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LICENSE

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+MIT License
+
+Copyright (c) 2026 Tencent.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
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+SOFTWARE.

NOTICE

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+Pixal3D
+Copyright (c) 2026 Tencent.
+
+Tencent is pleased to support the community by making Pixal3D available.
+
+The open-source software and/or models included in this distribution may have
+been modified by Tencent (“Tencent Modifications”). All Tencent Modifications
+are Copyright (c) Tencent.
+
+For the avoidance of doubt, Pixal3D refers solely to code, parameters, weights,
+and associated documentation made publicly available by Tencent in accordance
+with the MIT License (see the LICENSE file).
+
+
+================================================================================
+Third-Party Components
+================================================================================
+
+This product includes software developed by third parties. The third-party
+components listed below remain licensed under their respective original terms.
+Pixal3D does not impose any additional restrictions beyond those specified in
+the original licenses of these third-party components. Users are required to
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+In case you believe there have been errors in the attribution below, you may
+submit the concerns to us for review and correction.
+
+
+================================================================================
+Open Model Licensed under Apache-2.0
+================================================================================
+
+1. dinov2
+   Copyright (c) 2025 dinov2 original author and authors
+
+
+================================================================================
+Open Source Software Licensed under the MIT License
+================================================================================
+
+2. TRELLIS.2
+   Copyright (c) Microsoft Corporation.
+
+3. Direct3D-S2
+   Copyright (c) DreamTech.
+
+4. MoGe
+   Copyright (c) Microsoft Corporation.
+
+
+================================================================================
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+================================================================================
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+SOFTWARE.
+
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+================================================================================
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+Apache License
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README.md

