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negative_prompt="bad quality, worse quality", |
num_frames=16, |
guidance_scale=7.5, |
num_inference_steps=25, |
generator=torch.Generator("cpu").manual_seed(42), |
) |
frames = output.frames[0] |
export_to_gif(frames, "animation.gif") |
masterpiece, bestquality, sunset. |
Using Motion LoRAs with PEFT You can also leverage the PEFT backend to combine Motion LoRA’s and create more complex animations. First install PEFT with Copied pip install peft Then you can use the following code to combine Motion LoRAs. Copied import torch |
from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter |
from diffusers.utils import export_to_gif |
# Load the motion adapter |
adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16) |
# load SD 1.5 based finetuned model |
model_id = "SG161222/Realistic_Vision_V5.1_noVAE" |
pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16) |
pipe.load_lora_weights( |
"diffusers/animatediff-motion-lora-zoom-out", adapter_name="zoom-out", |
) |
pipe.load_lora_weights( |
"diffusers/animatediff-motion-lora-pan-left", adapter_name="pan-left", |
) |
pipe.set_adapters(["zoom-out", "pan-left"], adapter_weights=[1.0, 1.0]) |
scheduler = DDIMScheduler.from_pretrained( |
model_id, |
subfolder="scheduler", |
clip_sample=False, |
timestep_spacing="linspace", |
beta_schedule="linear", |
steps_offset=1, |
) |
pipe.scheduler = scheduler |
# enable memory savings |
pipe.enable_vae_slicing() |
pipe.enable_model_cpu_offload() |
output = pipe( |
prompt=( |
"masterpiece, bestquality, highlydetailed, ultradetailed, sunset, " |
"orange sky, warm lighting, fishing boats, ocean waves seagulls, " |
"rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, " |
"golden hour, coastal landscape, seaside scenery" |
), |
negative_prompt="bad quality, worse quality", |
num_frames=16, |
guidance_scale=7.5, |
num_inference_steps=25, |
generator=torch.Generator("cpu").manual_seed(42), |
) |
frames = output.frames[0] |
export_to_gif(frames, "animation.gif") |
masterpiece, bestquality, sunset. |
Using FreeInit FreeInit: Bridging Initialization Gap in Video Diffusion Models by Tianxing Wu, Chenyang Si, Yuming Jiang, Ziqi Huang, Ziwei Liu. FreeInit is an effective method that improves temporal consistency and overall quality of videos generated using video-diffusion-models without any addition training. It can be applied to AnimateDiff, ModelScope, VideoCrafter and various other video generation models seamlessly at inference time, and works by iteratively refining the latent-initialization noise. More details can be found it the paper. The following example demonstrates the usage of FreeInit. Copied import torch |
from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler |
from diffusers.utils import export_to_gif |
adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2") |
model_id = "SG161222/Realistic_Vision_V5.1_noVAE" |
pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16).to("cuda") |
pipe.scheduler = DDIMScheduler.from_pretrained( |
model_id, |
subfolder="scheduler", |
beta_schedule="linear", |
clip_sample=False, |
timestep_spacing="linspace", |
steps_offset=1 |
) |
# enable memory savings |
pipe.enable_vae_slicing() |
pipe.enable_vae_tiling() |
# enable FreeInit |
# Refer to the enable_free_init documentation for a full list of configurable parameters |
pipe.enable_free_init(method="butterworth", use_fast_sampling=True) |
# run inference |
output = pipe( |
prompt="a panda playing a guitar, on a boat, in the ocean, high quality", |
negative_prompt="bad quality, worse quality", |
num_frames=16, |
guidance_scale=7.5, |
num_inference_steps=20, |
generator=torch.Generator("cpu").manual_seed(666), |
) |
# disable FreeInit |
pipe.disable_free_init() |
frames = output.frames[0] |
export_to_gif(frames, "animation.gif") FreeInit is not really free - the improved quality comes at the cost of extra computation. It requires sampling a few extra times depending on the num_iters parameter that is set when enabling it. Setting the use_fast_sampling parameter to True can improve the overall performance (at the cost of lower quality compared to when use_fast_sampling=False but still better results than vanilla video generation models). Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines. AnimateDiffPipeline class diffusers.AnimateDiffPipeline < source > ( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel motion_adapter: MotionAdapter scheduler: Union feature_extractor: CLIPImageProcessor = None image_encoder: CLIPVisionModelWithProjection = None ) Parameters vae (AutoencoderKL) — |
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder (CLIPTextModel) — |
Frozen text-encoder (clip-vit-large-patch14). tokenizer (CLIPTokenizer) — |
A CLIPTokenizer to tokenize text. unet (UNet2DConditionModel) — |
A UNet2DConditionModel used to create a UNetMotionModel to denoise the encoded video latents. motion_adapter (MotionAdapter) — |
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