Publish DeCo-VTON checkpoint layout and model card

DressCode-512/unet/config.json

@@ -0,0 +1,68 @@
+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.35.1",
+  "_name_or_path": "stable-diffusion-v1-5/stable-diffusion-inpainting",
+  "act_fn": "silu",
+  "addition_embed_type": null,
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": null,
+  "attention_head_dim": 8,
+  "attention_type": "default",
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "class_embeddings_concat": false,
+  "conv_in_kernel": 3,
+  "conv_out_kernel": 3,
+  "cross_attention_dim": 768,
+  "cross_attention_norm": null,
+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dropout": 0.0,
+  "dual_cross_attention": false,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 9,
+  "layers_per_block": 2,
+  "mid_block_only_cross_attention": null,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "out_channels": 4,
+  "projection_class_embeddings_input_dim": null,
+  "resnet_out_scale_factor": 1.0,
+  "resnet_skip_time_act": false,
+  "resnet_time_scale_shift": "default",
+  "reverse_transformer_layers_per_block": null,
+  "sample_size": 64,
+  "time_cond_proj_dim": null,
+  "time_embedding_act_fn": null,
+  "time_embedding_dim": null,
+  "time_embedding_type": "positional",
+  "timestep_post_act": null,
+  "transformer_layers_per_block": 1,
+  "up_block_types": [
+    "UpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D"
+  ],
+  "upcast_attention": false,
+  "use_linear_projection": false
+}

DressCode-512/unet/diffusion_pytorch_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fbd294e84218ba7d793bf7b1cf7a9ff8517454abf3b9c1f98d79f46db2e085d4
+size 3438225152

README.md

@@ -1,79 +1,90 @@
 ---
 license: cc-by-nc-4.0
 tags:
-- virtual-try-on
-- diffusers
-- stable-diffusion
-- image-to-image
+  - virtual-try-on
+  - diffusion
+  - stable-diffusion
+  - image-to-image
+  - arxiv:2511.18775
 datasets:
-- VITON-HD
-- DressCode
-base_model:
-- stable-diffusion-v1-5/stable-diffusion-inpainting
-pipeline_tag: image-to-image
-language:
-- en
-library_name: diffusers
+  - VITON-HD
+  - DressCode
+base_model: stable-diffusion-v1-5/stable-diffusion-inpainting
 ---
 
-# Re-CatVTON
+# DeCo-VTON
 
-Official model weights for **"Rethinking Garment Conditioning in Diffusion-based Virtual Try-On"**.
+UNet checkpoints for **DeCo-VTON**, the official implementation of
+**Rethinking Garment Conditioning in Diffusion-based Virtual Try-On: Decouple,
+Don't Denoise**, accepted at ECCV 2026.
 
-📄 **Paper**: [Re-CatVTON](https://arxiv.org/abs/2511.18775)  
-💻 **Code**: [GitHub](https://github.com/Levinna/Re-CatVTON)
+DeCo-VTON is a single-UNet virtual try-on model that separates garment
+conditioning from the denoising target.
 
-## Available Checkpoints
+- Code: https://github.com/Levinna/DeCo-VTON
+- Paper: https://arxiv.org/abs/2511.18775
 
-| Dataset | Subfolder | Resolution |
-|---------|-----------|------------|
-| VITON-HD | `VITON-HD/checkpoint-16000/unet` | 512×384 |
-| DressCode | `DressCode/checkpoint-32000/unet` | 512×384 |
+This repository was formerly named `levinna/Re-CatVTON`. The old repository URL
+redirects here, and the original checkpoint paths remain available for backward
+compatibility.
 
-## Usage
-```python
-import torch
-from diffusers import AutoencoderKL, UNet2DConditionModel, DDPMScheduler
-from model.pipeline import RECATVTONPipeline
-from model.attn_processor import SkipAttnProcessor
-from model.utils import init_adapter
+## Checkpoints
+
+| Subfolder | Dataset | Resolution |
+|---|---|---|
+| `VITON-HD-512/unet` | VITON-HD | 512×384 |
+| `DressCode-512/unet` | DressCode | 512×384 |
 
-device = "cuda"
-dtype = torch.bfloat16
+Legacy paths such as `VITON-HD/checkpoint-16000/unet` and
+`DressCode/checkpoint-32000/unet` are retained for existing users.
+
+## Installation
+
+```bash
+git clone https://github.com/Levinna/DeCo-VTON.git
+cd DeCo-VTON
+pip install -r requirements.txt
+pip install -e .
+```
 
