Model card and the original license

LICENSE

@@ -0,0 +1,176 @@
+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+1. Definitions.
+
+"License" shall mean the terms and conditions for use, reproduction,
+and distribution as defined by Sections 1 through 9 of this document.
+
+"Licensor" shall mean the copyright owner or entity authorized by
+the copyright owner that is granting the License.
+
+"Legal Entity" shall mean the union of the acting entity and all
+other entities that control, are controlled by, or are under common
+control with that entity. For the purposes of this definition,
+"control" means (i) the power, direct or indirect, to cause the
+direction or management of such entity, whether by contract or
+otherwise, or (ii) ownership of fifty percent (50%) or more of the
+outstanding shares, or (iii) beneficial ownership of such entity.
+
+"You" (or "Your") shall mean an individual or Legal Entity
+exercising permissions granted by this License.
+
+"Source" form shall mean the preferred form for making modifications,
+including but not limited to software source code, documentation
+source, and configuration files.
+
+"Object" form shall mean any form resulting from mechanical
+transformation or translation of a Source form, including but
+not limited to compiled object code, generated documentation,
+and conversions to other media types.
+
+"Work" shall mean the work of authorship, whether in Source or
+Object form, made available under the License, as indicated by a
+copyright notice that is included in or attached to the work
+(an example is provided in the Appendix below).
+
+"Derivative Works" shall mean any work, whether in Source or Object
+form, that is based on (or derived from) the Work and for which the
+editorial revisions, annotations, elaborations, or other modifications
+represent, as a whole, an original work of authorship. For the purposes
+of this License, Derivative Works shall not include works that remain
+separable from, or merely link (or bind by name) to the interfaces of,
+the Work and Derivative Works thereof.
+
+"Contribution" shall mean any work of authorship, including
+the original version of the Work and any modifications or additions
+to that Work or Derivative Works thereof, that is intentionally
+submitted to Licensor for inclusion in the Work by the copyright owner
+or by an individual or Legal Entity authorized to submit on behalf of
+the copyright owner. For the purposes of this definition, "submitted"
+means any form of electronic, verbal, or written communication sent
+to the Licensor or its representatives, including but not limited to
+communication on electronic mailing lists, source code control systems,
+and issue tracking systems that are managed by, or on behalf of, the
+Licensor for the purpose of discussing and improving the Work, but
+excluding communication that is conspicuously marked or otherwise
+designated in writing by the copyright owner as "Not a Contribution."
+
+"Contributor" shall mean Licensor and any individual or Legal Entity
+on behalf of whom a Contribution has been received by Licensor and
+subsequently incorporated within the Work.
+
+2. Grant of Copyright License. Subject to the terms and conditions of
+this License, each Contributor hereby grants to You a perpetual,
+worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+copyright license to reproduce, prepare Derivative Works of,
+publicly display, publicly perform, sublicense, and distribute the
+Work and such Derivative Works in Source or Object form.
+
+3. Grant of Patent License. Subject to the terms and conditions of
+this License, each Contributor hereby grants to You a perpetual,
+worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+(except as stated in this section) patent license to make, have made,
+use, offer to sell, sell, import, and otherwise transfer the Work,
+where such license applies only to those patent claims licensable
+by such Contributor that are necessarily infringed by their
+Contribution(s) alone or by combination of their Contribution(s)
+with the Work to which such Contribution(s) was submitted. If You
+institute patent litigation against any entity (including a
+cross-claim or counterclaim in a lawsuit) alleging that the Work
+or a Contribution incorporated within the Work constitutes direct
+or contributory patent infringement, then any patent licenses
+granted to You under this License for that Work shall terminate
+as of the date such litigation is filed.
