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import gradio as gr
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.utils import export_to_video
import uuid
# Model Yükleme
model_id = "vdo/zeroscope_v2_576w"
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.to("cpu")
def generate_video(prompt):
try:
# Kaliteyi koruyalım (Patronun istediği gibi net olsun)
# num_inference_steps=20 (Dengeli kalite)
frames = pipe(
prompt,
num_inference_steps=20,
height=320,
width=576,
num_frames=16
).frames
output_filename = f"viral_{uuid.uuid4()}.mp4"
export_to_video(frames[0], output_filename, fps=8)
# SADECE dosya adını döndür, Gradio bunu otomatik URL'ye çevirir
return output_filename
except Exception as e:
print(f"HATA: {str(e)}")
return None
# API İsmi: predict
demo = gr.Interface(fn=generate_video, inputs="text", outputs="video", api_name="predict")
demo.launch() |