FlowCache/FlowCache4MAGI-1-dev3-motion/README.md

FLOW CACHING FOR AUTOREGRESSIVE VIDEO GENERATION

This repository provides the official implementation of FlowCache on MAGI-1 model, a caching-based acceleration method for autoregressive video generation models.

🚀 Installation

Please follow the installation instructions provided in the MAGI-1, as this implementation is built on top of MAGI-1.

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▶️ Usage

1. Single Video Generation

Run accelerated generation using FlowCache:

# FlowCache for text-to-video generation
bash scripts/single_run/flowcache_t2v.sh

# FlowCache for video-to-video generation
bash scripts/single_run/flowcache_v2v.sh

# Baseline acceleration method (TeaCache) for text-to-video
bash scripts/single_run/teacache_t2v.sh

# Baseline acceleration method (TeaCache) for video-to-video
bash scripts/single_run/teacache_v2v.sh

2. Benchmark Sampling

Generate videos for evaluation on standard benchmarks:

# VBench
bash scripts/sample/flowcache_vbench.sh
bash scripts/sample/teacache_vbench.sh

# PhysicsIQ
bash scripts/sample/flowcache_physicsiq.sh
bash scripts/sample/teacache_physicsiq.sh

3. Quality Evaluation

Compute perceptual and structural similarity metrics between original and accelerated generations:

bash scripts/metric.sh

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⚙️ Key Parameters

Parameter	Description
rel_l1_thresh	Relative L1 distance threshold for cache reuse decision
warmup_steps	Number of denoising steps where reuse is disabled
total_cache_chunk_nums (B_total)	Total number of cache chunks maintained
compress_strategy	Granularity for selecting important KV caches: token, frame, or chunk
query_granularity	Granularity for importance scoring: token, frame, or chunk
mix_lambda	Weight balancing importance and redundancy (default: 0.07)
mode	Generation mode: t2v (text-to-video), i2v (image-to-video), or v2v (video-to-video)
prompt	Input prompt for conditional generation
output_path	Path to save generated videos
config_file	Path to MAGI-1 model configuration

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