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from flask import Flask, request, jsonify, send_from_directory
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from diffusers import DiffusionPipeline
import torch
import imageio
import uuid
import os
app = Flask(__name__)
# Load models once
modelscope_model = pipeline(Tasks.text_to_video_synthesis, model='damo-vilab/modelscope-text-to-video-synthesis')
zeroscope_model = DiffusionPipeline.from_pretrained(
"cerspense/zeroscope_v2_576w",
torch_dtype=torch.float16
).to("cuda")
# Output directory (for temporary files)
os.makedirs("output", exist_ok=True)
# Serve the generated video dynamically (no permanent storage)
@app.route('/generate-video', methods=['POST'])
def generate_video():
data = request.get_json()
model_type = data.get("model", "").lower()
if not model_type:
return jsonify({"error": "Model is required: 'modelscope' or 'zeroscope'"}), 400
params = data.get("params", {})
prompt = params.get("prompt")
if not prompt:
return jsonify({"error": "Prompt is required in 'params'"}), 400
# Common settings
seed = params.get("seed", 42)
output_filename = f"{uuid.uuid4().hex}.mp4"
output_path = os.path.join("output", output_filename)
try:
if model_type == "modelscope":
# Extract ModelScope-specific parameters
result = modelscope_model({
"text": prompt,
"output_path": output_path,
"seed": seed,
"fps": params.get("fps", 8),
"num_frames": params.get("num_frames", 16),
"batch_size": params.get("batch_size", 1),
"decode_audio": params.get("decode_audio", False),
"resolution": params.get("resolution", "512x512")
})
video_path = result["output_path"]
elif model_type == "zeroscope":
# Extract Zeroscope-specific parameters
inference_steps = params.get("num_inference_steps", 25)
height = params.get("height", 320)
width = params.get("width", 576)
fps = params.get("fps", 8)
generator = torch.manual_seed(seed)
with torch.autocast("cuda"):
output = zeroscope_model(
prompt,
num_inference_steps=inference_steps,
height=height,
width=width,
generator=generator
)
video_frames = output.frames
# Save as video
imageio.mimsave(output_path, video_frames, fps=fps)
video_path = output_path
else:
return jsonify({"error": f"Unsupported model type: {model_type}"}), 400
# Return a URL to the generated video file
video_url = f"/download-video/{output_filename}"
return jsonify({
"success": True,
"model": model_type,
"video_url": video_url,
"filename": output_filename
})
except Exception as e:
return jsonify({
"success": False,
"error": str(e)
}), 500
# Endpoint to download the generated video
@app.route('/download-video/<filename>', methods=['GET'])
def download_video(filename):
try:
return send_from_directory("output", filename, as_attachment=True)
except FileNotFoundError:
return jsonify({"error": "Video file not found"}), 404
if __name__ == '__main__':
app.run(host='0.0.0.0', port=7860)
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