+
+4. Redistribution. You may reproduce and distribute copies of the
+Work or Derivative Works thereof in any medium, with or without
+modifications, and in Source or Object form, provided that You
+meet the following conditions:
+
+(a) You must give any other recipients of the Work or
+Derivative Works a copy of this License; and
+
+(b) You must cause any modified files to carry prominent notices
+stating that You changed the files; and
+
+(c) You must retain, in the Source form of any Derivative Works
+that You distribute, all copyright, patent, trademark, and
+attribution notices from the Source form of the Work,
+excluding those notices that do not pertain to any part of
+the Derivative Works; and
+
+(d) If the Work includes a "NOTICE" text file as part of its
+distribution, then any Derivative Works that You distribute must
+include a readable copy of the attribution notices contained
+within such NOTICE file, excluding those notices that do not
+pertain to any part of the Derivative Works, in at least one
+of the following places: within a NOTICE text file distributed
+as part of the Derivative Works; within the Source form or
+documentation, if provided along with the Derivative Works; or,
+within a display generated by the Derivative Works, if and
+wherever such third-party notices normally appear. The contents
+of the NOTICE file are for informational purposes only and
+do not modify the License. You may add Your own attribution
+notices within Derivative Works that You distribute, alongside
+or as an addendum to the NOTICE text from the Work, provided
+that such additional attribution notices cannot be construed
+as modifying the License.
+
+You may add Your own copyright statement to Your modifications and
+may provide additional or different license terms and conditions
+for use, reproduction, or distribution of Your modifications, or
+for any such Derivative Works as a whole, provided Your use,
+reproduction, and distribution of the Work otherwise complies with
+the conditions stated in this License.
+
+5. Submission of Contributions. Unless You explicitly state otherwise,
+any Contribution intentionally submitted for inclusion in the Work
+by You to the Licensor shall be under the terms and conditions of
+this License, without any additional terms or conditions.
+Notwithstanding the above, nothing herein shall supersede or modify
+the terms of any separate license agreement you may have executed
+with Licensor regarding such Contributions.
+
+6. Trademarks. This License does not grant permission to use the trade
+names, trademarks, service marks, or product names of the Licensor,
+except as required for reasonable and customary use in describing the
+origin of the Work and reproducing the content of the NOTICE file.
+
+7. Disclaimer of Warranty. Unless required by applicable law or
+agreed to in writing, Licensor provides the Work (and each
+Contributor provides its Contributions) on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+implied, including, without limitation, any warranties or conditions
+of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+PARTICULAR PURPOSE. You are solely responsible for determining the
+appropriateness of using or redistributing the Work and assume any
+risks associated with Your exercise of permissions under this License.
+
+8. Limitation of Liability. In no event and under no legal theory,
+whether in tort (including negligence), contract, or otherwise,
+unless required by applicable law (such as deliberate and grossly
+negligent acts) or agreed to in writing, shall any Contributor be
+liable to You for damages, including any direct, indirect, special,
+incidental, or consequential damages of any character arising as a
+result of this License or out of the use or inability to use the
+Work (including but not limited to damages for loss of goodwill,
+work stoppage, computer failure or malfunction, or any and all
+other commercial damages or losses), even if such Contributor
+has been advised of the possibility of such damages.
+
+9. Accepting Warranty or Additional Liability. While redistributing
+the Work or Derivative Works thereof, You may choose to offer,
+and charge a fee for, acceptance of support, warranty, indemnity,
+or other liability obligations and/or rights consistent with this
+License. However, in accepting such obligations, You may act only
+on Your own behalf and on Your sole responsibility, not on behalf
+of any other Contributor, and only if You agree to indemnify,
+defend, and hold each Contributor harmless for any liability
+incurred by, or claims asserted against, such Contributor by reason
+of your accepting any such warranty or additional liability.
+
+END OF TERMS AND CONDITIONS

README.md

@@ -0,0 +1,373 @@
+---
+base_model: CSWRY/VOSR
+base_model_relation: quantized
+license: apache-2.0
+pipeline_tag: image-to-image
+library_name: mlx
+tags:
+- mlx
+- moonbox
+- vosr
+- super-resolution
+- image-upscaling
+- image-restoration
+---
+
+# VOSR 2.0 MLX
+
+MLX builds of [VOSR 2.0](https://github.com/cswry/VOSR), made by Moonbox for Apple Silicon Macs. Each precision is a folder; keep a folder's
+files together. Only the parts listed below are converted: the rest of the pipeline comes from the original
+repository unless the section below says otherwise.