@@ -1,3 +1,302 @@
 ---
-license: unknown
+license: mit
+license_link: LICENSE
+extra_gated_eu_disallowed: true
+pipeline_tag: image-to-3d
 ---
+
+
+<div align="center">
+
+# Pixal3D: Pixel-Aligned 3D Generation from Images
+
+<h3>SIGGRAPH 2026</h3>
+
+<small>[Dong-Yang Li](https://ldyang694.github.io/)¹ · [Wang Zhao](https://thuzhaowang.github.io/)²* · [Yuxin Chen](https://orcid.org/0000-0002-7854-1072)² · [Wenbo Hu](https://wbhu.github.io/)² · [Meng-Hao Guo](https://menghaoguo.github.io/)¹ · [Fang-Lue Zhang](https://fanglue.github.io/)³ · [Ying Shan](https://www.linkedin.com/in/YingShanProfile)² · [Shi-Min Hu](https://cg.cs.tsinghua.edu.cn/shimin.htm)¹✉</small>
+
+¹Tsinghua University (BNRist) &nbsp;&nbsp; ²Tencent ARC Lab &nbsp;&nbsp; ³Victoria University of Wellington
+
+*Project lead &nbsp;&nbsp; ✉Corresponding author
+
+</div>
+
+<div align="center">
+  <a href="https://ldyang694.github.io/projects/pixal3d/"><img src=https://img.shields.io/badge/Project%20Page-333399.svg?logo=googlehome height=22px></a>
+  <a href="https://github.com/TencentARC/Pixal3D"><img src=https://img.shields.io/badge/GitHub-181717.svg?logo=github&logoColor=white height=22px></a>
+  <a href="https://huggingface.co/spaces/TencentARC/Pixal3D"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Demo-276cb4.svg height=22px></a>
+  <a href="https://huggingface.co/TencentARC/Pixal3D"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Models-d96902.svg height=22px></a>
+  <a href="https://arxiv.org/abs/2605.10922"><img src=https://img.shields.io/badge/Arxiv-b5212f.svg?logo=arxiv height=22px></a>
+  <a href="LICENSE"><img src=https://img.shields.io/badge/License-MIT-yellow.svg height=22px></a>
+</div>
+
+
+**Pixal3D** generates high-fidelity 3D assets from a single image. Unlike previous methods that loosely inject image features via attention, Pixal3D explicitly lifts pixel features into 3D through back-projection, establishing direct pixel-to-3D correspondences. This enables near-reconstruction-level fidelity with detailed geometry and PBR textures.
+
+---
+
+## ✨ News
+
+- **May 2026**: Release training code and data preparation toolkit. 🔧
+- **May 2026**: Release the improved version based on [Trellis.2](https://github.com/microsoft/TRELLIS.2) backbone. 💪
+- **May 2026**: Release inference code and online demo. 🤗
+- **Apr 2026**: Our paper is accepted to SIGGRAPH 2026! 🎉
+
+## 📌 Branches
+
+| Branch | Description |
+|--------|-------------|
+| `main` | **Latest version** — improved implementation based on [Trellis.2](https://github.com/microsoft/TRELLIS.2) backbone with better performance. |
+| `paper` | **Paper version** — original implementation based on [Direct3D-S2](https://github.com/DreamTechAI/Direct3D-S2), corresponding to results reported in our SIGGRAPH 2026 paper. |
+
+> If you want to reproduce the results in our paper, please switch to the `paper` branch.
+
+## 🎮 Try It Online
+
+You can try Pixal3D directly in your browser without any installation via our Hugging Face Gradio demo:
+
+👉 [**Launch Demo**](https://huggingface.co/spaces/TencentARC/Pixal3D)
+
+## 🚀 Getting Started
+
+### Installation
+
+#### Step 1: Follow TRELLIS.2 Installation
+
+Please first follow the installation guide of [TRELLIS.2](https://github.com/microsoft/TRELLIS.2) to set up the base environment.
+
+#### Step 2: Install Additional Dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+#### Step 3: Install natten
+
+```bash
+NATTEN_CUDA_ARCH="xx" NATTEN_N_WORKERS=xx pip install natten==0.21.0 --no-build-isolation
+```
+
+Please replace `xx` with the CUDA architecture and the number of build workers suitable for your machine.
+
+#### Step 4: Install utils3d
+
+```bash
+pip install https://github.com/LDYang694/Storages/releases/download/20260430/utils3d-0.0.2-py3-none-any.whl
+```
+
+> **Note**: `requirements-hfdemo.txt` is for the Hugging Face Spaces demo (H-series GPU architecture) and may not be compatible with other architectures.
+
+### Usage
+
+#### Inference
+
+Generate a GLB mesh from a single image:
+
+```bash
+python inference.py --image assets/images/0_img.png --output ./output.glb
+```
+
+**Low-VRAM mode** (reduces peak VRAM by loading models on-demand):
+
+```bash
+python inference.py --image assets/images/0_img.png --output ./output.glb --low_vram
+```
+
+By default, the pipeline resolution is **1536** (standard mode) or **1024** (low-VRAM mode). You can override this with `--resolution`:
+
+```bash
+# Force 1536 even in low-VRAM mode
