Create app.py

app.py

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+import gradio as gr
+import torch
+import tempfile
+from diffusers import DiffusionPipeline
+from diffusers.utils import export_to_video
+
+# Model ID specified in your request
+MODEL_ID = "DollasAndSpence/LTX2.3-ICEdit-Insight"
+
+# Load the pipeline
+# We use bfloat16 for optimized memory usage on modern GPUs
+pipe = DiffusionPipeline.from_pretrained(
+    MODEL_ID, 
+    torch_dtype=torch.bfloat16,
+)
+
+# Offload model to CPU when not actively computing to save VRAM
+pipe.enable_model_cpu_offload()
+
+def process_video(prompt, input_video_path, inference_steps, guidance_scale):
+    if not input_video_path:
+        raise gr.Error("Please provide an input video.")
+        
+    kwargs = {
+        "prompt": prompt,
+        "video": input_video_path, 
+        "num_inference_steps": inference_steps,
+        "guidance_scale": guidance_scale,
+        "output_type": "np",
+    }
+    
+    # Generate the edited/restored frames
+    # Depending on the exact wrapper, LTX may require frame rounding (e.g. 8k + 1 rule)
+    try:
+        output = pipe(**kwargs)
+    except Exception as e:
+        raise gr.Error(f"Inference failed: {str(e)}")
+    
+    # Export the processed frames to a temporary mp4 file
+    out_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
+    export_to_video(output.frames[0], out_file, fps=24)
+    
+    return out_file
+
+# Build the Gradio UI
+with gr.Blocks(title="LTX2.3 ICEdit-Insight Video Restoration") as demo:
+    gr.Markdown(f"# 🎥 {MODEL_ID} Video Editor")
+    gr.Markdown("Upload a video and provide an instruction to remove AI glitches, watermarks, subtitles, or enhance HD details.")
+    
+    with gr.Row():
+        with gr.Column():
+            input_vid = gr.Video(label="Input Video (To be fixed/edited)")
+            prompt = gr.Textbox(
+                label="Editing Instruction", 
+                placeholder="e.g., Clean up AI inpainting glitches, remove watermarks, enhance details..."
+            )
+            
+            with gr.Accordion("Advanced Settings", open=False):
+                steps = gr.Slider(minimum=1, maximum=50, value=30, step=1, label="Inference Steps")
+                cfg = gr.Slider(minimum=1.0, maximum=15.0, value=4.0, step=0.1, label="Guidance Scale")
+                
+            run_btn = gr.Button("Generate Fix", variant="primary")
+            
+        with gr.Column():
+            output_vid = gr.Video(label="Restored Output Video")
+            
+    run_btn.click(
+        fn=process_video,
+        inputs=[prompt, input_vid, steps, cfg],
+        outputs=output_vid
+    )
+
+if __name__ == "__main__":
+    demo.launch()