IOPaint/lama_cleaner/model/sd.py

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import random
import PIL.Image
import cv2
import numpy as np
import torch
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from diffusers import (
PNDMScheduler,
DDIMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
)
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from loguru import logger
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from lama_cleaner.model.base import DiffusionInpaintModel
from lama_cleaner.model.utils import torch_gc, set_seed
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from lama_cleaner.schema import Config, SDSampler
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class CPUTextEncoderWrapper:
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def __init__(self, text_encoder, torch_dtype):
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self.config = text_encoder.config
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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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del text_encoder
torch_gc()
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def __call__(self, x, **kwargs):
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input_device = x.device
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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
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class SD(DiffusionInpaintModel):
pad_mod = 8
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min_size = 512
def init_model(self, device: torch.device, **kwargs):
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from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
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fp16 = not kwargs.get("no_half", False)
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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()
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torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
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self.model = StableDiffusionInpaintPipeline.from_pretrained(
self.model_id_or_path,
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revision="fp16" if use_gpu and fp16 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()
# https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention
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if kwargs.get("enable_xformers", False):
self.model.enable_xformers_memory_efficient_attention()
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if kwargs.get("cpu_offload", False) and use_gpu:
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# TODO: gpu_id
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logger.info("Enable sequential cpu offload")
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self.model.enable_sequential_cpu_offload(gpu_id=0)
else:
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):
"""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
"""
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scheduler_config = self.model.scheduler.config
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if config.sd_sampler == SDSampler.ddim:
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scheduler = DDIMScheduler.from_config(scheduler_config)
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elif config.sd_sampler == SDSampler.pndm:
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scheduler = PNDMScheduler.from_config(scheduler_config)
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elif config.sd_sampler == SDSampler.k_lms:
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scheduler = LMSDiscreteScheduler.from_config(scheduler_config)
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elif config.sd_sampler == SDSampler.k_euler:
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scheduler = EulerDiscreteScheduler.from_config(scheduler_config)
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elif config.sd_sampler == SDSampler.k_euler_a:
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scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler_config)
elif config.sd_sampler == SDSampler.dpm_plus_plus:
scheduler = DPMSolverMultistepScheduler.from_config(scheduler_config)
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else:
raise ValueError(config.sd_sampler)
self.model.scheduler = scheduler
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set_seed(config.sd_seed)
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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]
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output = self.model(
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image=PIL.Image.fromarray(image),
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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"),
num_inference_steps=config.sd_steps,
guidance_scale=config.sd_guidance_scale,
output_type="np.array",
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callback=self.callback,
height=img_h,
width=img_w,
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).images[0]
output = (output * 255).round().astype("uint8")
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
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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
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@staticmethod
def is_downloaded() -> bool:
# model will be downloaded when app start, and can't switch in frontend settings
return True
class SD15(SD):
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model_id_or_path = "runwayml/stable-diffusion-inpainting"
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class SD2(SD):
model_id_or_path = "stabilityai/stable-diffusion-2-inpainting"