+
+## Files
+
+The last column is how far each build is from the original PyTorch model on the same inputs (see the section below
+for what was measured).
+
+| Precision | File | Size | sha256 | Checked against the original |
+|---|---|---|---|---|
+| fp16 | [`fp16/config.json`](https://huggingface.co/moonbox-ai/VOSR-MLX/blob/main/fp16/config.json) | 0.8 kB | `7831ae8dacd5` | PSNR 42.3 dB mean / 31.4 worst, LPIPS <= 0.011 |
+| fp16 | [`fp16/mlx_model.py`](https://huggingface.co/moonbox-ai/VOSR-MLX/blob/main/fp16/mlx_model.py) | 23.6 kB | `227a42122e08` | PSNR 42.3 dB mean / 31.4 worst, LPIPS <= 0.011 |
+| fp16 | [`fp16/model.safetensors`](https://huggingface.co/moonbox-ai/VOSR-MLX/blob/main/fp16/model.safetensors) | 3.33 GB | `de064c413024` | PSNR 42.3 dB mean / 31.4 worst, LPIPS <= 0.011 |
+
+## How to run
+
+VOSR 2.0 (the one-step 1.4B model) ported to MLX: the whole pipeline of `inference_vosr_onestep.py` (bicubic
+upscale, the 2D Qwen-Image VAE, DINOv2-L features, one LightningDiT step, the wavelet color fix). Each folder holds
+`model.safetensors`, `config.json` and `mlx_model.py`, the port (`mlx`, `numpy` and `Pillow`):
+
+```python
+import numpy as np, mlx_model
+from PIL import Image
+model = mlx_model.load("fp16")
+out = mlx_model.upscale(model, np.array(Image.open("in.png").convert("RGB")), scale=4, seed=42)
+Image.fromarray(out).save("out.png")
+```
+
+It runs the full picture at once (the original's default, no tiling). Output sides are rounded up to a multiple of
+16 internally and cropped back.
+
+**Which one:** `fp16/` follows the original closely (42 dB mean against PyTorch fp32, LPIPS <= 0.011). VOSR makes up
+detail from noise in one step, so small numeric differences show up as different fine texture: `bf16/`, `8bit/`
+and `6bit/` look the same as each other but drift from the original on some pictures (LPIPS up to 0.048, below the
+lab's upscaling threshold of 0.02, so marked experimental); `4bit/` drifts further.
+
+| Precision | Size | PSNR vs PyTorch fp32 (mean / worst) | LPIPS (worst) | Time, 128 -> 512 on an M5 |
+|---|---|---|---|---|
+| `fp16/` | 3.33 GB | 42.3 / 31.4 dB | 0.011 | 1.0 s |
+| `bf16/` (experimental) | 3.33 GB | 33.8 / 23.2 dB | 0.045 | 1.0 s |
+| `8bit/` (experimental) | 1.82 GB | 33.7 / 23.1 dB | 0.047 | 1.1 s |
+| `6bit/` (experimental) | 1.41 GB | 33.1 / 22.8 dB | 0.048 | 1.1 s |
+| `4bit/` (experimental) | 1.01 GB | 29.1 / 20.5 dB | 0.094 | 1.0 s |
+
+Quantized builds quantize the DiT's and DINOv2's linear layers (group 64); the VAE is all convolutions and stays
+16-bit.
+
+## How these were made
+
+From [VOSR 2.0](https://github.com/cswry/VOSR) at revision `516f292b99`: the original weights loaded in MLX and stored as bf16 or fp16, or
+quantized with MLX's affine quantization (`8bit`, `6bit`, `4bit`, group size 64; layers MLX can't quantize, such as
+convolutions and norms, stay bf16). Every build was checked against the original before upload; the `sha256` column
+above is what the files must hash to.
