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import cv2
import gradio as gr
import torch
from diffusers import StableDiffusionPipeline
import numpy as np
from transformers.utils import move_cache
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Handle Transformers cache migration
move_cache()
# Initialize the Stable Diffusion pipeline
model_id = "CompVis/stable-diffusion-v1-4"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
# Load text summarizer
summarizer_model = "facebook/bart-large-cnn"
tokenizer = AutoTokenizer.from_pretrained(summarizer_model)
summarizer = AutoModelForSeq2SeqLM.from_pretrained(summarizer_model)
# Create video from images using `OpenCV`
def text_to_video(input_text, num_frames=10, fps=2):
# Summarize the input text
inputs = tokenizer(input_text, return_tensors="pt", truncation=True)
summary_ids = summarizer.generate(inputs["input_ids"], max_length=30, min_length=5, length_penalty=2.0)
prompt = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
# Generate frames
frames = []
for i in range(num_frames):
prompt_with_frame = f"{prompt}, frame {i+1}"
image = pipe(prompt_with_frame).images[0]
frames.append(np.array(image))
# Save frames as a video
height, width, layers = frames[0].shape
video_path = "output.avi"
out = cv2.VideoWriter(video_path, cv2.VideoWriter_fourcc(*'XVID'), fps, (width, height))
for frame in frames:
out.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
out.release()
return video_path
# Gradio interface
def generate_video(text, frames, fps):
video_file = text_to_video(text, num_frames=frames, fps=fps)
return video_file
interface = gr.Interface(
fn=generate_video,
inputs=[
gr.Textbox(label="Enter your text prompt"),
gr.Slider(5, 30, value=10, step=1, label="Number of Frames"),
gr.Slider(1, 10, value=2, step=1, label="Frames per Second (FPS)"),
],
outputs=gr.Video(label="Generated Video"),
title="Text-to-Video Generator",
description="Enter a text prompt to generate a short video."
)
if __name__ == "__main__":
interface.launch()
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