221 lines
7.5 KiB
Python
221 lines
7.5 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, LMSDiscreteScheduler, EulerDiscreteScheduler, \
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EulerAncestralDiscreteScheduler
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from loguru import logger
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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 CPUTextEncoderWrapper:
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def __init__(self, text_encoder, torch_dtype):
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self.text_encoder = text_encoder.to(torch.device('cpu'), non_blocking=True)
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self.text_encoder = self.text_encoder.to(torch.float32, non_blocking=True)
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self.torch_dtype = torch_dtype
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def __call__(self, x):
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input_device = x.device
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return [self.text_encoder(x.to(self.text_encoder.device))[0].to(input_device).to(self.torch_dtype)]
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class SD(InpaintModel):
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pad_mod = 8
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min_size = 512
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def init_model(self, device: torch.device, **kwargs):
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from diffusers.pipelines.stable_diffusion 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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safety_checker=None,
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))
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use_gpu = device == torch.device('cuda') and torch.cuda.is_available()
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torch_dtype = torch.float16 if use_gpu else torch.float32
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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 use_gpu else "main",
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torch_dtype=torch_dtype,
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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/v0.7.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
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self.model.enable_attention_slicing()
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# https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention
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if kwargs['sd_enable_xformers']:
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self.model.enable_xformers_memory_efficient_attention()
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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 = CPUTextEncoderWrapper(self.model.text_encoder, torch_dtype)
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self.callback = kwargs.pop("callback", None)
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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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scheduler_kwargs = dict(
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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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)
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if config.sd_sampler == SDSampler.ddim:
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scheduler = DDIMScheduler(
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**scheduler_kwargs,
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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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scheduler = PNDMScheduler(**scheduler_kwargs, skip_prk_steps=True)
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elif config.sd_sampler == SDSampler.k_lms:
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scheduler = LMSDiscreteScheduler(**scheduler_kwargs)
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elif config.sd_sampler == SDSampler.k_euler:
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scheduler = EulerDiscreteScheduler(**scheduler_kwargs)
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elif config.sd_sampler == SDSampler.k_euler_a:
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scheduler = EulerAncestralDiscreteScheduler(**scheduler_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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_kwargs = {
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self.image_key: PIL.Image.fromarray(image),
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}
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img_h, img_w = image.shape[:2]
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output = self.model(
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prompt=config.prompt,
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negative_prompt=config.negative_prompt,
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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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callback=self.callback,
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height=img_h,
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width=img_w,
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**_kwargs
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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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def forward_post_process(self, result, image, mask, config):
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if config.sd_match_histograms:
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result = self._match_histograms(result, image[:, :, ::-1], mask)
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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)
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return result, image, mask
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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 SD15(SD):
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model_id_or_path = "runwayml/stable-diffusion-inpainting"
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image_key = "image"
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