How to use from the
Use from the
Diffusers library
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("BiliSakura/RSEdit-DiT-channel-concat", dtype=torch.bfloat16, device_map="cuda")

prompt = "Turn this cat into a dog"
input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")

image = pipe(image=input_image, prompt=prompt).images[0]

RSEdit DiT Concat Ablation (channel_concat)

This directory stores a local Diffusers-compatible checkpoint for the RSEdit DiT channel-concatenation ablation run.

Contents

  • model_index.json: pipeline/component registry (RSEditDiTChannelConcatPipeline).
  • pipeline.py: custom local pipeline implementation used for inference/loading.
  • checkpoint-30000/: training-state snapshot at step 30k (includes optimizer/scheduler/random states and transformer checkpoint).
  • transformer/, vae/, text_encoder/, tokenizer/, scheduler/: exported model components for direct pipeline loading.
  • logs/: experiment logs (logs/rsedit-dit/...).

Quick Load (Diffusers)

from diffusers import DiffusionPipeline

model_dir = "path/to/model"
pipe = DiffusionPipeline.from_pretrained(model_dir, trust_remote_code=True)
pipe = pipe.to("cuda")

Notes

  • checkpoint-30000/optimizer.bin is large and is only needed to resume training, not for inference.
  • Keep pipeline.py and model_index.json in sync if the custom pipeline class name changes.
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