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