Total runs:
95
Run Growth:
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54.74%
Sponsored by
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BiliSakura / FD-Loss-diffusers
Model's Last Updated: May 31 2026
text-to-image
Introduction of FD-Loss-diffusers
Model Details of FD-Loss-diffusers
FD-Loss-diffusers
Post-training checkpoints with the same JiT / iMF / pMF architectures as the base models, distilled with FD-loss (feature distillation).
-
JiT
: all
/16variants for 256px generation - iMF : all variants for 256px generation
-
pMF
: both
/16and/32 - SIM suffix: SigLIP + Inception + MAE FD-loss
-
Default inference:
1 NFE
(
num_inference_steps=1)
FD-Loss JiT uses
legacy time convention
and
velocity Euler
via bundled
FDLossFlowMatchScheduler
(
scheduler/scheduling_flow_match_fd.py
; timesteps
t=1→0
).
Demo
Class-conditional sample (ImageNet class
golden retriever
),
JiT-H/16
FD-SIM at 256×256, 1 NFE, CFG 2.2, interval [0.1, 1.0], seed 42.
Inference (
JiT-B-16-SIM
)
from pathlib import Path
import torch
from diffusers import DiffusionPipeline
model_dir = Path("./JiT-B-16-SIM")
pipe = DiffusionPipeline.from_pretrained(
str(model_dir),
custom_pipeline=str(model_dir / "pipeline.py"),
trust_remote_code=True,
)
pipe.to("cuda")
generator = torch.Generator(device="cuda").manual_seed(42)
image = pipe(
class_labels="golden retriever",
num_inference_steps=1, # 1 NFE (FD-Loss default)
guidance_scale=3.0,
guidance_interval_min=0.1,
guidance_interval_max=1.0,
generator=generator,
).images[0]
| Parameter | JiT-B-16-SIM default | Source |
|---|---|---|
num_inference_steps
|
1
|
--num_sampling_steps 1
|
guidance_scale
|
3.0
|
JiT_B eval preset |
guidance_interval_min
/
max
|
0.1
/
1.0
|
JiT_B eval preset |
legacy_time_convention
|
True
(pipeline default)
|
--legacy_time_convention
|
Inference (
JiT-L-16-SIM
)
Same loading pattern as JiT-B; use CFG 2.4 (JiT_L eval preset):
model_dir = Path("./JiT-L-16-SIM")
pipe = DiffusionPipeline.from_pretrained(
str(model_dir),
custom_pipeline=str(model_dir / "pipeline.py"),
trust_remote_code=True,
).to("cuda")
image = pipe(
class_labels="golden retriever",
num_inference_steps=1,
guidance_scale=2.4,
generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]
Inference (
JiT-H-16-SIM
)
Same loading pattern as JiT-B/L; use CFG 2.2 (JiT_H eval preset):
model_dir = Path("./JiT-H-16-SIM")
pipe = DiffusionPipeline.from_pretrained(
str(model_dir),
custom_pipeline=str(model_dir / "pipeline.py"),
trust_remote_code=True,
).to("cuda")
image = pipe(
class_labels="golden retriever",
num_inference_steps=1,
guidance_scale=2.2,
guidance_interval_min=0.1,
guidance_interval_max=1.0,
generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]
Available variants
| Variant | Path | Architecture | Resolution | CFG (1 NFE) |
|---|---|---|---|---|
| JiT-B-16-SIM |
./JiT-B-16-SIM
|
JiT-B/16 | 256×256 | 3.0 |
| JiT-L-16-SIM |
./JiT-L-16-SIM
|
JiT-L/16 | 256×256 | 2.4 |
| JiT-H-16-SIM |
./JiT-H-16-SIM
|
JiT-H/16 | 256×256 | 2.2 |
| iMF-B-SIM |
./iMF-B-SIM
|
iMF-B/2 | 256×256 | 8.0 |
| iMF-L-SIM |
./iMF-L-SIM
|
iMF-L/2 | 256×256 | 8.0 |
| iMF-XL-SIM |
./iMF-XL-SIM
|
iMF-XL/2 | 256×256 | 8.0 |
| pMF-B-16-SIM |
