Create app.py

app.py

@@ -0,0 +1,58 @@
+import os
+import gradio as gr
+from diffusers import DiffusionPipeline
+import torch
+
+# === Configure cache directory ===
+cache_dir = os.path.expanduser("~/Downloads/Openking")
+os.makedirs(cache_dir, exist_ok=True)
+
+# Set Hugging Face cache environment variables
+os.environ["HF_HOME"] = cache_dir
+os.environ["HF_HUB_CACHE"] = cache_dir
+os.environ["HF_DATASETS_CACHE"] = cache_dir
+
+# === Load Hugging Face token from secrets (required for private models) ===
+# In Hugging Face Spaces, store your token as a secret named "HF_TOKEN"
+hf_token = os.getenv("HF_TOKEN")
+if not hf_token:
+    raise ValueError("Please set your Hugging Face token as a secret named 'HF_TOKEN' in your Space settings.")
+
+# === Load the model ===
+model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+
+try:
+    pipe = DiffusionPipeline.from_pretrained(
+        model_id,
+        use_auth_token=hf_token,
+        cache_dir=cache_dir,
+        torch_dtype=torch.float16,
+        variant="fp16"
+    )
+    pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")
+except Exception as e:
+    raise RuntimeError(f"Failed to load model: {e}")
+
+# === Gradio interface ===
+def generate_video(prompt: str, num_inference_steps: int = 50):
+    try:
+        # Note: Adjust this call based on the actual model's inference API.
+        # Since this is a text-to-video model, the exact method may vary.
+        # This is a placeholder—check the model card for correct usage.
+        video_frames = pipe(prompt, num_inference_steps=num_inference_steps).frames
+        # For now, return a placeholder message
+        return f"Generated video for: '{prompt}' with {num_inference_steps} steps. (Output handling depends on model output format.)"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🎥 Wan2.1 Text-to-Video Generator")
+    prompt = gr.Textbox(label="Prompt", placeholder="A cat flying through space...")
+    steps = gr.Slider(10, 100, value=50, label="Inference Steps")
+    output = gr.Textbox(label="Result")
+    btn = gr.Button("Generate Video")
+    btn.click(generate_video, inputs=[prompt, steps], outputs=output)
+
+# Launch app
+if __name__ == "__main__":
+    demo.launch()