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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 iopaint.const import KANDINSKY22_NAME |
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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 Kandinsky(DiffusionInpaintModel): |
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pad_mod = 64 |
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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 AutoPipelineForInpainting |
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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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"torch_dtype": torch_dtype, |
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"local_files_only": is_local_files_only(**kwargs), |
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} |
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self.model = AutoPipelineForInpainting.from_pretrained( |
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self.name, **model_kwargs |
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).to(device) |
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enable_low_mem(self.model, kwargs.get("low_mem", False)) |
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self.callback = kwargs.pop("callback", None) |
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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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self.set_scheduler(config) |
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generator = torch.manual_seed(config.sd_seed) |
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mask = mask.astype(np.float32) / 255 |
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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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image=PIL.Image.fromarray(image), |
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mask_image=mask[:, :, 0], |
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height=img_h, |
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width=img_w, |
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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", |
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callback_on_step_end=self.callback, |
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generator=generator, |
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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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class Kandinsky22(Kandinsky): |
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name = KANDINSKY22_NAME |
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