-# Load components
-vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse").to(device, dtype)
+## Usage
 
-# Choose one:
-unet = UNet2DConditionModel.from_pretrained(
-    "levinna/Re-CatVTON", 
-    subfolder="VITON-HD/checkpoint-16000/unet"  # or "DressCode/checkpoint-32000/unet"
-).to(device, dtype)
+> This repository contains UNet checkpoints, not a standalone Diffusers
+> pipeline. `DiffusionPipeline.from_pretrained()` cannot load them directly.
+> Install DeCo-VTON and use `DeCoVTONPipeline.from_vton_checkpoint()` as shown
+> below.
 
-scheduler = DDPMScheduler.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-inpainting", # or can use Re-CatVTON scheduler config
-    subfolder="scheduler"
-)
+The pipeline loads the VAE from `stabilityai/sd-vae-ft-mse` and the scheduler
+configuration from `stable-diffusion-v1-5/stable-diffusion-inpainting`.
 
-# Initialize attention processors (disable cross-attention)
-init_adapter(unet, cross_attn_cls=SkipAttnProcessor)
+```python
+import torch
+from decovton import DeCoVTONPipeline
 
-# Create pipeline
-pipeline = RECATVTONPipeline(vae=vae, unet=unet, scheduler=scheduler)
+pipe = DeCoVTONPipeline.from_vton_checkpoint(
+    hf_repo="levinna/DeCo-VTON",
+    subfolder="VITON-HD-512/unet",
+    torch_dtype=torch.bfloat16,
+).to("cuda")
 ```
-You can check more detailed instructions on Official [GitHub](https://github.com/Levinna/Re-CatVTON)
+
+See the [GitHub repository](https://github.com/Levinna/DeCo-VTON) for dataset
+preparation, inference, and evaluation instructions.
 
 ## License
-This model is licensed under CC BY-NC 4.0 due to the usage of non-commercial datasets (VITON-HD, DressCode).
-- **Model Weights**: [CC-BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)
-- **Code**: [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+
+The model weights are licensed under
+[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/), reflecting the
+non-commercial terms of the VITON-HD and DressCode datasets. The accompanying
+source code is licensed separately under CC BY-NC-SA 4.0.
 
 ## Citation
+
 ```bibtex
 @article{na2025rethinking,
-  title={Rethinking Garment Conditioning in Diffusion-based Virtual Try-On},
+  title={Rethinking Garment Conditioning in Diffusion-based Virtual Try-On: Decouple, Don't Denoise},
   author={Na, Kihyun and Choi, Jinyoung and Kim, Injung},
   journal={arXiv preprint arXiv:2511.18775},
   year={2025}
 }
-```
+```

VITON-HD-512/unet/config.json

@@ -0,0 +1,68 @@
+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.35.1",
+  "_name_or_path": "stable-diffusion-v1-5/stable-diffusion-inpainting",
+  "act_fn": "silu",
+  "addition_embed_type": null,
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": null,
+  "attention_head_dim": 8,
+  "attention_type": "default",
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "class_embeddings_concat": false,
+  "conv_in_kernel": 3,
+  "conv_out_kernel": 3,
+  "cross_attention_dim": 768,
+  "cross_attention_norm": null,
+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dropout": 0.0,
+  "dual_cross_attention": false,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 9,
+  "layers_per_block": 2,
+  "mid_block_only_cross_attention": null,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "out_channels": 4,
+  "projection_class_embeddings_input_dim": null,
+  "resnet_out_scale_factor": 1.0,
+  "resnet_skip_time_act": false,
+  "resnet_time_scale_shift": "default",
+  "reverse_transformer_layers_per_block": null,
+  "sample_size": 64,
+  "time_cond_proj_dim": null,
+  "time_embedding_act_fn": null,
+  "time_embedding_dim": null,
+  "time_embedding_type": "positional",
+  "timestep_post_act": null,
+  "transformer_layers_per_block": 1,
+  "up_block_types": [
+    "UpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D"
+  ],
+  "upcast_attention": false,
+  "use_linear_projection": false
+}

VITON-HD-512/unet/diffusion_pytorch_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4ce41428749aad3fdb7e7bd38bbcd374e80967f6b41144a726a488f897afb2e3
+size 3438225152