Create app.py

app.py

@@ -0,0 +1,107 @@
+import os
+import torch
+import gradio as gr
+import spaces  # Critical for ZeroGPU support in Spaces
+from diffusers import DiffusionPipeline  # Common for HF video models
+from PIL import Image
+
+# =============================================================
+# INITIALIZATION & PIPELINE CONFIGURATION
+# =============================================================
+MODEL_ID = "MiniMaxAI/MiniMax-M1"
+
+def load_pipeline():
+    # Load model with bfloat16 for modern GPU efficiency [17]
+    # Note: Ensure the model is available on the HF Hub or adjust path
+    pipe = DiffusionPipeline.from_pretrained(
+        MODEL_ID, 
+        torch_dtype=torch.bfloat16,
+        use_safetensors=True
+    )
+    
+    # Critical VRAM optimizations for deployment on 24GB or ZeroGPU [2]
+    pipe.enable_model_cpu_offload() 
+    pipe.enable_vae_slicing()
+    return pipe
+
+# Pipeline is initialized globally to avoid reloads on every click
+pipe = load_pipeline()
+
+# =============================================================
+# GENERATION LOGIC
+# =============================================================
+@spaces.GPU(duration=300)  # Allocated GPU time for complex generation [16]
+def generate_video(prompt, negative_prompt, steps, guidance_scale, seed):
+    # Standard 5-second video length at 16 FPS [18-20]
+    num_frames = 81 
+    
+    # Use Generator for deterministic results [21, 22]
+    generator = torch.Generator("cuda").manual_seed(int(seed))
+    
+    # Execution using VACE-style inputs [8, 23]
+    output = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=int(steps),
+        guidance_scale=float(guidance_scale),
+        num_frames=num_frames,
+        generator=generator
+    ).frames
+    
+    # Exporting generated frames to a video file [24, 25]
+    # For a real app, use diffusers.utils.export_to_video
+    from diffusers.utils import export_to_video
+    import tempfile
+    
+    temp_path = tempfile.mktemp(suffix=".mp4")
+    export_to_video(output, temp_path, fps=16)
+    return temp_path
+
+# =============================================================
+# UI DESIGN (Using gr.Blocks for Professional Layout)
+# =============================================================
+# CSS for custom styling to improve "Joyful Experience" [26, 27]
+css = """
+.container { max-width: 1000px; margin: auto; }
+.gen_btn { background-color: #7224f2 !important; color: white !important; }
+"""
+
+with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
+    gr.Markdown(f"# {MODEL_ID} High-Fidelity Video Generator")
+    gr.Markdown("Leveraging ZeroGPU and VRAM offloading for cinematic AI video [2, 28].")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            prompt_input = gr.Textbox(
+                label="Prompt", 
+                placeholder="Describe the action and scene details...",
+                lines=4
+            )
+            neg_prompt = gr.Textbox(
+                label="Negative Prompt", 
+                value="blurry, distorted, low quality, watermark, text"
+            )
+            
+            with gr.Accordion("Advanced Settings", open=False):
+                steps = gr.Slider(20, 50, value=30, step=1, label="Inference Steps")
+                guidance = gr.Slider(1.0, 15.0, value=7.0, label="Guidance Scale")
+                seed = gr.Number(value=42, label="Seed")
+            
+            generate_btn = gr.Button("Generate Video", variant="primary", elem_classes="gen_btn")
+            
+        with gr.Column(scale=1):
+            video_output = gr.Video(label="Generated Output")
+
+    # Event listener mapping [29, 30]
+    generate_btn.click(
+        fn=generate_video,
+        inputs=[prompt_input, neg_prompt, steps, guidance, seed],
+        outputs=video_output
+    )
+
+# =============================================================
+# LAUNCH
+# =============================================================
+if __name__ == "__main__":
+    # Ensure app.py is at the root for automatic HF detection [14, 15]
+    demo.launch()