add blocks interface

app.py

@@ -221,8 +221,50 @@ def inference(text, init_image, skip_timesteps, clip_guidance_scale, tv_scale, r
     writer.close()
     return img, 'video.mp4'
     
-title = "CLIP Guided Diffusion Faces Model"
-description = "Gradio demo for CLIP Guided Diffusion. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'> Comics faces model by <a href='https://linktree/devdef'>Alex Spirin</a>. Based on the original <a href='https://huggingface.co/spaces/EleutherAI/clip-guided-diffusion'>CLIP Guided Diffusion Space</a> by akhaliq / Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings) | <a href='https://github.com/Sxela/DiscoDiffusion-Warp/blob/main/Disco_Diffusion_v5_2_Warp_custom_model.ipynb' target='_blank'>Colab</a></p>"
-iface = gr.Interface(inference, inputs=["text",gr.inputs.Image(type="file", label='initial image (optional)', optional=True),gr.inputs.Slider(minimum=0, maximum=45, step=1, default=10, label="skip_timesteps"), gr.inputs.Slider(minimum=0, maximum=3000, step=1, default=600, label="clip guidance scale (Controls how much the image should look like the prompt)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=0, label="tv_scale (Controls the smoothness of the final output)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=0, label="range_scale (Controls how far out of range RGB values are allowed to be)"), gr.inputs.Slider(minimum=0, maximum=1000, step=1, default=0, label="init_scale (This enhances the effect of the init image)"), gr.inputs.Number(default=0, label="Seed"), gr.inputs.Image(type="file", label='image prompt (optional)', optional=True), gr.inputs.Slider(minimum=50, maximum=500, step=1, default=50, label="timestep respacing"),gr.inputs.Slider(minimum=1, maximum=64, step=1, default=32, label="cutn")], outputs=["image","video"], title=title, description=description, article=article, examples=[["Brad Pitt", "face.jpeg", 0, 1000, 150, 50, 0, 0, "face.jpeg", 90, 32]])
-iface.launch()
+demo = gr.Blocks()
+with demo:
+    gr.Markdown(
+    """
+    # CLIP Guided Diffusion Faces Model 
+    ### by [Alex Spirin](https://linktr.ee/devdef)
+    Gradio Blocks demo for CLIP Guided Diffusion. To use it, simply add your text, or click one of the examples to load them.
+    Based on the original [Space](https://huggingface.co/spaces/EleutherAI/clip-guided-diffusion) by akhaliq. 
+    """)
+
+    with gr.Row():
+      text = gr.Textbox(placeholder="Enter a description of a face", label='Text prompt')
+    with gr.Row():
+      with gr.Column():
+       clip_guidance_scale = gr.Slider(minimum=0, maximum=3000, step=1, value=600, label="Prompt strength")
+       tv_scale = gr.Slider(minimum=0, maximum=1000, step=1, value=0, label="Smoothness")
+       range_scale = gr.Slider(minimum=0, maximum=1000, step=1, value=0, label="Compress color range")
+
+      with gr.Column():
+        timestep_respacing = gr.Slider(minimum=25, maximum=100, step=1, value=50, label="timestep respacing")
+        cutn = gr.Slider(minimum=4, maximum=32, step=1, value=16, label="cutn")
+        seed = gr.Number(value=0, label="Seed")
+    with gr.Row():
+       with gr.Column():
+        init_image = gr.Image(source="upload", label='initial image (optional)')
+        init_scale = gr.Slider(minimum=0, maximum=45, step=1, value=10, label="Look like the image above")
+
+       with gr.Column():
+        image_prompts = gr.Image(source="upload", label='image prompt (optional)')
+        skip_timesteps = gr.Slider(minimum=0, maximum=1000, step=1, value=0, label="Look like the image above")
+
+    with gr.Row():                          
+      run_button = gr.Button("Run!")
+    with gr.Row(): 
+      gr.Markdown(
+    """
+      # Results
+    """)
+    with gr.Row():
+      output_image = gr.Image(label='Output image', type='numpy')
+      output_video = gr.Video(label='Output video')
+
+    outputs=[output_image,output_video]
+    
+    run_button.click(inference, inputs=[text, init_image, skip_timesteps, clip_guidance_scale, tv_scale, range_scale, init_scale, seed, image_prompts,timestep_respacing, cutn], outputs=outputs)
+
+demo.launch(enable_queue=True)