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
`--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

This script:

Loads checkpoints from trainer.checkpointer.save_dir
Generates eval.num_samples images using student sampling
Computes FID against reference statistics
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 are computed following the EDM and EDM2 repositories. Store them in $DATA_ROOT_DIR/fid-refs/:

Dataset	Reference File
CIFAR-10	`fid-refs/cifar10-32x32.npz`
ImageNet-64 (EDM)	`fid-refs/imagenet-64x64.npz`
ImageNet-64 (EDM2)	`fid-refs/imagenet-64x64-edmv2.npz`
ImageNet-256	`fid-refs/imagenet_256.pkl`
COCO-2014	`fid-refs/coco2014_eval_30k.npz`