non-implemented mask with any type
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@ -72,6 +72,10 @@ class Api:
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if init_images is None:
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raise HTTPException(status_code=404, detail="Init image not found")
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mask = img2imgreq.mask
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if mask:
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raise HTTPException(status_code=400, detail="Mask not supported yet")
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populate = img2imgreq.copy(update={ # Override __init__ params
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"sd_model": shared.sd_model,
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@ -102,5 +102,5 @@ StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
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StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
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"StableDiffusionProcessingImg2Img",
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StableDiffusionProcessingImg2Img,
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[{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}]
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[{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}]
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).generate_model()
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@ -623,7 +623,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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sampler = None
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def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: str=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs):
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def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: Any=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs):
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super().__init__(**kwargs)
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self.init_images = init_images
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