BiliSakura/AFM-diffusers

Self-contained Adversarial Flow Models checkpoints for Hugging Face diffusers.

Converted from ByteDance-Seed/Adversarial-Flow-Models using libs/AFM-diffusers/scripts/convert_afm_to_diffusers.py.

All models use LDM (Rombach et al., 2022) latent space with sd-vae-ft-mse. Guidance abbreviations: CG = classifier guidance (Dhariwal & Nichol, 2021), DA = data augmentation (Karras et al., 2020a).

Demo

AFM-XL-2-2NFE-noguide — class 207 (golden retriever), seed 0, 2 NFE:

Each variant folder includes demo.png generated with the same prompt settings.

Benchmark results (ImageNet 256×256)

Model	Params	Guidance	NFE	FID	sFID	IS	Prec.	Recall	Checkpoint
AFM-B/2	130M	None	1	6.07	5.31	169.51	0.72	0.49	AFM-B-2-1NFE-noguide/
AFM-M/2	306M	None	1	5.21	5.60	178.48	0.75	0.54	AFM-M-2-1NFE-noguide/
AFM-L/2	457M	None	1	4.36	5.39	186.21	0.77	0.53	AFM-L-2-1NFE-noguide/
AFM-XL/2	673M	None	1	3.98	5.40	201.85	0.78	0.52	AFM-XL-2-1NFE-noguide/
AFM-XL/2	673M	None	2	2.36	4.35	235.77	0.81	0.52	AFM-XL-2-2NFE-noguide/
AFM-B/2	130M	CG+DA	1	3.05	5.32	269.18	0.81	0.51	AFM-B-2-1NFE-guided/
AFM-M/2	306M	CG+DA	1	2.82	5.20	279.12	0.81	0.50	AFM-M-2-1NFE-guided/
AFM-L/2	457M	CG+DA	1	2.63	5.10	277.96	0.81	0.52	AFM-L-2-1NFE-guided/
AFM-XL/2	673M	CG+DA	1	2.38	4.87	284.18	0.81	0.52	AFM-XL-2-1NFE-guided/
AFM-XL/2	675M	CG+DA	2	2.11	4.33	273.84	0.82	0.55	AFM-XL-2-2NFE-guided/
AFM-XL/2 (2× deep, 56-layer)	675M	CG+DA	1	2.08	4.79	298.33	0.79	0.56	AFM-XL-2-56layer-1NFE-guided/
AFM-XL/2	675M	CG+DA	4	2.03	4.59	259.66	0.78	0.59	AFM-XL-2-4NFE-guided/
AFM-XL/2 (4× deep, 112-layer)	675M	CG+DA	1	1.94	4.54	292.20	0.79	0.56	AFM-XL-2-112layer-1NFE-guided/

Available checkpoints

Variant	Model	Steps	Guidance
AFM-B-2-1NFE-guided/	AFM-B/2	1	guided
AFM-B-2-1NFE-noguide/	AFM-B/2	1	noguide
AFM-M-2-1NFE-guided/	AFM-M/2	1	guided
AFM-M-2-1NFE-noguide/	AFM-M/2	1	noguide
AFM-L-2-1NFE-guided/	AFM-L/2	1	guided
AFM-L-2-1NFE-noguide/	AFM-L/2	1	noguide
AFM-XL-2-1NFE-guided/	AFM-XL/2	1	guided
AFM-XL-2-1NFE-noguide/	AFM-XL/2	1	noguide
AFM-XL-2-2NFE-guided/	AFM-XL/2	2	guided
AFM-XL-2-2NFE-noguide/	AFM-XL/2	2	noguide
AFM-XL-2-4NFE-guided/	AFM-XL/2	4	guided
AFM-XL-2-56layer-1NFE-guided/	AFM-XL/2	1	guided
AFM-XL-2-112layer-1NFE-guided/	AFM-XL/2	1	guided

Inference

from pathlib import Path
import torch
from diffusers import DiffusionPipeline

model_dir = Path("./AFM-XL-2-1NFE-guided")
pipe = DiffusionPipeline.from_pretrained(
    str(model_dir),
    local_files_only=True,
    custom_pipeline=str(model_dir / "pipeline.py"),
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
).to("cuda")

image = pipe(class_labels="golden retriever", num_inference_steps=1).images[0]