Principle:Huggingface Diffusers Video Memory Management

Property	Value
Principle Name	Video Memory Management
Overview	Memory optimization techniques specific to video generation including VAE tiling, CPU offloading, and sliced decoding
Domains	Video Generation, GPU Memory Optimization
Related Implementation	Huggingface_Diffusers_Video_Memory_Setup
Knowledge Sources	Repo (https://github.com/huggingface/diffusers), Source (src/diffusers/pipelines/pipeline_utils.py:L1174-L1270, src/diffusers/models/autoencoders/autoencoder_kl_wan.py:L1086-L1114)
Last Updated	2026-02-13 00:00 GMT

Description

Video generation models require substantially more memory than image generation because they operate on 5D tensors (B, C, F, H, W) with dozens to hundreds of frames. A single 720p 81-frame video's latent representation at 16 channels is approximately 1 x 16 x 21 x 60 x 104 floats. The three primary optimization strategies are:

1. Model CPU Offloading (enable_model_cpu_offload) - Moves entire model components to GPU only when needed, following a sequential offload chain defined by model_cpu_offload_seq.
2. VAE Tiling (enable_tiling) - Splits the spatial dimensions of VAE inputs into overlapping tiles, processes them independently, and blends the results.
3. VAE Slicing (enable_slicing) - Processes batch dimension one sample at a time during encoding and decoding.

Theoretical Basis

Model CPU Offloading

The offload chain defines the order in which components move to GPU. For video pipelines:

• Wan: text_encoder -> transformer -> transformer_2 -> vae
• HunyuanVideo: text_encoder -> text_encoder_2 -> transformer -> vae
• CogVideoX: text_encoder -> transformer -> vae

Each component is wrapped with an accelerate hook (cpu_offload_with_hook). When a component's forward() is called, the hook moves it to GPU. When the next component in the chain runs, the previous one is moved back to CPU. This means only one large model is on GPU at a time.

VAE Tiling

The VAE is the primary memory bottleneck during decoding because it operates at full pixel resolution. Tiling works by:

1. Splitting the latent tensor into overlapping spatial tiles (controlled by tile_sample_min_height, tile_sample_min_width)
2. Decoding each tile independently through the full decoder
3. Blending overlapping regions using linear interpolation (blend_v and blend_h methods) to avoid seam artifacts
4. The stride parameters (tile_sample_stride_height, tile_sample_stride_width) control the overlap between adjacent tiles

For temporal processing, the Wan VAE decodes one latent frame at a time using a feature caching system (feat_cache) that maintains causal convolution state across frames. This means temporal memory usage is constant regardless of video length.

VAE Slicing

When the batch dimension is greater than 1, slicing processes each batch element independently through the encoder/decoder rather than all at once, trading throughput for peak memory.

Usage

Apply these optimizations after pipeline instantiation but before calling the pipeline:

1. Always enable model_cpu_offload when GPU memory is limited (< 24GB for 14B models)
2. Always enable VAE tiling for videos above 480p resolution
3. Enable VAE slicing only for batch sizes > 1
4. These techniques can be combined: offloading + tiling together provides maximum memory savings

Related Pages

• Huggingface_Diffusers_Video_Memory_Setup (implements this principle) - Concrete API calls for enabling memory optimizations
• Huggingface_Diffusers_Video_Pipeline_Selection (prerequisite) - Pipeline must be instantiated before configuring memory
• Huggingface_Diffusers_Video_Decoding_Export (benefits from this) - Decoding is the primary consumer of the tiling optimization

Implementation:Huggingface_Diffusers_Video_Memory_Setup

Uses Heuristic

• Heuristic:Huggingface_Diffusers_Memory_Offloading_Strategy