Visual Generation Models
Collection
6 items β’ Updated
How to use BiliSakura/ADM-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/ADM-diffusers", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Self-contained OpenAI ADM-G checkpoints for Hugging Face diffusers. No external code repo is required β each subfolder ships its own pipeline.py, component modules, and weights.
This repo is derived from the development bundle in Visual-Generative-Foundation-Model-Collection, but inference only needs:
BiliSakura/ADM-diffusers)diffusers, torch, huggingface_hubThis Hugging Face repo hosts multiple self-contained checkpoints as subfolders. Each subfolder includes its own pipeline.py, model_index.json, weights, and component code (unet/, classifier/, scheduler/).
| Subfolder | Resolution | Classifier scale | OpenAI sources |
|---|---|---|---|
ADM-G-256/ |
256Γ256 | 1.0 | 256x256_diffusion.pt + 256x256_classifier.pt |
ADM-G-512/ |
512Γ512 | 4.0 | 512x512_diffusion.pt + 512x512_classifier.pt |
Both resolutions use the class-conditional diffusion checkpoint plus the noisy classifier (not the 256 uncond variant).
import sys
from pathlib import Path
import torch
from huggingface_hub import snapshot_download
repo_dir = Path(snapshot_download("BiliSakura/ADM-diffusers"))
variant = "ADM-G-512" # or "ADM-G-256"
sys.path.insert(0, str(repo_dir / variant))
from pipeline import ADMPipeline
pipe = ADMPipeline.from_pretrained(".")
pipe.to("cuda")
pipe.unet.float()
pipe.classifier.float()
pipe.classifier.model.dtype = torch.float32
images = pipe(
class_labels=207,
num_inference_steps=250,
classifier_guidance_scale=4.0 if variant == "ADM-G-512" else 1.0,
).images
import sys
from pathlib import Path
repo = Path("BiliSakura/ADM-diffusers").resolve()
variant = "ADM-G-256"
sys.path.insert(0, str(repo / variant))
from pipeline import ADMPipeline
pipe = ADMPipeline.from_pretrained(".")
pipe.to("cuda")
BiliSakura/ADM-diffusers/
βββ README.md
βββ ADM-G-256/
β βββ pipeline.py
β βββ model_index.json
β βββ unet/
β βββ classifier/
β βββ scheduler/
βββ ADM-G-512/
βββ pipeline.py
βββ model_index.json
βββ demo.png
βββ unet/
βββ classifier/
βββ scheduler/