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import PIL |
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import PIL.Image |
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import cv2 |
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import torch |
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from loguru import logger |
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from iopaint.helper import decode_base64_to_image |
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from .base import DiffusionInpaintModel |
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from iopaint.schema import InpaintRequest |
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from .utils import get_torch_dtype, enable_low_mem, is_local_files_only |
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class PaintByExample(DiffusionInpaintModel): |
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name = "Fantasy-Studio/Paint-by-Example" |
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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 import DiffusionPipeline |
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use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False)) |
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model_kwargs = { |
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"local_files_only": is_local_files_only(**kwargs), |
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} |
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if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False): |
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logger.info("Disable Paint By Example Model NSFW checker") |
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model_kwargs.update( |
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dict(safety_checker=None, requires_safety_checker=False) |
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) |
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self.model = DiffusionPipeline.from_pretrained( |
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self.name, torch_dtype=torch_dtype, **model_kwargs |
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) |
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enable_low_mem(self.model, kwargs.get("low_mem", False)) |
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if kwargs.get("cpu_offload", False) and use_gpu: |
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self.model.image_encoder = self.model.image_encoder.to(device) |
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self.model.enable_sequential_cpu_offload(gpu_id=0) |
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else: |
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self.model = self.model.to(device) |
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def forward(self, image, mask, config: InpaintRequest): |
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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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if config.paint_by_example_example_image is None: |
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raise ValueError("paint_by_example_example_image is required") |
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example_image, _, _ = decode_base64_to_image( |
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config.paint_by_example_example_image |
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) |
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output = self.model( |
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image=PIL.Image.fromarray(image), |
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mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"), |
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example_image=PIL.Image.fromarray(example_image), |
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num_inference_steps=config.sd_steps, |
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guidance_scale=config.sd_guidance_scale, |
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negative_prompt="out of frame, lowres, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, disfigured, gross proportions, malformed limbs, watermark, signature", |
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output_type="np.array", |
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generator=torch.manual_seed(config.sd_seed), |
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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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