Mirrors/AnimateDiff
1
0
Fork 0
mirror of https://github.com/guoyww/AnimateDiff.git synced 2026-04-03 09:46:36 +02:00
Code Issues Packages Projects Releases Wiki Activity

optimize memory cost

Browse Source
This commit is contained in:
Yuwei Guo
2023-07-12 16:41:08 +08:00
parent 41a698ae8e
commit 05fdf470ad
2 changed files with 10 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

7
animatediff/pipelines/pipeline_animation.py
View File

@@ -6,6 +6,7 @@ from dataclasses import dataclass
import numpy as np import numpy as np
import torch import torch
from tqdm import tqdm
from diffusers.utils import is_accelerate_available from diffusers.utils import is_accelerate_available
from packaging import version from packaging import version
@@ -239,7 +240,11 @@ class AnimationPipeline(DiffusionPipeline):
video_length = latents.shape[2] video_length = latents.shape[2]
latents = 1 / 0.18215 * latents latents = 1 / 0.18215 * latents
latents = rearrange(latents, "b c f h w -> (b f) c h w") latents = rearrange(latents, "b c f h w -> (b f) c h w")
video = self.vae.decode(latents).sample # video = self.vae.decode(latents).sample
video = []
for frame_idx in tqdm(range(latents.shape[0])):
video.append(self.vae.decode(latents[frame_idx:frame_idx+1]).sample)
video = torch.cat(video)
video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length) video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
video = (video / 2 + 0.5).clamp(0, 1) video = (video / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16

4
scripts/animate.py
View File

@@ -17,6 +17,7 @@ from animatediff.pipelines.pipeline_animation import AnimationPipeline
from animatediff.utils.util import save_videos_grid from animatediff.utils.util import save_videos_grid
from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint
from animatediff.utils.convert_lora_safetensor_to_diffusers import convert_lora from animatediff.utils.convert_lora_safetensor_to_diffusers import convert_lora
from diffusers.utils.import_utils import is_xformers_available
from einops import rearrange, repeat from einops import rearrange, repeat
@@ -51,6 +52,9 @@ def main(args):
vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae") vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae")
unet = UNet3DConditionModel.from_pretrained_2d(args.pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs)) unet = UNet3DConditionModel.from_pretrained_2d(args.pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs))
if is_xformers_available(): unet.enable_xformers_memory_efficient_attention()
else: assert False
pipeline = AnimationPipeline( pipeline = AnimationPipeline(
vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs)), scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs)),