+python inference.py --image assets/images/0_img.png --output ./output.glb --low_vram --resolution 1536
+
+# Force 1024 in standard mode
+python inference.py --image assets/images/0_img.png --output ./output.glb --resolution 1024
+```
+
+**Tip**: If you don't have `flash_attn` installed, you can use PyTorch's built-in SDPA backend instead:
+> ```bash
+> ATTN_BACKEND=sdpa python inference.py --image assets/images/0_img.png --output ./output.glb --low_vram
+> ```
+
+### Web Demo
+
+We provide a Gradio web demo for Pixal3D, which allows you to generate 3D meshes from images interactively.
+
+```bash
+python app.py 
+```
+
+Low-VRAM mode is also available for the web demo. The frontend default resolution will automatically switch to 1024 in low-VRAM mode (1536 otherwise), but can be changed manually in the UI.
+
+```bash
+python app.py --low_vram
+# or via environment variable:
+LOW_VRAM=1 python app.py
+```
+## 🔧 Training
+
+We provide the full training codebase for reproducing Pixal3D from scratch.
+
+### Data Preparation
+
+Prepare view-aligned O-Voxel data and rendered condition images by following the data toolkit instructions:
+
+> 📂 **[data_toolkit/README.md](data_toolkit/README.md)**
+
+### Overview
+
+Pixal3D is trained as a three-stage cascade, each progressively increasing resolution:
+
+| Stage | Model | Resolutions | Config Prefix |
+|-------|-------|-------------|---------------|
+| 1 | Sparse Structure | 32 → 64 | `ss_flow_img_dit_*_proj_finetune` |
+| 2 | Shape | 256 → 512 → 1024 | `slat_flow_img2shape_*_proj_finetune` |
+| 3 | Texture | 256 → 512 → 1024 | `slat_flow_imgshape2tex_*_proj_finetune` |
+
+All stages use **pixel-aligned projection conditioning** and **view-aligned latents** (2 views by default). Within each stage, start from the lowest resolution and progressively fine-tune to higher resolutions by setting `finetune_ckpt` in the config.
+
+### Quick Start
+
+```sh
+python train.py \
+  --config <CONFIG_JSON> \
+  --output_dir <OUTPUT_DIR> \
+  --data_dir '<DATA_DIR_JSON>'
+```
+
+`--data_dir` is a JSON string describing the dataset layout. Different stages require different keys:
+
+| Stage | Required keys |
+|-------|---------------|
+| Sparse Structure | `base`, `ss_latent`, `render_cond` |
+| Shape | `base`, `shape_latent`, `render_cond` |
+| Texture | `base`, `shape_latent`, `pbr_latent`, `render_cond` |
+
+### Example: Training All Three Stages
+
+Below we show the full training sequence using ObjaverseXL as an example. Each higher-resolution step requires updating `finetune_ckpt` in its config JSON to point to the previous checkpoint.
+
+<details>
+<summary><b>Stage 1: Sparse Structure (32 → 64)</b></summary>
+
+```sh
+# Resolution 32
+python train.py \
+  --config configs/gen/ss_flow_img_dit_1_3B_32_bf16_proj_finetune.json \
+  --output_dir results/ss_32 \
+  --data_dir '{"ObjaverseXL_sketchfab": {"base": "datasets/ObjaverseXL_sketchfab", "ss_latent": "datasets/ObjaverseXL_sketchfab/ss_latents/ss_enc_conv3d_16l8_fp16_64_view", "render_cond": "datasets/ObjaverseXL_sketchfab/renders_cond"}}'
+
+# Resolution 64 (set finetune_ckpt → results/ss_32 checkpoint)
+python train.py \
+  --config configs/gen/ss_flow_img_dit_1_3B_32_bf16_proj_finetune_ft64.json \
+  --output_dir results/ss_ft64 \
+  --data_dir '{"ObjaverseXL_sketchfab": {"base": "datasets/ObjaverseXL_sketchfab", "ss_latent": "datasets/ObjaverseXL_sketchfab/ss_latents/ss_enc_conv3d_16l8_fp16_64_view", "render_cond": "datasets/ObjaverseXL_sketchfab/renders_cond"}}'
+```
+</details>
+
+<details>
+<summary><b>Stage 2: Shape (256 → 512 → 1024)</b></summary>
+
+```sh
+# Resolution 256
+python train.py \
+  --config configs/gen/slat_flow_img2shape_dit_1_3B_256_bf16_proj_finetune.json \
+  --output_dir results/shape_256 \
+  --data_dir '{"ObjaverseXL_sketchfab": {"base": "datasets/ObjaverseXL_sketchfab", "shape_latent": "datasets/ObjaverseXL_sketchfab/shape_latents/shape_enc_next_dc_f16c32_fp16_256_view", "render_cond": "datasets/ObjaverseXL_sketchfab/renders_cond"}}'
+
+# Resolution 512
+python train.py \
+  --config configs/gen/slat_flow_img2shape_dit_1_3B_256_bf16_proj_finetune_ft512.json \
+  --output_dir results/shape_ft512 \
+  --data_dir '{"ObjaverseXL_sketchfab": {"base": "datasets/ObjaverseXL_sketchfab", "shape_latent": "datasets/ObjaverseXL_sketchfab/shape_latents/shape_enc_next_dc_f16c32_fp16_512_view", "render_cond": "datasets/ObjaverseXL_sketchfab/renders_cond"}}'
+
+# Resolution 1024
+python train.py \
