| import PIL.Image |
| import cv2 |
| import numpy as np |
| import torch |
|
|
| from iopaint.const import KANDINSKY22_NAME |
| from .base import DiffusionInpaintModel |
| from iopaint.schema import InpaintRequest |
| from .utils import get_torch_dtype, enable_low_mem, is_local_files_only |
|
|
|
|
| class Kandinsky(DiffusionInpaintModel): |
| pad_mod = 64 |
| min_size = 512 |
|
|
| def init_model(self, device: torch.device, **kwargs): |
| from diffusers import AutoPipelineForInpainting |
|
|
| use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False)) |
|
|
| model_kwargs = { |
| "torch_dtype": torch_dtype, |
| "local_files_only": is_local_files_only(**kwargs), |
| } |
| self.model = AutoPipelineForInpainting.from_pretrained( |
| self.name, **model_kwargs |
| ).to(device) |
| enable_low_mem(self.model, kwargs.get("low_mem", False)) |
|
|
| self.callback = kwargs.pop("callback", None) |
|
|
| def forward(self, image, mask, config: InpaintRequest): |
| """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 |
| """ |
| self.set_scheduler(config) |
|
|
| generator = torch.manual_seed(config.sd_seed) |
| mask = mask.astype(np.float32) / 255 |
| img_h, img_w = image.shape[:2] |
|
|
| |
| output = self.model( |
| prompt=config.prompt, |
| negative_prompt=config.negative_prompt, |
| image=PIL.Image.fromarray(image), |
| mask_image=mask[:, :, 0], |
| height=img_h, |
| width=img_w, |
| num_inference_steps=config.sd_steps, |
| guidance_scale=config.sd_guidance_scale, |
| output_type="np", |
| callback_on_step_end=self.callback, |
| generator=generator, |
| ).images[0] |
|
|
| output = (output * 255).round().astype("uint8") |
| output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) |
| return output |
|
|
|
|
| class Kandinsky22(Kandinsky): |
| name = KANDINSKY22_NAME |
|
|
