Checkpoint	Path	Resolution	Recommended CFG (ω)	CFG interval	Noise scale
pMF-B/16	`./pMF-B-16`	256×256	7.5	[0.1, 0.8]	1.0
pMF-B/32	`./pMF-B-32`	512×512	6.5	[0.1, 0.7]	2.0
pMF-L/16	`./pMF-L-16`	256×256	7.0	[0.2, 0.7]	1.0
pMF-L/32	`./pMF-L-32`	512×512	7.5	[0.2, 0.6]	4.0
pMF-H/16	`./pMF-H-16`	256×256	7.0	[0.2, 0.6]	2.0
pMF-H/32	`./pMF-H-32`	512×512	5.5	[0.1, 0.6]	4.0

Inference

python

from pathlib import Path
from diffusers import DiffusionPipeline
import torch

model_dir = Path("./pMF-L-16")
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.float32,
).to("cuda")

generator = torch.Generator(device="cuda").manual_seed(42)
image = pipe(
    class_labels="golden retriever",
    num_inference_steps=1,
    guidance_scale=7.0,
    guidance_interval_min=0.2,
    guidance_interval_max=0.7,
    noise_scale=1.0,
    generator=generator,
).images[0]
image.save("demo.png")

Load a variant subfolder (e.g. ./pMF-L-16), not the repo root.

BiliSakura/pMF-diffusers

pMF-diffusers

Available checkpoints

Inference

BiliSakura/
pMF-diffusers