# L2P: Unlocking Latent Potential for Pixel Generation

An efficient transfer paradigm enabling high-quality, end-to-end pixel-space diffusion with minimal computational overhead and data requirements.

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--- ## 📰 News - **\[2026/05\]** Technical report released. --- ## 🗺️ Roadmap | Status | Item | | :---: | :--- | | 🛠️ | 1K inference code & weights | | 🛠️ | Training code | | 🛠️ | 4K/8K/10K UHR generation | | 🛠️ | Compatibility with more LDM T2I model | --- ## 📦 Installation --- ## 🎨 Inference ```python import torch from diffsynth.pipelines.z_image_L2P import ZImagePipeline, ModelConfig main_model_path = "/path/model-1k-merge.safetensors" text_encoder_paths = [ "/path/Z-Image-Turbo/text_encoder/model-00001-of-00003.safetensors", "/path/Z-Image-Turbo/text_encoder/model-00002-of-00003.safetensors", "/path/Z-Image-Turbo/text_encoder/model-00003-of-00003.safetensors", ] tokenizer_path = "/path/Z-Image-Turbo/tokenizer" pipe = ZImagePipeline.from_pretrained( torch_dtype=torch.bfloat16, device="cuda", model_configs=[ ModelConfig(path=[main_model_path]), ModelConfig(path=text_encoder_paths), ], tokenizer_config=ModelConfig(path=tokenizer_path), ) prompt = "an origami pig on fire in the middle of a dark room with a pentagram on the floor" image = pipe( prompt=prompt, seed=42, rand_device="cuda", num_inference_steps=30, cfg_scale=2.0, height=1024, width=1024, ) image.save("example.png") ``` ### Gradio Demo Launch a multi-GPU web UI: ```bash python app.py ``` The demo auto-detects free GPUs, dispatches each request to an idle device, and exposes a Gradio interface at `http://0.0.0.0:23231`. --- ## 🏋️ Training The full training pipeline consists of four steps: **(1)** prepare the Z-Image base weights → **(2)** convert them into a pixel-space initialization → **(3)** launch training → **(4)** merge the trained delta back with the pixel-init weights for inference. ### Step 1 · Prepare Z-Image weights Download the official **Z-Image-Turbo** checkpoint from Hugging Face: - 🤗 [Tongyi-MAI/Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) ### Step 2 · Offline weight conversion (latent → pixel init) Convert the latent-space DiT weights into a **pixel-space initialization** that L2P can fine-tune from: ```bash python examples/z_image/L2P_convert_weight.py \ --latent_ckpt_files \ /path/to/Z-Image-Turbo/transformer/diffusion_pytorch_model-00001-of-00003.safetensors \ /path/to/Z-Image-Turbo/transformer/diffusion_pytorch_model-00002-of-00003.safetensors \ /path/to/Z-Image-Turbo/transformer/diffusion_pytorch_model-00003-of-00003.safetensors \ --output_path ./pretrain_weight/Z-Image-Pixel-Init/diffusion_pytorch_model.safetensors ``` ### Step 3 · Launch training **Standard training** : ```bash bash train_run.sh ``` **Low-VRAM training** (single GPU < 24 GB VRAM): ```bash bash train_run_low_VRAM.sh ``` #### Dataset format Provide a directory of images plus a CSV metadata file: ``` data/ ├── images/ # raw image folder └── metadata.csv # columns: file_name, text, ... ``` ### Step 4 · Offline weight merge (for inference) ```bash python merge_weights.py \ --file_a ./models/train/L2P_Standard/step-xxx.safetensors \ --file_b ./pretrain_weight/Z-Image-Pixel-Init/diffusion_pytorch_model.safetensors \ --file_out ./models/train/L2P_Standard/model-merge.safetensors ``` - `--file_a`: trained checkpoint from Step 3 - `--file_b`: pixel-init weights from Step 2 - `--file_out`: merged single-file weight --- ## 📜 Citation If you find this work useful, please consider citing: ```bibtex @article{chen2026l2p, title = {L2P: Unlocking Latent Potential for Pixel Generation}, author = {Chen, Zhennan and Zhu, Junwei and Chen, Xu and Zhang, Jiangning and Chen, Jiawei and Zeng, Zhuoqi and Zhang, Wei and Wang, Chengjie and Yang, Jian and Tai, Ying}, journal = {arXiv preprint arXiv:2605.12013}, year = {2026} } @article{chen2025dip, title = {DiP: Taming Diffusion Models in Pixel Space}, author = {Chen, Zhennan and Zhu, Junwei and Chen, Xu and Zhang, Jiangning and Hu, Xiaobin and Zhao, Hanzhen and Wang, Chengjie and Yang, Jian and Tai, Ying}, journal = {arXiv preprint arXiv:2511.18822}, year = {2025} } ``` --- ## 🙏 Acknowledgements L2P is built upon the excellent open-source work of [**DiffSynth-Studio**](https://github.com/modelscope/DiffSynth-Studio), [**Z-Image**](https://github.com/Tongyi-MAI/Z-Image).