Update app.py

app.py

@@ -1,34 +1,55 @@
-import gradio as gr
 import torch
-from diffusers import DiffusionPipeline
-
-# Load the WAN 2.1 T2V Model
-
-from diffusers import DiffusionPipeline
-
-from diffusers import DiffusionPipeline
-
-from diffusers import DiffusionPipeline
-
-from diffusers import DiffusionPipeline
-
-pipe = DiffusionPipeline.from_pretrained("sarthak247/Wan2.1-T2V-1.3B-nf4")
-
-prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
-image = pipe(prompt).images[0]
-def generate_image(prompt):
-    """Generates an image from text prompt using WAN 2.1"""
-    image = pipe(prompt).images[0]
-    return image
-
-# Create Gradio UI
-interface = gr.Interface(
-    fn=generate_image,
-    inputs=gr.Textbox(label="Enter Prompt"),
-    outputs=gr.Image(label="Generated Image"),
-    title="WAN 2.1 - Text-to-Image Generation",
-    description="Generate images from text using WAN 2.1 T2V model."
+import gradio as gr
+import imageio
+import os
+from safetensors.torch import load_file
+from torchvision import transforms
+from PIL import Image
+import numpy as np
+
+# Define model path (assuming it's in the HF Space)
+MODEL_PATH = "sarthak247/Wan2.1-T2V-1.3B-nf4"
+MODEL_FILE = f"{MODEL_PATH}/diffusion_pytorch_model.safetensors"
+
+# Load model weights manually
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Loading model on {device}...")
+
+try:
+    model_weights = load_file(MODEL_FILE, device=device)
+    print("Model loaded successfully!")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    model_weights = None
+
+# Placeholder function - Replace with actual inference logic
+def generate_video(prompt):
+    """
+    Generates a placeholder video using the model.
+    Replace this function with the actual inference logic once available.
+    """
+    if model_weights is None:
+        return "Model failed to load. Please check the logs."
+
+    # Simulate an image output (Replace this with actual video frame generation)
+    img = Image.new("RGB", (512, 512), color="black")
+    transform = transforms.ToTensor()
+    frame = (transform(img).permute(1, 2, 0).numpy() * 255).astype(np.uint8)
+
+    # Create a fake video with repeated frames
+    frames = [frame] * 16  # 16 repeated frames (Replace with actual video frames)
+    output_path = "output.mp4"
+    imageio.mimsave(output_path, frames, fps=8)
+
+    return output_path
+
+# Gradio UI
+iface = gr.Interface(
+    fn=generate_video,
+    inputs=gr.Textbox(label="Enter Text Prompt"),
+    outputs=gr.Video(label="Generated Video"),
+    title="Wan2.1-T2V-1.3B Video Generation",
+    description="This app loads the model manually and generates text-to-video output."
 )
 
-# Launch the app
-interface.launch()
+iface.launch()