./pMF-B-16-SIM
|
pMF-B/16 | 256×256 | 7.5 |
| pMF-B-32-SIM |
./pMF-B-32-SIM
|
pMF-B/32 | 512×512 | 6.5 |
| pMF-L-16-SIM |
./pMF-L-16-SIM
|
pMF-L/16 | 256×256 | 7.0 |
| pMF-L-32-SIM |
./pMF-L-32-SIM
|
pMF-L/32 | 512×512 | 7.5 |
| pMF-H-32-SIM |
./pMF-H-32-SIM
|
pMF-H/32 | 512×512 | 5.5 |
Inference (
iMF-B-SIM
)
Uses production
IMFPipeline
from
iMF-diffusers/iMF-B-2
(native iMF time convention, not legacy JiT time). Use
torch.float32
(same as base iMF variants):
model_dir = Path("./iMF-B-SIM")
pipe = DiffusionPipeline.from_pretrained(
str(model_dir),
custom_pipeline=str(model_dir / "pipeline.py"),
trust_remote_code=True,
torch_dtype=torch.float32,
).to("cuda")
image = pipe(
class_labels="golden retriever",
num_inference_steps=1,
guidance_scale=8.0,
guidance_interval_start=0.4,
guidance_interval_end=0.65,
generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]
Inference (
iMF-L-SIM
)
Same loading pattern as iMF-B-SIM (production
IMFPipeline
from
iMF-diffusers/iMF-L-2
):
model_dir = Path("./iMF-L-SIM")
pipe = DiffusionPipeline.from_pretrained(
str(model_dir),
custom_pipeline=str(model_dir / "pipeline.py"),
trust_remote_code=True,
torch_dtype=torch.float32,
).to("cuda")
image = pipe(
class_labels="golden retriever",
num_inference_steps=1,
guidance_scale=8.0,
guidance_interval_start=0.4,
guidance_interval_end=0.65,
generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]
Regenerate from checkpoint:
python _convert_imf_l_fd_sim.py
Inference (
iMF-XL-SIM
)
Same loading pattern as iMF-B/L-SIM (production
IMFPipeline
from
iMF-diffusers/iMF-XL-2
):
model_dir = Path("./iMF-XL-SIM")
pipe = DiffusionPipeline.from_pretrained(
str(model_dir),
custom_pipeline=str(model_dir / "pipeline.py"),
trust_remote_code=True,
torch_dtype=torch.float32,
).to("cuda")
image = pipe(
class_labels="golden retriever",
num_inference_steps=1,
guidance_scale=8.0,
guidance_interval_start=0.4,
guidance_interval_end=0.65,
generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]
Inference (
pMF-B-16-SIM
)
Uses production
PMFPipeline
from
pMF-diffusers/pMF-B-16
(native pMF time convention). Use
torch.bfloat16
on CUDA:
model_dir = Path("./pMF-B-16-SIM")
pipe = DiffusionPipeline.from_pretrained(
str(model_dir),
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,
guidance_scale=7.5,
guidance_interval_min=0.1,
guidance_interval_max=0.8,
noise_scale=1.0,
generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]
Inference (
pMF-B-32-SIM
)
Same loading pattern as pMF-B-16-SIM (production
PMFPipeline
from
pMF-diffusers/pMF-B-32
):
model_dir = Path("./pMF-B-32-SIM")
pipe = DiffusionPipeline.from_pretrained(
str(model_dir),
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,
guidance_scale=6.5,
guidance_interval_min=0.1,
guidance_interval_max=0.7,
noise_scale=2.0,
generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]
Runs of BiliSakura FD-Loss-diffusers on huggingface.co
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Url of FD-Loss-diffusers
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Provider of FD-Loss-diffusers huggingface.co
Other API from BiliSakura
Updated:February 18 2026
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