Image-to-Image
Diffusers
TensorBoard
Safetensors
RSEditDiTChannelConcatPipeline
remote-sensing
image-editing
diffusion
Instructions to use BiliSakura/RSEdit-DiT-channel-concat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/RSEdit-DiT-channel-concat with Diffusers:
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] - Notebooks
- Google Colab
- Kaggle
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.binis large and is only needed to resume training, not for inference.- Keep
pipeline.pyandmodel_index.jsonin sync if the custom pipeline class name changes.
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