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import gradio as gr
import spaces
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.utils import export_to_video
import cv2
import numpy as np
pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
@spaces.GPU(duration=250)
def generate(prompt, num_inference_steps=25):
video_frames = pipe(prompt, num_inference_steps).frames
resized_frames = []
allowed_resolutions = [16, 24, 32, 40, 48, 56, 64, 128, 256, 512] # Define allowed resolutions (multiples of 8)
for frame in video_frames:
height, width, _ = frame.shape
# Find the closest allowed resolution smaller than the original
new_height = max(res for res in allowed_resolutions if res < height)
new_width = max(res for res in allowed_resolutions if res < width)
resized_frame = cv2.resize(frame, (new_width, new_height))
resized_frames.append(resized_frame)
video_path = export_to_video(np.array(resized_frames))
return video_path
prompt = gr.Textbox("Enter prompt to generate a video")
num_inference_steps = gr.Slider(10, 50, value=25)
interface = gr.Interface(
generate,
inputs=[prompt, num_inference_steps],
examples=[["Astronaut riding a horse", 25], ["Darth vader surfing in waves", 20]],
outputs="video",
cache_examples=False,
theme="soft"
).launch()
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