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README.md

@@ -1,3 +1,68 @@
 ---
 license: apache-2.0
+library_name: pytorch
+tags:
+  - image-generation
+  - diffusion
+  - class-conditional
+  - imagenet
+  - pytorch
 ---
+
+# LDM-is-AE: Latent Diffusion is an Intrinsic Auto-Encoder
+
+Released **inference checkpoints** for *LDM-is-AE* (NeurIPS 2026).
+Code: https://github.com/PolyU-VCLab/LDMisAE
+
+## TL;DR
+
+Latent diffusion is usually a two-stage pipeline -- train a VAE, then train a diffusion model in its
+latent space. **LDM-is-AE removes the pipeline**: the DiT backbone is split into **DiT-E** / **DiT-D**
+and the intermediate feature is supervised in the **image domain at every timestep**, so the backbone
+itself becomes an intrinsic auto-encoder trained end-to-end in a single stage.
+
+## Checkpoints
+
+| File | Resolution | Keys | Size |
+| --- | --- | --- | --- |
+| `LDMisAE.256.ckpt` | 256x256 | `model_ema1`, `args`, `epoch` | 3.90 GB |
+| `LDMisAE.512.ckpt` | 512x512 | `model_ema1`, `args`, `epoch` | 3.93 GB |
+
+Class-conditional ImageNet models. The files are **ema1-only** (inference); resuming training needs a
+full checkpoint.
+
+## Results
+
+| Setting | FID | IS |
+| --- | --- | --- |
+| ImageNet 256x256, class-conditional | **1.80** | **314** |
+| ImageNet 512x512, class-conditional | **1.90** | **320** |
+| Reconstruction (gFID) | **1.82** | -- |
+
+## Usage
+
+```bash
+git clone https://github.com/PolyU-VCLab/LDMisAE.git && cd LDMisAE
+pip install -r requirements.txt
+huggingface-cli download xtudbxk/LDMisAE LDMisAE.256.ckpt --local-dir weights
+CKPT=weights/LDMisAE.256.ckpt IMG_SIZE=256 CFG=2.25 NUM_IMAGES=50000 bash scripts/inference.sh
+```
+
+Keep `--ema_mode ema1` (the default); `none` / `ema2` fail fast with a clear error. The LPIPS `vgg.pth`
+must be reachable before sampling -- see the repository README (*Quick Start*).
+
+## Citation
+
+```bibtex
+@inproceedings{ldm_is_ae_2026,
+  title     = {LDM-is-AE: Latent Diffusion is an Intrinsic Auto-Encoder for End-to-End Image Generation},
+  author    = {Zhang, Zhengqiang and Sun, Lingchen and Wu, Rongyuan and Yi, Qiaosi and
+               Kong, Xiangtao and Xiao, Chaodong and Zhang, Lei},
+  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
+  year      = {2026}
+}
+```
+
+## License
+
+Code is **Apache-2.0** (https://github.com/PolyU-VCLab/LDMisAE). Weights are released for research use.