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from diffsynth import ModelManager, SDImagePipeline, SDVideoPipeline, save_video, download_models
from diffsynth.extensions.RIFE import RIFEInterpolater
import torch
# Download models (automatically)
# `models/stable_diffusion/dreamshaper_8.safetensors`: [link](https://civitai.com/api/download/models/128713?type=Model&format=SafeTensor&size=pruned&fp=fp16)
# `models/AnimateDiff/mm_sd_v15_v2.ckpt`: [link](https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15_v2.ckpt)
# `models/RIFE/flownet.pkl`: [link](https://drive.google.com/file/d/1APIzVeI-4ZZCEuIRE1m6WYfSCaOsi_7_/view?usp=sharing)
download_models(["DreamShaper_8", "AnimateDiff_v2", "RIFE"])
# Load models
model_manager = ModelManager(torch_dtype=torch.float16, device="cuda")
model_manager.load_models([
"models/stable_diffusion/dreamshaper_8.safetensors",
"models/AnimateDiff/mm_sd_v15_v2.ckpt",
"models/RIFE/flownet.pkl"
])
# Text -> Image
pipe_image = SDImagePipeline.from_model_manager(model_manager)
torch.manual_seed(0)
image = pipe_image(
prompt = "lightning storm, sea",
negative_prompt = "",
cfg_scale=7.5,
num_inference_steps=30, height=512, width=768,
)
# Text + Image -> Video (6GB VRAM is enough!)
pipe = SDVideoPipeline.from_model_manager(model_manager)
output_video = pipe(
prompt = "lightning storm, sea",
negative_prompt = "",
cfg_scale=7.5,
num_frames=64,
num_inference_steps=10, height=512, width=768,
animatediff_batch_size=16, animatediff_stride=1, input_frames=[image]*64, denoising_strength=0.9,
vram_limit_level=0,
)
# Video -> Video with high fps
interpolater = RIFEInterpolater.from_model_manager(model_manager)
output_video = interpolater.interpolate(output_video, num_iter=3)
# Save images and video
save_video(output_video, "output_video.mp4", fps=120)
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