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app.py

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+from flask import Flask, request, jsonify, send_file
+import torch
+from diffusers.utils import export_to_video
+from diffusers import AutoencoderKLWan, WanPipeline
+from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
+import os
+from uuid import uuid4
+
+app = Flask(__name__)
+
+# Load the model once at startup
+model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+scheduler = UniPCMultistepScheduler(
+    prediction_type='flow_prediction',
+    use_flow_sigmas=True,
+    num_train_timesteps=1000,
+    flow_shift=5.0
+)
+pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
+pipe.scheduler = scheduler
+pipe.to("cuda")
+
+@app.route('/generate_video', methods=['POST'])
+def generate_video():
+    data = request.json
+    prompt = data.get('prompt')
+    negative_prompt = data.get('negative_prompt', '')
+    height = data.get('height', 720)
+    width = data.get('width', 1280)
+    num_frames = data.get('num_frames', 81)
+    guidance_scale = data.get('guidance_scale', 5.0)
+
+    output = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        height=height,
+        width=width,
+        num_frames=num_frames,
+        guidance_scale=guidance_scale,
+    ).frames[0]
+
+    output_filename = f"{uuid4()}.mp4"
+    output_path = os.path.join("outputs", output_filename)
+    os.makedirs("outputs", exist_ok=True)
+    export_to_video(output, output_path, fps=16)
+
+    return send_file(output_path, mimetype='video/mp4')
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000)

requirements.txt

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+flask
+torch
+diffusers
+imageio
+modelscope
+accelerate
+transformers
+scipy
+safetensors
+numpy
+opencv-python
+pillow
+requests
+tqdm
+gradio
+scikit-image
+tensorflow