import os os.system("pip install torch") os.system("pip install diffusers") os.system("python -m pip install --upgrade pip") os.system("pip install imageio") os.system("pip install numpy") os.system("pip install transformers") ''' import torch from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler from diffusers.utils import export_to_video import gradio as gr 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() def text_video(prompt): video_frames = pipe(prompt, num_inference_steps=25).frames video_path = export_to_video(video_frames) result = gr.Video(label="Generated Video") gr.Interface( fn=text_video, inputs=gr.Textbox(label="어떤 비디오를 생성할까요? : "), outputs=result ).launch()''' import torch import imageio from diffusers import TextToVideoZeroPipeline import numpy as np model_id = "runwayml/stable-diffusion-v1-5" pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") seed = 0 video_length = 8 chunk_size = 4 prompt = "A panda is playing guitar on times square" # Generate the video chunk-by-chunk result = [] chunk_ids = np.arange(0, video_length, chunk_size - 1) generator = torch.Generator(device="cuda") for i in range(len(chunk_ids)): print(f"Processing chunk {i + 1} / {len(chunk_ids)}") ch_start = chunk_ids[i] ch_end = video_length if i == len(chunk_ids) - 1 else chunk_ids[i + 1] # Attach the first frame for Cross Frame Attention frame_ids = [0] + list(range(ch_start, ch_end)) # Fix the seed for the temporal consistency generator.manual_seed(seed) output = pipe(prompt=prompt, video_length=len(frame_ids), generator=generator, frame_ids=frame_ids) result.append(output.images[1:]) # Concatenate chunks and save result = np.concatenate(result) result = [(r * 255).astype("uint8") for r in result] imageio.mimsave("video.mp4", result, fps=4)