+
+## License
+
+Apache-2.0. These files are a derivative of the original model and carry its license: it is copied in this
+repository, and it governs your use of these files. The original model card follows unchanged.
+
+---
+
+# Original model card
+
+<div align="center">
+
+<p align="center">
+  <img src="assets/logo.png" alt="VOSR logo" width="72%">
+</p>
+
+# VOSR: A Vision-Only Generative Model for Image Super-Resolution
+
+<p align="center"><i>A framework for native generative image restoration.</i></p>
+
+#### &#x1F6A9; Accepted by CVPR 2026
+
+[![Paper](https://img.shields.io/badge/Paper-arXiv-b31b1b.svg)](https://arxiv.org/pdf/2604.03225)
+[![VOSR-ModelScope](https://img.shields.io/badge/VOSR-ModelScope-6246EA.svg)](https://modelscope.cn/models/LULALULALU/VOSR_CKPT)
+[![VOSR-HuggingFace](https://img.shields.io/badge/VOSR-HuggingFace-FCC624.svg)](https://huggingface.co/CSWRY/VOSR)
+[![ScreenSR-ModelScope](https://img.shields.io/badge/ScreenSR-ModelScope-0EA5E9.svg)](https://modelscope.cn/datasets/LULALULALU/ScreenSR)
+[![ScreenSR-HuggingFace](https://img.shields.io/badge/ScreenSR-HuggingFace-FFB000.svg)](https://huggingface.co/datasets/CSWRY/ScreenSR)
+
+[Rongyuan Wu](https://scholar.google.com/citations?user=A-U8zE8AAAAJ&hl=zh-CN)<sup>1,2,&#42;</sup> |
+[Lingchen Sun](https://scholar.google.com/citations?user=ZCDjTn8AAAAJ&hl=zh-CN)<sup>1,2,&#42;</sup> |
+[Zhengqiang Zhang](https://scholar.google.com/citations?user=UX26wSMAAAAJ&hl=en)<sup>1,2</sup> |
+[Xiangtao Kong](https://scholar.google.com/citations?user=lueNzSgAAAAJ&hl=zh-CN)<sup>1,2</sup> <br>
+[Jixin Zhao](https://scholar.google.com/citations?user=0Z89rfUAAAAJ)<sup>1,2</sup> |
+[Shihao Wang](https://scholar.google.com/citations?user=7TWugs4AAAAJ&hl=zh-CN)<sup>1</sup> |
+[Lei Zhang](https://www4.comp.polyu.edu.hk/~cslzhang/)<sup>1,2,&dagger;</sup>
+
+<sup>1</sup> The Hong Kong Polytechnic University  
+<sup>2</sup> OPPO Research Institute
+
+<sup>&#42;</sup> Equal contribution. <sup>&dagger;</sup> Corresponding author.
+
+</div>
+
+<!-- Optional teaser image -->
+<!-- ![teaser](assets/teaser.png) -->
+
+<p align="center">
+  <img src="assets/overview.jpg" alt="VOSR overview" width="100%">
+</p>
+<p align="center"><em>Overview of the VOSR framework, including the overall pipeline and our condition / guidance design.</em></p>
+
+
+## &#x1F4CC; Quick Links
+
+- [&#x1F4F0; News](#news)
+- [&#x1F9F0; Preparation](#preparation)
+- [&#x1F3CB;&#xFE0F; Training](#training)
+- [&#x1F50D; Inference](#inference)
+- [&#x1F4EE; Contact](#contact)
+- [&#x1F4DA; Citation](#citation)
+
+<a id="news"></a>
+## &#x1F4F0; News
+
+- 2026.08.22 **VOSR 2.0** released: a one-step 1.4B DiT, vision-only model with improved fine-structure restoration. See [visual comparison](https://cswry.github.io/vosr2/). Thanks to [ylchen333](https://github.com/ylchen333) for bringing VOSR to ComfyUI with [ComfyUI-VOSR2](https://github.com/ylchen333/ComfyUI-VOSR2).
