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BiliSakura / iMF-diffusers
Model's Last Updated: May 30 2026
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Introduction of iMF-diffusers
Model Details of iMF-diffusers
iMF-diffusers
Native diffusers implementation of Improved Mean Flows (iMF) . Each variant folder is self-contained:
-
pipeline.py—IMFPipeline -
scheduler/scheduler_config.json—FlowMatchEulerDiscreteSchedulerconfig -
transformer/transformer_imf.py—IMFTransformer2DModel -
vae/— bundledstabilityai/sd-vae-ft-mse(AutoencoderKL)
Demo
Class-conditional sample (ImageNet class
207
, golden retriever),
iMF-XL/2
at 256×256, 1 step, CFG 1.8, interval [0.0, 1.0], seed 42.
Available checkpoints
| Checkpoint | Path | Latent size | FID eval CFG (ω) | FID eval interval |
|---|---|---|---|---|
| iMF-B/2 |
./iMF-B-2
|
32×32 | 8.0 | [0.40, 0.65] |
| iMF-L/2 |
./iMF-L-2
|
32×32 | 10.5 | [0.40, 0.60] |
| iMF-XL/2 |
./iMF-XL-2
|
32×32 | 8.0 | [0.42, 0.62] |
FID eval settings follow upstream imeanflow eval config .
Inference
from pathlib import Path
from diffusers import DiffusionPipeline
import torch
model_dir = Path("./iMF-XL-2")
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")
generator = torch.Generator(device="cuda").manual_seed(42)
image = pipe(
class_labels="golden retriever",
num_inference_steps=1,
guidance_scale=1.8,
guidance_interval_start=0.0,
guidance_interval_end=1.0,
generator=generator,
).images[0]
image.save("demo.png")
Load a
variant subfolder
(e.g.
./iMF-XL-2
), not the repo root.
Runs of BiliSakura iMF-diffusers on huggingface.co
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