frame_gen.py

import torch
from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter
from diffusers.utils import export_to_gif
from moviepy.editor import VideoFileClip, concatenate_videoclips

adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)

pipeline = AnimateDiffPipeline.from_pretrained("emilianJR/epiCRealism", motion_adapter=adapter, torch_dtype=torch.float16)
scheduler = DDIMScheduler.from_pretrained(
    "emilianJR/epiCRealism",
    subfolder="scheduler",
    clip_sample=False,
    timestep_spacing="linspace",
    beta_schedule="linear",
    steps_offset=1,
)
pipeline.scheduler = scheduler
pipeline.enable_vae_slicing()
#pipeline.enable_model_cpu_offload()


def gen_movie(frames_desc):
    frames_desc = frames_desc[0]
    x = 1
    for frame_description in frames_desc:
        output = pipeline(
            prompt="frame_description",
            negative_prompt="high resolution",
            num_frames=16,
            guidance_scale=7.5,
            num_inference_steps=50,
            generator=torch.Generator("cpu").manual_seed(0),
        )
        frames = output.frames[0]
        export_to_gif(frames, f'animation{x}.gif')
        x += 1

    
    # List to store VideoFileClip objects for each input GIF
    video_clips = []
    
    # Load each input GIF file and append it to the list
    for x in range(1,2):
        video_clips.append(VideoFileClip(f'animation{x}.gif'))
    
    # Concatenate the video clips to create a single video
    final_clip = concatenate_videoclips(video_clips)
    
    # Export the final video to a new MP4 file
    final_clip.write_videofile("combined_video.mp4", codec="libx264", fps=24)