| --- |
| language: |
| - en |
| pipeline_tag: text-to-image |
| license: apache-2.0 |
| tags: |
| - pvd |
| - image-generation |
| - class-conditional |
| - diffusion |
| - lora |
| - pytorch |
| --- |
| <div align="center"> |
| <h1>Two Halves are More than One: Phase-wise Velocity Distillation for Fast and High-Quality Image Generation</h1> |
|
|
| <a href="https://arxiv.org/abs/2610.08070"><img src="https://img.shields.io/badge/Paper-arXiv-red" alt="Paper"></a> |
| <a href="https://github.com/PolyU-VCLab/PVD"><img src="https://img.shields.io/badge/Code-Github-blue" alt="Code"></a> |
| <a href="https://huggingface.co/VCLab-PolyU/PVD"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Model-HuggingFace-yellow" alt="Model"></a> |
| <a href="https://huggingface.co/papers/2610.08070"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Paper-HuggingFace-yellow" alt="HF Paper"></a> |
| </div> |
|
|
| Model weights for PVD class-conditioned image generation (C2I) and text-to-image generation with SD3.5 Medium, FLUX.1-dev and Qwen-Image. |
|
|
| ## Model Details |
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|
| | Variant | Weight directory | Released components | Configuration | |
| | --- | --- | --- | --- | |
| | C2I | `weights/pvd_c2i/` | `model.safetensors`; VAE and latent statistics in `cache/` | ImageNet classes, 256×256 | |
| | SD3.5 Medium | `weights/pvd_sd35m/` | `part1.safetensors`, `part2.safetensors`, `model_config.json` | 12 layers per transformer | |
| | SD3.5 Medium (Unsplash) | `weights/pvd_sd35m_unsplash/` | `part1.safetensors`, `part2.safetensors`, `model_config.json` | Further trained on the Unsplash dataset | |
| | FLUX.1-dev | `weights/pvd_flux/` | `backbone.safetensors`, `part1/` and `part2/` LoRA adapters | 9 dual-stream + 19 single-stream layers; rank 64 | |
| | FLUX.1-dev (Unsplash) | `weights/pvd_flux_unsplash/` | `part1/` and `part2/` LoRA adapters; reuses `weights/pvd_flux/backbone.safetensors` | Further trained on the Unsplash dataset | |
| | Qwen-Image | `weights/pvd_qwenimage/` | `backbone.safetensors`, `part1/` and `part2/` LoRA adapters | 30 layers; rank 32 | |
| | Qwen-Image (Unsplash) | `weights/pvd_qwen_unsplash/` | `part1/` and `part2/` LoRA adapters; reuses `weights/pvd_qwenimage/backbone.safetensors` | Further trained on the Unsplash dataset | |
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|
| Each adapter folder contains `adapter_model.safetensors` and `adapter_config.json`. The C2I decoder and latent statistics are `cache/vavae-imagenet256-f16d32-dinov2.pt` and `cache/latents_stats.pt`. |
|
|
| ## Usage |
|
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| Place this repository's `weights/` and `cache/` folders in the [PVD code repository](https://github.com/PolyU-VCLab/PVD) root. Keep the folder names as listed above. See the code repository's README for base model components and inference commands. |
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