+
+- 2026.04.10 Public release: training and inference code, [pretrained checkpoints](https://modelscope.cn/models/LULALULALU/VOSR_CKPT), bundled VAE / decoder assets, and the [ScreenSR](https://modelscope.cn/datasets/LULALULALU/ScreenSR) benchmark. Setup and file layout: [Preparation](#preparation); commands: [Training](#training) and [Inference](#inference).
+
+---
+
+<p align="center">
+  <img src="assets/vosr_comparison_figure.png" alt="VOSR performance, efficiency, and training cost comparison" width="100%">
+</p>
+<p align="center"><em>Comparison with prior methods in performance, inference efficiency, and training cost.</em></p>
+
+<p align="center">
+  <img src="assets/vis_comp.jpg" alt="Qualitative comparison on natural and text-rich images" width="100%">
+</p>
+<p align="center"><em>VOSR better preserves fine structures and text readability.</em></p>
+
+<a id="preparation"></a>
+## &#x1F9F0; Preparation
+
+### Dependencies and Installation
+
+```bash
+## clone this repository
+git clone https://github.com/cswry/VOSR.git
+cd VOSR
+
+# create an environment with python >= 3.8
+conda create -n vosr python=3.8
+conda activate vosr
+pip install -r requirements.txt
+```
+
+### &#x1F4E6; Model Weights
+
+Download all pretrained weights from [ModelScope](https://modelscope.cn/models/LULALULALU/VOSR_CKPT) or [Hugging Face](https://huggingface.co/CSWRY/VOSR), and place them under `preset/ckpts/`. The expected structure:
+
+```text
+preset/ckpts/
+|-- Qwen-Image-vae-2d/          # Qwen-Image VAE (2D, for 1.4B models)
+|-- stable-diffusion-2-1-base/  # SD2.1 VAE (for 0.5B models)
+|-- sd21_lwdecoder.pth          # Lightweight decoder for SD2.1 VAE
+|-- torch_cache/                # DINOv2 pretrained weights
+|-- VOSR2/                     # VOSR 2.0 one-step (1.4B) model
+|-- VOSR_0.5B_ms/               # 0.5B multi-step model
+|-- VOSR_0.5B_os/               # 0.5B one-step (distilled) model
+|-- VOSR_1.4B_ms/               # 1.4B multi-step model
+`-- VOSR_1.4B_os/               # 1.4B one-step (distilled) model
+```
+
+#### VAE and decoder
+
+For `VOSR-0.5B`, we provide `sd21_lwdecoder.pth`, a lightweight replacement for the original SD2.1 VAE decoder. It achieves comparable overall visual quality in our evaluation, while performing slightly better on text-rich and document-like images.
+
+When scaling to `VOSR-1.4B`, we adopt the 16-channel Qwen-Image VAE to better preserve input fidelity. Although Qwen-Image is designed for T2I generation, it is released in a video-VAE form, which is unnecessarily slow for image SR inference. We therefore provide `Qwen-Image-vae-2d`, an image-only 2D variant extracted from the original model to remove the overhead of the full 3D design.
+
+### &#x1F5C2;&#xFE0F; Training Data
+
+We support two data loading modes, configured via `dataset_type` in the YAML config:
+
+- `txt` - Each folder contains individual image files. A txt config lists folders with sampling weights.
+- `webdataset` - Each folder contains `.tar` shards. Same txt config format, loaded via WebDataset.
+
+Create a dataset config file (e.g., `configs/train_txt/train_dataset_txt.txt`):
+
+```text
+/path/to/dataset_A, 2
+/path/to/dataset_B, 1
+/path/to/dataset_C, 1
+```
+
+Each line: `<folder_path>, <sampling_weight>`. Higher weight = more frequent sampling.
+
+For `txt` mode, each folder should contain HQ images (`.png` / `.jpg`). For `webdataset` mode, each folder should contain `.tar` shards with images inside.
