IOPaint/lama_cleaner/model/paint_by_example.py

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import PIL
import PIL.Image
import cv2
import torch
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from loguru import logger
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from lama_cleaner.model.base import DiffusionInpaintModel
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from lama_cleaner.schema import Config
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class PaintByExample(DiffusionInpaintModel):
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name = "paint_by_example"
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pad_mod = 8
min_size = 512
def init_model(self, device: torch.device, **kwargs):
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from diffusers import DiffusionPipeline
fp16 = not kwargs.get("no_half", 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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model_kwargs = {"local_files_only": kwargs.get("local_files_only", False)}
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if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False):
logger.info("Disable Paint By Example Model NSFW checker")
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model_kwargs.update(
dict(safety_checker=None, requires_safety_checker=False)
)
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self.model = DiffusionPipeline.from_pretrained(
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"Fantasy-Studio/Paint-by-Example", torch_dtype=torch_dtype, **model_kwargs
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)
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self.model.enable_attention_slicing()
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if kwargs.get("enable_xformers", False):
self.model.enable_xformers_memory_efficient_attention()
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# TODO: gpu_id
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if kwargs.get("cpu_offload", False) and use_gpu:
self.model.image_encoder = self.model.image_encoder.to(device)
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self.model.enable_sequential_cpu_offload(gpu_id=0)
else:
self.model = self.model.to(device)
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@staticmethod
def download():
from diffusers import DiffusionPipeline
DiffusionPipeline.from_pretrained("Fantasy-Studio/Paint-by-Example")
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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
"""
output = self.model(
image=PIL.Image.fromarray(image),
mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
example_image=config.paint_by_example_example_image,
num_inference_steps=config.paint_by_example_steps,
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output_type="np.array",
generator=torch.manual_seed(config.paint_by_example_seed),
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).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.paint_by_example_match_histograms:
result = self._match_histograms(result, image[:, :, ::-1], mask)
if config.paint_by_example_mask_blur != 0:
k = 2 * config.paint_by_example_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