67 lines
1.9 KiB
Python
67 lines
1.9 KiB
Python
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
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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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}
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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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if torch.backends.mps.is_available():
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self.model.enable_attention_slicing()
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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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# kandinsky 没有 strength
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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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