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Update app.py
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app.py
CHANGED
@@ -31,14 +31,42 @@ pipe = StableDiffusion3CommonPipeline.from_pretrained(
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pipe.to('cuda:0', torch.float16)
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def infer(image_in, prompt):
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prompt = 'Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see a big rain approaching. text "InstantX" on image'
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n_prompt = 'NSFW, nude, naked, porn, ugly'
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# controlnet config
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controlnet_conditioning = [
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dict(
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control_index=0,
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control_image=
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control_weight=0.7,
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control_pooled_projections='zeros'
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)
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)
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pipe.to('cuda:0', torch.float16)
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def resize_image(input_path, output_path, target_height):
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# Open the input image
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img = Image.open(input_path)
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# Calculate the aspect ratio of the original image
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original_width, original_height = img.size
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original_aspect_ratio = original_width / original_height
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# Calculate the new width while maintaining the aspect ratio and the target height
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new_width = int(target_height * original_aspect_ratio)
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# Resize the image while maintaining the aspect ratio and fixing the height
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img = img.resize((new_width, target_height), Image.LANCZOS)
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# Save the resized image
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img.save(output_path)
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return output_path
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def infer(image_in, prompt):
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prompt = 'Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see a big rain approaching. text "InstantX" on image'
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n_prompt = 'NSFW, nude, naked, porn, ugly'
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image_to_canny = load_image(image_in)
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image_to_canny = np.array(image_to_canny)
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image_to_canny = cv2.Canny(image_to_canny, 100, 200)
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image_to_canny = image_to_canny[:, :, None]
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image_to_canny = np.concatenate([image_to_canny, image_to_canny, image_to_canny], axis=2)
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image_to_canny = Image.fromarray(image_to_canny)
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# controlnet config
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controlnet_conditioning = [
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dict(
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control_index=0,
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control_image=image_to_canny,
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control_weight=0.7,
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control_pooled_projections='zeros'
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)
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