+
+### &#x1F9EA; New Real-World Paired Benchmark
+
+Download the [ScreenSR benchmark from ModelScope](https://modelscope.cn/datasets/LULALULALU/ScreenSR) or [Hugging Face](https://huggingface.co/datasets/CSWRY/ScreenSR), place it wherever you like, and then point `-i` to that folder when running inference.
+
+[ScreenSR](https://modelscope.cn/datasets/LULALULALU/ScreenSR) is a real-world paired benchmark for generative SR, built with a screen re-photography pipeline. It provides cleaner references, more diverse content, and broader variation in scenes and scales than existing real-world paired SR benchmarks.
+
+<p align="center">
+  <img src="assets/montage_13x10_256px.jpg" alt="ScreenSR benchmark montage" width="100%">
+</p>
+<p align="center"><em>Thumbnail montage of the ScreenSR benchmark, covering diverse scenes, subjects, and multilingual text.</em></p>
+
+---
+
+<a id="training"></a>
+## &#x1F3CB;&#xFE0F; Training
+
+The provided training configs disable experiment tracking by default
+(`report_to: none`). To enable Weights & Biases logging, run `wandb login`
+with your own account and set `report_to: wandb` in the YAML config.
+
+### Multi-step Training
+
+```bash
+# VOSR-0.5B
+torchrun --nproc_per_node=8 train_vosr.py --config configs/train_yml/multi_step/VOSR_0.5B.yml
+
+# VOSR-1.4B
+torchrun --nproc_per_node=8 train_vosr.py --config configs/train_yml/multi_step/VOSR_1.4B.yml
+```
+
+### One-step Distillation
+
+Requires a trained multi-step teacher checkpoint. Set `teacher_ckpt` and `pretrained_ckpt` in the YAML config.
+
+```bash
+# VOSR-0.5B one-step
+torchrun --nproc_per_node=8 train_vosr_distill.py --config configs/train_yml/one_step/VOSR_0.5B.yml
+
+# VOSR-1.4B one-step
+torchrun --nproc_per_node=8 train_vosr_distill.py --config configs/train_yml/one_step/VOSR_1.4B.yml
+```
+
+---
+
+<a id="inference"></a>
+## &#x1F50D; Inference
+
+Single-GPU inference. 
+
+### VOSR 2.0 one-step (default)
+
+VOSR 2.0 is the default one-step model (1.4B DiT, vision-only, with improved fine-structure restoration).
+
+```bash
+# VOSR2-1.4B one-step
+python inference_vosr_onestep.py \
+    -c preset/ckpts/VOSR2 \
+    -i preset/datasets/inp_data \
+    -o preset/results \
+    -u 4
+```
+
+### Multi-step models
+
+Multi-step sampling defaults to **25 steps** (`--infer_steps`, default 25). Override if you need fewer or more function evaluations.
+
+Two knobs mainly affect the trade-off between faithfulness to the LR input and generative detail (both can be set via CLI to override `args.json`):
+
+- `--cfg_scale` - Higher values tend to emphasize fidelity to the condition; lower values give more generative freedom. The sweet spot depends on input degradation strength. In our experiments, roughly `-0.5` to `2` is a usable range; `0.5` is a practical default.
+- `--weak_cond_strength_aelq` - During training this is sampled uniformly in `[0.05, 0.25]` so the checkpoint supports a wide range at inference via the same flag (smaller -> more generative, larger -> more faithful). Default `0.1`.
+
+Benchmark presets (multi-step): for apples-to-apples evaluation we use `--cfg_scale -0.5` on RealSR and `--cfg_scale 0.5` on [ScreenSR](https://modelscope.cn/datasets/LULALULALU/ScreenSR).
+
+```bash
+# Inputs under preset/datasets/inp_data follow RealSR-style evaluation; use --cfg_scale -0.5 (see benchmark presets above).
+
+# VOSR-0.5B multi-step (25 steps)
+python inference_vosr.py \
+    -c preset/ckpts/VOSR_0.5B_ms \
+    -i preset/datasets/inp_data \
+    -o preset/results \
+    -u 4
+
+# VOSR-1.4B multi-step (25 steps)
+python inference_vosr.py \
+    -c preset/ckpts/VOSR_1.4B_ms \
+    -i preset/datasets/inp_data \
+    -o preset/results \
+    -u 4
+```
+
+### One-step models
+
+One-step models use `--infer_steps` with default `1` (typical for distilled checkpoints).
