Skip to content

scripts

Directory actions

More options

Directory actions

More options

Latest commit

 

History

History

scripts

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
fid
fid
 
 
inference
inference
 
 
README.md
README.md
 
 
__init__.py
__init__.py
 
 
download_data.py
download_data.py
 
 
format.sh
format.sh
 
 

README.md

FastGen Inference

Generate images and videos using pretrained or distilled models.

Script Modality Modes
image_model_inference.py Image Unconditional, class-conditional, T2I
video_model_inference.py Video T2V, I2V, V2V, Video2World

Key Arguments

Argument Description
--do_student_sampling Run distilled student (few-step)
--do_teacher_sampling Run teacher (multi-step)
--ckpt_path Path to distilled checkpoint (drop suffix .pth for FSDP checkpoints)
--num_steps Sampling steps for teacher
--classes N Class-conditional with N classes
--unconditional Unconditional generation
--input_image_file Input images for I2V
--source_video_file Source videos for V2V
--fps Output video frame rate
model.guidance_scale CFG scale (config override)
trainer.seed Random seed for reproducibility (config override)

Example commands

Unconditional

python scripts/inference/image_model_inference.py \
    --config fastgen/configs/experiments/EDM/config_sft_edm_cifar10.py \
    --do_student_sampling False --unconditional --num_samples 16 --num_steps 18

Class-Conditional

python scripts/inference/image_model_inference.py \
    --config fastgen/configs/experiments/DiT/config_sft_sit_xl.py \
    --do_student_sampling False --classes 1000 --num_steps 50 \
    --prompt_file scripts/inference/prompts/classes.txt \
    - model.guidance_scale=4.0

Text-to-Image (T2I)

python scripts/inference/image_model_inference.py \
    --config fastgen/configs/experiments/Flux/config_sft.py \
    --do_student_sampling False --num_steps 50 \
    - model.guidance_scale=3.5

Text-to-Video (T2V)

python scripts/inference/video_model_inference.py \
    --config fastgen/configs/experiments/WanT2V/config_dmd2.py \
    --do_student_sampling False --num_steps 50 --fps 16 \
    --neg_prompt_file scripts/inference/prompts/negative_prompt.txt \
    - model.guidance_scale=5.0

Image-to-Video (I2V)

python scripts/inference/video_model_inference.py \
    --config fastgen/configs/experiments/WanI2V/config_dmd2_wan22_5b.py \
    --do_student_sampling False --num_steps 50 --fps 16 \
    --neg_prompt_file scripts/inference/prompts/negative_prompt.txt \
    --input_image_file scripts/inference/prompts/source_image_paths.txt \
    - model.guidance_scale=5.0

Video-to-Video (V2V)

python scripts/inference/video_model_inference.py \
    --config fastgen/configs/experiments/WanV2V/config_sft.py \
    --do_student_sampling False --num_steps 50 --fps 16 \
    --neg_prompt_file scripts/inference/prompts/negative_prompt.txt \
    --source_video_file scripts/inference/prompts/source_video_paths.txt \
    - model.guidance_scale=5.0

Video2World (Cosmos)

python scripts/inference/video_model_inference.py \
    --config fastgen/configs/experiments/CosmosPredict2/config_sft.py \
    --do_student_sampling False --num_steps 35 --fps 24 \
    --neg_prompt_file scripts/inference/prompts/negative_prompt_cosmos.txt \
    --input_image_file scripts/inference/prompts/source_image_paths.txt \
    --num_conditioning_frames 1 \
    - model.guidance_scale=5.0 model.net.is_video2world=True model.input_shape="[16, 24, 88, 160]"

Causal / Autoregressive

Use causal configs (e.g., config_sft_causal_wan22_5b.py) for autoregressive generation.

python scripts/inference/video_model_inference.py \
    --config fastgen/configs/experiments/WanI2V/config_sft_causal_wan22_5b.py \
    --do_student_sampling False --num_steps 50 --fps 16 \
    --neg_prompt_file scripts/inference/prompts/negative_prompt.txt \
    --input_image_file scripts/inference/prompts/source_image_paths.txt \
    - model.guidance_scale=5.0

For generating longer videos via extrapolation:

  • --num_segments N: Generate N consecutive video segments autoregressively (default: 1)
  • --overlap_frames K: Overlap K latent frames between segments for temporal consistency (default: 0)

FID Evaluation

Compute Fréchet Inception Distance (FID) for image models using fid/compute_fid_from_ckpts.py.

Usage

torchrun --nproc_per_node=8 scripts/fid/compute_fid_from_ckpts.py \
    --config fastgen/configs/experiments/EDM/config_dmd2_cifar10.py \
    - trainer.ddp=True

This script:

  1. Loads checkpoints from trainer.checkpointer.save_dir
  2. Generates eval.num_samples images using student sampling
  3. Computes FID against reference statistics
  4. Saves results to {save_path}/{eval.samples_dir}/fid.json

Config Options

Parameter Description
eval.num_samples Number of samples to generate (default: 50000)
eval.min_ckpt Minimum checkpoint iteration to evaluate
eval.max_ckpt Maximum checkpoint iteration to evaluate
eval.samples_dir Subdirectory name for generated samples
eval.save_images Save visualization grid instead of computing FID

Reference Statistics

FID reference statistics can be computed with the following commands:

# Prepare FID refs for CIFAR-10 (uses EDM's fid.py, outputs .npz)
python scripts/download_data.py --dataset cifar10 --compute-fid-refs
# Prepare all FID refs (uses EDM's fid.py for EDM format, EDM2's calculate_metrics.py for EDM2 format)
python scripts/download_data.py --dataset all --imagenet-source /path/to/imagenet --compute-fid-refs

This computes the FID reference statistics according to the EDM and EDM2 repositories and stores them in $DATA_ROOT_DIR/fid-refs/:

Dataset Reference File Format
CIFAR-10 fid-refs/cifar10-32x32.npz EDM (.npz)
ImageNet-64 (EDM) fid-refs/imagenet-64x64.npz EDM (.npz)
ImageNet-64 (EDM2) fid-refs/imagenet-64x64-edmv2.pkl EDM2 (.pkl)
ImageNet-256 fid-refs/imagenet_256.pkl EDM2 (.pkl)