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BiliSakura/
ADM-diffusers

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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

Subfolder	Resolution	Guidance scale	OpenAI sources
`ADM-G-256/`	256×256	1.0	`256x256_diffusion.pt` + `256x256_classifier.pt`
`ADM-G-512/`	512×512	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).

ImageNet class labels

Each variant keeps an id2label map directly in its own model_index.json (same style as DiT on the Hub). Runtime label resolution is English-only:

pipe.id2label — inspect id → English label correspondence
pipe.labels — reverse map (English synonym → id), sorted for browsing
pipe.get_label_ids("golden retriever")
pipe(class_labels="golden retriever", ...)

Chinese labels are still preserved in the main source repo under src/labels/id2label_cn.json for reference.

Demo

Settings used for this demo image: ADM-G-512, DDIMScheduler, num_inference_steps=50, guidance_scale=4.0, seed=42, class "golden retriever".

python

from pathlib import Path
import torch
from diffusers import DDIMScheduler, DiffusionPipeline

model_dir = Path("./BiliSakura/ADM-diffusers/ADM-G-512")
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,
)
pipe = pipe.to("cuda")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
class_id = pipe.get_label_ids("golden retriever")[0]
generator = torch.Generator(device="cuda").manual_seed(42)

out = pipe(
    class_labels=class_id,
    guidance_scale=4.0,
    num_inference_steps=50,
    generator=generator,
).images[0]
out

Repo layout

text

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/