AllGPTORG/VOSR_1.4B_Mobile

VOSR 1.4B Mobile

AllGPTORG/VOSR_1.4B_Mobile is a mobile deployment package of the VOSR-1.4B one-step image super-resolution model, quantized and compiled as Qualcomm QNN DLC graphs for the Snapdragon 8 Gen 3 NPU.

The package is designed for high-quality image restoration and super-resolution, including text-rich images. It preserves the original one-step VOSR pipeline while splitting the network into smaller graphs suitable for mobile integration.

This repository contains QNN deployment artifacts. It is not a Transformers or Diffusers checkpoint and cannot be loaded with from_pretrained().

Model summary

Property	Value
Base model	CSWRY/VOSR, VOSR-1.4B one-step
Task	Generative image restoration and super-resolution
Target SoC	Qualcomm Snapdragon 8 Gen 3 / SM8650
Compile target	Samsung Galaxy S24 family, Android 14
Runtime format	Qualcomm QNN DLC
Static tile size	512 x 512 pixels
Batch size	1
Activations	FP16 (A16)
DiT block weights	INT4 (W4A16)
Auxiliary/final graph weights	INT8 (W8A16)
Total DLC size	1,736,469,708 bytes / 1.617 GiB

Files

The graphs must be executed in the order shown below.

Order	File	Precision	Size
1	vosr_dinov2l_layer17.dlc	W8A16	229.50 MiB
2	vosr_qwen_vae_encoder.dlc	W8A16	19.09 MiB
3	vosr_dit_prepare.dlc	W8A16	42.61 MiB
4	vosr_dit_blocks_00_12.dlc	W4A16	444.86 MiB
5	vosr_dit_blocks_12_18.dlc	W4A16	222.62 MiB
6	vosr_dit_blocks_18_24.dlc	W4A16	222.62 MiB
7	vosr_dit_blocks_24_36.dlc	W4A16	444.86 MiB
8	vosr_dit_final.dlc	W8A16	4.90 MiB
9	vosr_qwen_vae_decoder.dlc	W8A16	24.97 MiB

manifest.json contains the same graph order, precision assignment, and exact byte size for programmatic use.

Tensor interface

All image and latent tensors use NCHW layout.

Graph	Inputs	Outputs
DINOv2-L layer 17	lq_image: FP16 [1,3,512,512]	dino_features: FP16 [1,1024,1024]
Qwen VAE encoder	lq_image: FP16 [1,3,512,512]; posterior_noise: FP16 [1,16,64,64]	lq_latent: FP16 [1,16,64,64]
DiT prepare	latent_pair: FP16 [1,32,64,64]; timestep: FP32 [1]; next_timestep: FP32 [1]; dino_features: FP16 [1,1024,1024]	hidden: FP16 [1,1024,1536]; conditioning: FP16 [1,1536]; block_conditioning: FP16 [1,9216]; projected_dino: FP16 [1,1024,1536]
DiT block stages	hidden, block_conditioning, projected_dino	hidden_out: FP16 [1,1024,1536]
DiT final	hidden: FP16 [1,1024,1536]; conditioning: FP16 [1,1536]	velocity: FP16 [1,16,64,64]
Qwen VAE decoder	normalized_latent: FP16 [1,16,64,64]	sr_image: FP16 [1,3,512,512]

Integration outline

Use the Qualcomm AI Engine Direct SDK / QNN runtime to load and execute the DLCs. The host application is responsible for preprocessing, graph orchestration, random noise generation, the one-step latent update, tiling, and image postprocessing.

1. Resize the low-resolution image to the requested output resolution using bicubic interpolation, split it into 512 x 512 tiles if needed, convert RGB values to FP16 NCHW, and normalize them to [-1, 1].
2. Run the DINO graph and Qwen VAE encoder on the same image tile. Supply seeded normal noise as posterior_noise if reproducible output is required.
3. Create an FP16 normal-noise latent z with shape [1,16,64,64], concatenate lq_latent and z along the channel axis, and use the result as latent_pair.
4. For the one-step schedule, run DiT prepare with timestep = [1.0] and next_timestep = [0.0].
5. Pass hidden sequentially through all four DiT block-stage DLCs. Reuse block_conditioning and projected_dino for every stage.
6. Run DiT final and apply the one-step update z = z - velocity.
7. Decode the updated latent with the Qwen VAE decoder, clamp the output to [-1, 1], convert it back to RGB, and blend overlapping tiles when tiling.

For 4x super-resolution, bicubic-upscale the source to the final target resolution before creating the model tiles. The network then restores detail at that target resolution.

Validation status

• All nine graphs were quantized with representative intermediate activations.
• All nine graphs were successfully compiled to QNN DLC format for SM8650.
• The Qwen VAE encoder and DiT prepare graphs were profiled on the Snapdragon 8 Gen 3 NPU.
• A single pre-linked QNN context is intentionally not included. Linking the entire hybrid package exceeded the cloud linker memory limit, so applications should execute the DLC sequence or link it with a compatible local QNN SDK.

Performance, memory use, and image quality depend on the QNN SDK version, device firmware, thermal state, tiling implementation, and host-side orchestration. Test on the exact target device before shipping a production application.

Intended use

This package is intended for research and mobile application development involving:

• photo and screenshot restoration;
• text-rich image enhancement;
• single-image super-resolution;
• Snapdragon NPU deployment experiments.

It is not intended for forensic reconstruction or for recovering information that is not present in the source image. Generative restoration can introduce plausible but incorrect details.

Attribution

This is a quantized mobile derivative of VOSR. The original architecture, training, and checkpoints were created by the VOSR authors. See the official project and paper for full details.

Citation

If you use this model, please cite the original VOSR work:

@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}
}

License

Released under the Apache License 2.0, following the upstream VOSR repository. Users are responsible for reviewing and complying with the licenses and terms of all upstream components and the Qualcomm QNN SDK/runtime used for deployment.