Image-to-Image
Diffusers
TensorBoard
Safetensors
RSEditUNetTokenConcatPipeline
remote-sensing
image-editing
diffusion
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("BiliSakura/RSEdit-UNet-token-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 UNet Concat Ablation
This directory stores checkpoints for the RSEdit UNet concatenation ablation run.
The active/exported checkpoint is under:
token_concat/
token_concat/ contents
model_index.json: pipeline/component registry (RSEditUNetTokenConcatPipeline).pipeline.py: custom local pipeline implementation used for inference/loading.checkpoint-30000/: training-state snapshot at step 30k (optimizer/scheduler/random state plus UNet weights).unet/,vae/,text_encoder/,tokenizer/,scheduler/: exported model components for direct pipeline loading.feature_extractor/,safety_checker/: SD-style auxiliary components referenced by this pipeline.logs/: experiment logs (token_concat/logs/...).
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 mainly needed for resume training rather than inference.
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