Update app.py
Browse filesUpdate background color and description
app.py
CHANGED
@@ -66,14 +66,15 @@ def infer(prompts, negative_prompts, image):
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output=np.array(output, dtype=np.float32)
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return output
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with gr.Blocks(
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gr.Markdown(
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"""
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<h1 style="text-align: center;">
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Animal Pose Control Net
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</h1>
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<h3 style="text-align:
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</h3>
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""")
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with gr.Row():
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@@ -82,7 +83,7 @@ with gr.Blocks(theme='kfahn/AnimalPose') as demo:
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negative_prompts = gr.Textbox(label="Negative Prompt", value="lowres, two heads, bad muzzle, bad anatomy, missing ears, missing paws")
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conditioning_image = gr.Image(label="Conditioning Image")
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run_btn = gr.Button("Run")
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wandb = wandb_report(report_url)
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with gr.Column():
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keypoint_tool = addp5sketch(sketch_url)
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output = gr.Image(
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@@ -93,7 +94,7 @@ with gr.Blocks(theme='kfahn/AnimalPose') as demo:
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[Dataset](https://huggingface.co/datasets/JFoz/dog-poses-controlnet-dataset)
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[Diffusers model](https://huggingface.co/JFoz/dog-pose)
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[Github](https://github.com/fi4cr/animalpose)
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[Training Report](https://wandb.ai/john-fozard/dog-cat-pose/runs/kmwcvae5)
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""")
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run_btn.click(fn=infer, inputs = [prompts, negative_prompts, conditioning_image], outputs = output)
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output=np.array(output, dtype=np.float32)
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return output
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with gr.Blocks(css=".gradio-container {background-color: #45f10c};") as demo:
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gr.Markdown(
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"""
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<h1 style="text-align: center;">
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π Animal Pose Control Net π
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</h1>
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<h3 style="text-align: left;"> This is a demo of Animal Pose ControlNet, which is a model trained on runwayml/stable-diffusion-v1-5 with a new type of conditioning.</h3>
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<h3 style="text-align: left;"> While this is definitely a work in progress, you can still try it out by using the p5 sketch to create a keypoint image and using it as the conditioning image.</h3>
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<h3 style="text-align: left;"> The model was generated as part of the Hugging Face Jax Diffusers sprint. Thank you to both Hugging Face and Google Cloud who provided the TPUs for training!
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</h3>
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""")
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with gr.Row():
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negative_prompts = gr.Textbox(label="Negative Prompt", value="lowres, two heads, bad muzzle, bad anatomy, missing ears, missing paws")
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conditioning_image = gr.Image(label="Conditioning Image")
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run_btn = gr.Button("Run")
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#wandb = wandb_report(report_url)
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with gr.Column():
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keypoint_tool = addp5sketch(sketch_url)
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output = gr.Image(
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[Dataset](https://huggingface.co/datasets/JFoz/dog-poses-controlnet-dataset)
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[Diffusers model](https://huggingface.co/JFoz/dog-pose)
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[Github](https://github.com/fi4cr/animalpose)
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[WANDB Training Report](https://wandb.ai/john-fozard/dog-cat-pose/runs/kmwcvae5)
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""")
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run_btn.click(fn=infer, inputs = [prompts, negative_prompts, conditioning_image], outputs = output)
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