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Update app.py
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app.py
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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
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from diffusers import UniPCMultistepScheduler
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from diffusers.utils import load_image
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import gradio as gr
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import torch
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# Constants
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low_threshold = 100
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high_threshold = 200
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# Models
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controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
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pipe = StableDiffusionControlNetPipeline.from_pretrained(
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"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
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)
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pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
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# This command loads the individual model components on GPU on-demand. So, we don't
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# need to explicitly call pipe.to("cuda").
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pipe.enable_model_cpu_offload()
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# Generator seed,
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generator = torch.manual_seed(0)
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def get_canny_filter(image):
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if not isinstance(image, np.ndarray):
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image = np.array(image)
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image = cv2.Canny(image, low_threshold, high_threshold)
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image = image[:, :, None]
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image = np.concatenate([image, image, image], axis=2)
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canny_image = Image.fromarray(image)
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return canny_image
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def generate_images(image, prompt):
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canny_image = get_canny_filter(image)
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output = pipe(
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prompt,
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canny_image,
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generator=generator,
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num_images_per_prompt=3
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)
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return output.images
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gr.Interface(
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generate_images,
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inputs=[
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gr.Image(type="pil"),
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gr.Textbox(
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label="Enter your prompt",
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max_lines=1,
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placeholder="Sandra Oh, best quality, extremely detailed",
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),
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],
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outputs=gr.Gallery().style(grid=[2], height="auto"),
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title="Generate controlled outputs with ControlNet and Stable Diffusion. ",
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description="This Space uses Canny edge maps as the additional conditioning.",
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examples=[["input_image_vermeer.png", "Sandra Oh, best quality, extremely detailed"]],
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allow_flagging=False,
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).launch(enable_queue=True)
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