Text-to-Image
Diffusers
Safetensors
English
fd-loss
jit
imf
pmf
image-generation
class-conditional
imagenet
Instructions to use BiliSakura/FD-Loss-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/FD-Loss-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BiliSakura/FD-Loss-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
metadata
license: mit
library_name: diffusers
pipeline_tag: text-to-image
tags:
- diffusers
- imf
- image-generation
- class-conditional
iMF-L-SIM
Self-contained Diffusers variant for iMF-L/2 (FD-SIM post-trained) (Improved Mean Flows).
Load
from pathlib import Path
from diffusers import DiffusionPipeline
import torch
model_dir = Path("iMF-L-SIM")
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,
)
pipe.to("cuda")
image = pipe(
class_labels=207,
num_inference_steps=1,
guidance_scale=8.0,
guidance_interval_start=0.4,
guidance_interval_end=0.65,
).images[0]