we do not have a full checkpoint conversion validation, if you encounter pipeline loading failure and unsidered output, please contact me via bili_sakura@zju.edu.cn

BiliSakura/BBDM-ckpt

Packaged BBDM/LBBDM checkpoints converted from original BBDM-format weights to:

• unet/config.json
• unet/diffusion_pytorch_model.safetensors
• scheduler/scheduler_config.json

These checkpoints use OpenAI-style BBDM UNet keys (input_blocks.*, middle_block.*, output_blocks.*). Use the community BBDM loader from pytorch-image-translation-models.

Variants

Model variant	Domain
edges2handbags-f4	Edges -> Handbags
edges2shoes-f4	Edges -> Shoes
faces2comics-f4	Faces -> Comics
CelebAMaskHQ-f4	CelebAMaskHQ
CelebAMaskHQ-f8	CelebAMaskHQ
CelebAMaskHQ-f16	CelebAMaskHQ

Repository layout

BBDM-ckpt/
  edges2shoes-f4/
    unet/
      config.json
      diffusion_pytorch_model.safetensors
    scheduler/
      scheduler_config.json
    conversion_status.json
  ...

Usage

import torch
from diffusers import VQModel
from examples.community.bbdm import load_bbdm_community_pipeline

device = "cuda"
ckpt_root = "/root/worksapce/models/BiliSakura/BBDM-ckpt"

# Example: edges2shoes-f4 <-> vqgan_f4 pairing
pipe = load_bbdm_community_pipeline(f"{ckpt_root}/edges2shoes-f4", device=device)
vqvae = VQModel.from_pretrained(f"{ckpt_root}/vqgan_f4/vqvae").to(device).eval()

# Input image should be normalized to [-1, 1], shape [B, 3, 256, 256].
x = torch.rand(1, 3, 256, 256, device=device) * 2 - 1
with torch.no_grad():
    x_latent = vqvae.encode(x).latents                  # [B, 3, 64, 64]
    y_latent = pipe(source_image=x_latent, num_inference_steps=200, output_type="pt").images
    y = vqvae.decode(y_latent).sample                   # [B, 3, 256, 256]

print(x_latent.shape, y_latent.shape, y.shape)

Note: num_inference_steps should be >= 3 for linear skip sampling.

VQGAN Pairing Guide

Use VQGAN variants that match the UNet latent tensor shape (in_channels, image_size):

BBDM checkpoint	UNet latent shape	Recommended VQGAN
edges2handbags-f4	[B, 3, 64, 64]	vqgan_f4/vqvae
edges2shoes-f4	[B, 3, 64, 64]	vqgan_f4/vqvae
faces2comics-f4	[B, 3, 64, 64]	vqgan_f4/vqvae
CelebAMaskHQ-f4	[B, 3, 64, 64]	vqgan_f4/vqvae
CelebAMaskHQ-f8	[B, 4, 32, 32]	vqgan_f8/vqvae
CelebAMaskHQ-f16	[B, 8, 16, 16]	vqgan_f16/vqvae

Compatibility note

These converted checkpoints are generally not compatible with src.BBDMPipeline.from_pretrained(...) because the native src BBDM wrapper expects diffusers UNet2DModel weight keys.

Use:

• examples.community.bbdm.load_bbdm_community_pipeline(...)

Conversion command

python -m examples.community.bbdm.convert_ckpt_to_unet \
  --raw-root "/root/worksapce/models/raw/BBDM Checkpoints" \
  --output-root "/root/worksapce/models/BiliSakura/BBDM-ckpt"

Citation

@inproceedings{li2023bbdm,
  title={BBDM: Image-to-Image Translation with Brownian Bridge Diffusion Models},
  author={Li, Bo and Xue, Kang and Liu, Bin and Lai, Yu-Kun},
  booktitle={CVPR},
  year={2023}
}