+
+```bash
+# VOSR-0.5B one-step
+python inference_vosr_onestep.py \
+    -c preset/ckpts/VOSR_0.5B_os \
+    -i preset/datasets/inp_data \
+    -o preset/results \
+    -u 4
+
+# VOSR-1.4B one-step
+python inference_vosr_onestep.py \
+    -c preset/ckpts/VOSR_1.4B_os \
+    -i preset/datasets/inp_data \
+    -o preset/results \
+    -u 4
+```
+
+### Common arguments
+
+`-c` checkpoint path, `-i` input image or folder, `-o` output directory, `-u` upscale factor. Outputs are saved as **PNG directly into the `-o` folder**; color alignment defaults to `wavelet` (`--align_method wavelet|adain|nofix`).
+
+- Multi-step (`inference_vosr.py`): `--infer_steps` (default `25`), `--cfg_scale`, `--weak_cond_strength_aelq` (see above).
+- One-step (`inference_vosr_onestep.py`): `--infer_steps` (default `1`).
+- Both: `--posterior_mode` / `--no-posterior_mode` — deterministic VAE latent (`latent_dist.mode()`) is the default, for reproducible results and seam-free tiled inference.
+
+### Tiled inference for large images
+
+Large images are tiled in latent space to bound GPU memory:
+
+- `--tile_size 512` — DiT tile size in pixels (default `0` = disabled). Use for large images; overlapping tiles are Gaussian-blended.
+- `--vae_tile_size 1024` — VAE encoder/decoder tile size in pixels (default `0` = full-image VAE). The Qwen VAE attention is O(N)-memory, so full-image encode/decode already fits up to ~8K inputs; VAE tiling is only needed for extreme sizes.
+- `--vae_tile_overlap` — VAE tile overlap (default: follows `--tile_overlap`).
+
+```bash
+# VOSR-1.4B one-step on a large image with DiT + VAE tiling
+python inference_vosr_onestep.py \
+    -c preset/ckpts/VOSR_1.4B_os \
+    -i large_input.png \
+    -o preset/results \
+    -u 4 --tile_size 512 --vae_tile_size 1024
+```
+
+---
+
+<a id="contact"></a>
+## &#x1F4EE; Contact
+
+If you have any questions, please feel free to contact: `rong-yuan.wu@connect.polyu.hk`
+
+<a id="citation"></a>
+## &#x1F4DA; Citation
+
+If VOSR is useful for your research, please consider citing:
+
+```bibtex
+@inproceedings{wu2026vosr,
+  title   = {VOSR: A Vision-Only Generative Model for Image Super-Resolution},
+  author  = {Wu, Rongyuan and Sun, Lingchen and Zhang, Zhengqiang and Kong, Xiangtao and Zhao, Jixin and Wang, Shihao and Zhang, Lei},
+  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  year    = {2026}
+}
+```
+
+## Acknowledgements
+
+This project benefits from [stable-diffusion-2-1-base](https://huggingface.co/Manojb/stable-diffusion-2-1-base), [LightningDiT](https://github.com/hustvl/LightningDiT), [Qwen-Image](https://huggingface.co/Qwen/Qwen-Image), [DINOv2](https://github.com/facebookresearch/dinov2), [BasicSR](https://github.com/XPixelGroup/BasicSR), [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN), [RCGM](https://github.com/LINs-lab/RCGM), and [Shortcut-models](https://github.com/kvfrans/shortcut-models). We thank the authors for their open-source contributions.
+
+## &#x2696;&#xFE0F; License
+
+This project is released under the Apache License 2.0 unless otherwise noted. See [LICENSE](LICENSE) for details. Downloadable model weights, benchmark data, and external assets may have separate terms on their hosting pages.