BiliSakura / ADM-diffusers

BiliSakura/ADM-diffusers

Self-contained OpenAI ADM-G checkpoints for Hugging Face diffusers. No external code repo is required — 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:

• This model repo ( BiliSakura/ADM-diffusers )
• PyPI diffusers , torch , huggingface_hub

This 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/ ).

Available checkpoints

Both resolutions use the class-conditional diffusion checkpoint plus the noisy classifier (not the 256 uncond variant).

Demo

Load from Hugging Face

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

Load from a local clone

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")

Repo layout

BiliSakura/ADM-diffusers/
├── README.md
├── ADM-G-256/
│   ├── pipeline.py
│   ├── model_index.json
│   ├── unet/
│   ├── classifier/
│   └── scheduler/
└── ADM-G-512/
    ├── pipeline.py
    ├── model_index.json
    ├── demo.png
    ├── unet/
    ├── classifier/
    └── scheduler/