Update handler.py
Browse files- handler.py +6 -5
handler.py
CHANGED
@@ -19,10 +19,10 @@ class EndpointHandler():
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#self.generator = torch.Generator(device="cuda").manual_seed(0)
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self.smooth_pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
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)
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self.smooth_pipe.to("cuda")
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# load StableDiffusionInpaintPipeline pipeline
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@@ -59,13 +59,14 @@ class EndpointHandler():
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strength = data.pop("strength", 0.2)
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guidance_scale = data.pop("guidance_scale", 8.0)
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num_inference_steps = data.pop("num_inference_steps", 20)
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if(method == "smooth"):
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if encoded_image is not None:
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image = self.decode_base64_image(encoded_image)
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out = self.smooth_pipe(prompt, image=image).images[0]
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return out
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# process image
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if encoded_image is not None and encoded_mask_image is not None:
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#self.generator = torch.Generator(device="cuda").manual_seed(0)
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# self.smooth_pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
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# "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
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# )
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# self.smooth_pipe.to("cuda")
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# load StableDiffusionInpaintPipeline pipeline
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strength = data.pop("strength", 0.2)
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guidance_scale = data.pop("guidance_scale", 8.0)
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num_inference_steps = data.pop("num_inference_steps", 20)
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"""
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if(method == "smooth"):
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if encoded_image is not None:
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image = self.decode_base64_image(encoded_image)
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out = self.smooth_pipe(prompt, image=image).images[0]
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return out
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"""
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# process image
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if encoded_image is not None and encoded_mask_image is not None:
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