Revert to fork code
Browse files- handler.py +3 -7
handler.py
CHANGED
@@ -76,15 +76,11 @@ class EndpointHandler():
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"""
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prompt = data.pop("inputs", None)
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image = data.pop("image", None)
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-
count = data.pop("count", None)
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controlnet_type = data.pop("controlnet_type", None)
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# Check if neither prompt nor image is provided
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if prompt is None and image is None:
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return {"error": "Please provide a prompt and base64 encoded image."}
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-
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if count is None:
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return {"error": "Please provide a count."}
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# Check if a new controlnet is provided
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if controlnet_type is not None and controlnet_type != self.control_type:
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@@ -114,7 +110,7 @@ class EndpointHandler():
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image=control_image,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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-
num_images_per_prompt=
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height=height,
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width=width,
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controlnet_conditioning_scale=controlnet_conditioning_scale,
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@@ -123,11 +119,11 @@ class EndpointHandler():
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# return first generate PIL image
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return out.images[0
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# helper to decode input image
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def decode_base64_image(self, image_string):
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base64_image = base64.b64decode(image_string)
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buffer = BytesIO(base64_image)
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image = Image.open(buffer)
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return image
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"""
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prompt = data.pop("inputs", None)
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image = data.pop("image", None)
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controlnet_type = data.pop("controlnet_type", None)
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# Check if neither prompt nor image is provided
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if prompt is None and image is None:
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return {"error": "Please provide a prompt and base64 encoded image."}
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# Check if a new controlnet is provided
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if controlnet_type is not None and controlnet_type != self.control_type:
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image=control_image,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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+
num_images_per_prompt=1,
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height=height,
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width=width,
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controlnet_conditioning_scale=controlnet_conditioning_scale,
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# return first generate PIL image
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return out.images[0]
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# helper to decode input image
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def decode_base64_image(self, image_string):
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base64_image = base64.b64decode(image_string)
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buffer = BytesIO(base64_image)
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image = Image.open(buffer)
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+
return image
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