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return self
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class DummyFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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def __call__(self, *args, **kwargs):
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return DummyFeatureExtractorOutput(torch.empty(0, 3))
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class DummySafetyChecker:
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def __init__(self, *args, **kwargs):
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pass
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def __call__(self, clip_input, images):
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return images, False
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class SD(InpaintModel):
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pad_mod = 64 # current diffusers only support 64 https://github.com/huggingface/diffusers/pull/505
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min_size = 512
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def init_model(self, device: torch.device, **kwargs):
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from .sd_pipeline import StableDiffusionInpaintPipeline
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model_kwargs = {"local_files_only": kwargs['sd_run_local']}
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if kwargs['sd_disable_nsfw']:
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logger.info("Disable Stable Diffusion Model NSFW checker")
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model_kwargs.update(dict(
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feature_extractor=DummyFeatureExtractor(),
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safety_checker=DummySafetyChecker(),
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))
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self.model = StableDiffusionInpaintPipeline.from_pretrained(
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self.model_id_or_path,
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revision="fp16" if torch.cuda.is_available() else "main",
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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use_auth_token=kwargs["hf_access_token"],
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**model_kwargs
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)
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# https://huggingface.co/docs/diffusers/v0.3.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
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self.model.enable_attention_slicing()
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self.model = self.model.to(device)
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if kwargs['sd_cpu_textencoder']:
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logger.info("Run Stable Diffusion TextEncoder on CPU")
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self.model.text_encoder = self.model.text_encoder.to(torch.device('cpu'), non_blocking=True)
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self.model.text_encoder = self.model.text_encoder.to(torch.float32, non_blocking=True )
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self.callbacks = kwargs.pop("callbacks", None)
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@torch.cuda.amp.autocast()
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def forward(self, image, mask, config: Config):
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"""Input image and output image have same size
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image: [H, W, C] RGB
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mask: [H, W, 1] 255 means area to repaint
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return: BGR IMAGE
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"""
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# image = norm_img(image) # [0, 1]
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# image = image * 2 - 1 # [0, 1] -> [-1, 1]
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# resize to latent feature map size
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# h, w = mask.shape[:2]
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# mask = cv2.resize(mask, (h // 8, w // 8), interpolation=cv2.INTER_AREA)
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# mask = norm_img(mask)
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#
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# image = torch.from_numpy(image).unsqueeze(0).to(self.device)
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# mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)
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if config.sd_sampler == SDSampler.ddim:
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scheduler = DDIMScheduler(
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beta_start=0.00085,
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beta_end=0.012,
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beta_schedule="scaled_linear",
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clip_sample=False,
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set_alpha_to_one=False,
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)
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elif config.sd_sampler == SDSampler.pndm:
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PNDM_kwargs = {
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"tensor_format": "pt",
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"beta_schedule": "scaled_linear",
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"beta_start": 0.00085,
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"beta_end": 0.012,
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"num_train_timesteps": 1000,
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"skip_prk_steps": True,
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}
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scheduler = PNDMScheduler(**PNDM_kwargs)
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else:
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raise ValueError(config.sd_sampler)
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self.model.scheduler = scheduler
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seed = config.sd_seed
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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if config.sd_mask_blur != 0:
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k = 2 * config.sd_mask_blur + 1
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mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
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output = self.model(
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prompt=config.prompt,
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init_image=PIL.Image.fromarray(image),
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mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
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strength=config.sd_strength,
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num_inference_steps=config.sd_steps,
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guidance_scale=config.sd_guidance_scale,
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output_type="np.array",
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callbacks=self.callbacks,
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).images[0]
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output = (output * 255).round().astype("uint8")
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output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
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return output
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@torch.no_grad()
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def __call__(self, image, mask, config: Config):
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"""
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images: [H, W, C] RGB, not normalized
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masks: [H, W]
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return: BGR IMAGE
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"""
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img_h, img_w = image.shape[:2]
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# boxes = boxes_from_mask(mask)
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if config.use_croper:
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logger.info("use croper")
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l, t, w, h = (
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config.croper_x,
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config.croper_y,
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config.croper_width,
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config.croper_height,
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)
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r = l + w
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b = t + h
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l = max(l, 0)
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r = min(r, img_w)
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t = max(t, 0)
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b = min(b, img_h)
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crop_img = image[t:b, l:r, :]
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crop_mask = mask[t:b, l:r]
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crop_image = self._pad_forward(crop_img, crop_mask, config)
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inpaint_result = image[:, :, ::-1]
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inpaint_result[t:b, l:r, :] = crop_image
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else:
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inpaint_result = self._pad_forward(image, mask, config)
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return inpaint_result
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@staticmethod
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def is_downloaded() -> bool:
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# model will be downloaded when app start, and can't switch in frontend settings
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return True
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class SD14(SD):
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model_id_or_path = "CompVis/stable-diffusion-v1-4"
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class SD15(SD):
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model_id_or_path = "CompVis/stable-diffusion-v1-5"
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