+  --config configs/gen/slat_flow_img2shape_dit_1_3B_512_bf16_proj_finetune_ft1024.json \
+  --output_dir results/shape_ft1024 \
+  --data_dir '{"ObjaverseXL_sketchfab": {"base": "datasets/ObjaverseXL_sketchfab", "shape_latent": "datasets/ObjaverseXL_sketchfab/shape_latents/shape_enc_next_dc_f16c32_fp16_1024_view", "render_cond": "datasets/ObjaverseXL_sketchfab/renders_cond"}}'
+```
+</details>
+
+<details>
+<summary><b>Stage 3: Texture (256 → 512 → 1024)</b></summary>
+
+```sh
+# Resolution 256
+python train.py \
+  --config configs/gen/slat_flow_imgshape2tex_dit_1_3B_256_bf16_proj_finetune.json \
+  --output_dir results/tex_256 \
+  --data_dir '{"ObjaverseXL_sketchfab": {"base": "datasets/ObjaverseXL_sketchfab", "shape_latent": "datasets/ObjaverseXL_sketchfab/shape_latents/shape_enc_next_dc_f16c32_fp16_256_view", "pbr_latent": "datasets/ObjaverseXL_sketchfab/pbr_latents/tex_enc_next_dc_f16c32_fp16_256_view", "render_cond": "datasets/ObjaverseXL_sketchfab/renders_cond"}}'
+
+# Resolution 512
+python train.py \
+  --config configs/gen/slat_flow_imgshape2tex_dit_1_3B_512_bf16_proj_finetune.json \
+  --output_dir results/tex_512 \
+  --data_dir '{"ObjaverseXL_sketchfab": {"base": "datasets/ObjaverseXL_sketchfab", "shape_latent": "datasets/ObjaverseXL_sketchfab/shape_latents/shape_enc_next_dc_f16c32_fp16_512_view", "pbr_latent": "datasets/ObjaverseXL_sketchfab/pbr_latents/tex_enc_next_dc_f16c32_fp16_512_view", "render_cond": "datasets/ObjaverseXL_sketchfab/renders_cond"}}'
+
+# Resolution 1024
+python train.py \
+  --config configs/gen/slat_flow_imgshape2tex_dit_1_3B_512_bf16_proj_finetune_ft1024.json \
+  --output_dir results/tex_ft1024 \
+  --data_dir '{"ObjaverseXL_sketchfab": {"base": "datasets/ObjaverseXL_sketchfab", "shape_latent": "datasets/ObjaverseXL_sketchfab/shape_latents/shape_enc_next_dc_f16c32_fp16_1024_view", "pbr_latent": "datasets/ObjaverseXL_sketchfab/pbr_latents/tex_enc_next_dc_f16c32_fp16_1024_view", "render_cond": "datasets/ObjaverseXL_sketchfab/renders_cond"}}'
+```
+</details>
+
+### Additional Options
+
+<details>
+<summary><b>All command-line arguments</b></summary>
+
+| Argument | Description | Default |
+|----------|-------------|---------|
+| `--config` | Config JSON path | *required* |
+| `--output_dir` | Output directory | *required* |
+| `--data_dir` | Dataset JSON string | `./data/` |
+| `--load_dir` | Checkpoint load directory | `output_dir` |
+| `--ckpt` | Resume from step | `latest` |
+| `--auto_retry` | Retries on failure | `3` |
+| `--tryrun` | Dry run | `false` |
+| `--profile` | Profiling | `false` |
+| `--num_nodes` | Number of nodes | `1` |
+| `--node_rank` | Current node rank | `0` |
+| `--num_gpus` | GPUs per node | all |
+| `--master_addr` | Master address | `localhost` |
+| `--master_port` | Master port | `12666` |
+| `--use_wandb` | Enable W&B logging | `false` |
+| `--wandb_project` | W&B project | `trellis2-training` |
+| `--wandb_name` | W&B run name | basename of `output_dir` |
+| `--wandb_id` | W&B run ID (resume) | — |
+
+</details>
+
+## 🌐 Community Projects
+
+We thank the community for building extensions and deployment guides for Pixal3D!
+
+- [Pixal3D-ComfyUI](https://github.com/Saganaki22/Pixal3D-ComfyUI) — ComfyUI integration with deployment guides for Windows, WSL, and more.
+
+## 🤗 Acknowledgements
+
+This project is heavily built upon [Trellis.2](https://github.com/microsoft/TRELLIS.2) and [Direct3D-S2](https://github.com/DreamTechAI/Direct3D-S2). We sincerely thank the authors for their outstanding work on scalable 3D generation , which serves as the foundation of our codebase and model architecture.
+
+We also thank the following repos for their great contributions:
+
+- [Direct3D-S2](https://github.com/DreamTechAI/Direct3D-S2)
+- [Trellis](https://github.com/microsoft/TRELLIS)
+- [Trellis.2](https://github.com/microsoft/TRELLIS.2)
+
+## 📄 Citation
+
+If you find this work useful, please consider citing:
+
+```bibtex
+@article{li2026pixal3d,
+    title={Pixal3D: Pixel-Aligned 3D Generation from Images},
+    author={Li, Dong-Yang and Zhao, Wang and Chen, Yuxin and Hu, Wenbo and Guo, Meng-Hao and Zhang, Fang-Lue and Shan, Ying and Hu, Shi-Min},
+    journal={arXiv preprint arXiv:2605.10922},
+    year={2026}
+}
+```
+
+## 📜 License
+
+This project is released under the [MIT License](LICENSE). The third-party components included in this project remain licensed under their respective original terms; see [NOTICE](NOTICE) for the full list of dependencies and their licenses.

