BiliSakura/CAFM-diffusers

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

Converted from ByteDance-Seed/Adversarial-Flow-Models using libs/AFM-diffusers/scripts/convert_cafm_to_diffusers.py. Z-Image weights are bundled self-contained under CAFM-Z-Image-T2I/.

Demo

CAFM-JiT-H-16-256 — class 207 (golden retriever), seed 0, 100 NFE (Heun):

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

Benchmark results (ImageNet 256×256)

Model	Space	NFE	FID	Checkpoint
CAFM JiT-H/16	pixel	100	1.80	CAFM-JiT-H-16-256/
CAFM SiT-XL/2	latent	250	1.53	CAFM-SiT-XL-2-256/
CAFM Z-Image	latent T2I	25	—	CAFM-Z-Image-T2I/

Available checkpoints

Variant	Backbone	Steps	Solver
CAFM-JiT-H-16-256/	JIT	100	heun
CAFM-SiT-XL-2-256/	SIT	250	heun
CAFM-Z-Image-T2I/	Z-IMAGE	25	euler

Inference

ImageNet class-conditional (JiT / SiT)

from pathlib import Path
import torch
from diffusers import DiffusionPipeline

model_dir = Path("./CAFM-SiT-XL-2-256")
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=250, sampler="heun").images[0]

Text-to-image (Z-Image)

from pathlib import Path
import torch
from diffusers import DiffusionPipeline

model_dir = Path("./CAFM-Z-Image-T2I")
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,
)
pipe.enable_model_cpu_offload()  # recommended for single-GPU inference

image = pipe(
    prompt="A golden retriever sitting in a sunny park, photo realistic.",
    height=512,
    width=512,
    num_inference_steps=25,
    sampler="euler",
).images[0]