74 lines
		
	
	
		
			2.5 KiB
		
	
	
	
		
			Python
		
	
	
	
			
		
		
	
	
			74 lines
		
	
	
		
			2.5 KiB
		
	
	
	
		
			Python
		
	
	
	
import torch
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from huggingface_hub import hf_hub_download
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from iopaint.const import ANYTEXT_NAME
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from iopaint.model.anytext.anytext_pipeline import AnyTextPipeline
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from iopaint.model.base import DiffusionInpaintModel
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from iopaint.model.utils import get_torch_dtype, is_local_files_only
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from iopaint.schema import InpaintRequest
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class AnyText(DiffusionInpaintModel):
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    name = ANYTEXT_NAME
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    pad_mod = 64
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    is_erase_model = False
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    @staticmethod
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    def download(local_files_only=False):
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        hf_hub_download(
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            repo_id=ANYTEXT_NAME,
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            filename="model_index.json",
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            local_files_only=local_files_only,
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        )
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        ckpt_path = hf_hub_download(
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            repo_id=ANYTEXT_NAME,
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            filename="pytorch_model.fp16.safetensors",
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            local_files_only=local_files_only,
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        )
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        font_path = hf_hub_download(
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            repo_id=ANYTEXT_NAME,
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            filename="SourceHanSansSC-Medium.otf",
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            local_files_only=local_files_only,
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        )
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        return ckpt_path, font_path
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    def init_model(self, device, **kwargs):
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        local_files_only = is_local_files_only(**kwargs)
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        ckpt_path, font_path = self.download(local_files_only)
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        use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False))
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        self.model = AnyTextPipeline(
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            ckpt_path=ckpt_path,
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            font_path=font_path,
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            device=device,
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            use_fp16=torch_dtype == torch.float16,
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        )
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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 inpainting
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        return: BGR IMAGE
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        """
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        height, width = image.shape[:2]
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        mask = mask.astype("float32") / 255.0
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        masked_image = image * (1 - mask)
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        # list of rgb ndarray
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        results, rtn_code, rtn_warning = self.model(
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            image=image,
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            masked_image=masked_image,
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            prompt=config.prompt,
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            negative_prompt=config.negative_prompt,
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            num_inference_steps=config.sd_steps,
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            strength=config.sd_strength,
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            guidance_scale=config.sd_guidance_scale,
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            height=height,
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            width=width,
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            seed=config.sd_seed,
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            sort_priority="y",
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            callback=self.callback
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        )
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        inpainted_rgb_image = results[0][..., ::-1]
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        return inpainted_rgb_image
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