ckpts/shape_dec_next_dc_f16c32_fp16.json

@@ -0,0 +1,24 @@
+{
+    "name": "FlexiDualGridVaeDecoder",
+    "args": {
+        "resolution": 256,
+        "model_channels": [1024, 512, 256, 128, 64],
+        "latent_channels": 32,
+        "num_blocks": [4, 16, 8, 4, 0],
+        "block_type": [
+            "SparseConvNeXtBlock3d",
+            "SparseConvNeXtBlock3d",
+            "SparseConvNeXtBlock3d",
+            "SparseConvNeXtBlock3d",
+            "SparseConvNeXtBlock3d"
+        ],
+        "up_block_type": [
+            "SparseResBlockC2S3d",
+            "SparseResBlockC2S3d",
+            "SparseResBlockC2S3d",
+            "SparseResBlockC2S3d"
+        ],
+        "block_args": [{}, {}, {}, {}, {}],
+        "use_fp16": true
+    }
+}

ckpts/shape_dec_next_dc_f16c32_fp16.safetensors

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ckpts/slat_flow_img2shape_dit_1_3B_1024_bf16.json

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+{
+    "name": "ElasticSLatFlowModel",
+    "args": {
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+        "in_channels": 32,
+        "out_channels": 32,
+        "model_channels": 1536,
+        "cond_channels": 1024,
+        "num_blocks": 30,
+        "num_heads": 12,
+        "mlp_ratio": 5.3334,
+        "pe_mode": "rope",
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+        "qk_rms_norm_cross": true,
+        "image_attn_mode": "proj",
+        "proj_in_channels": 2048,
+        "dtype": "bfloat16"
+    }
+}

ckpts/slat_flow_img2shape_dit_1_3B_1024_bf16.safetensors

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ckpts/slat_flow_img2shape_dit_1_3B_512_bf16.json

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+{
+    "name": "ElasticSLatFlowModel",
+    "args": {
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+        "in_channels": 32,
+        "out_channels": 32,
+        "model_channels": 1536,
+        "cond_channels": 1024,
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+        "mlp_ratio": 5.3334,
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+        "image_attn_mode": "proj",
+        "proj_in_channels": 2048,
+        "dtype": "bfloat16"
+    }
+}

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ckpts/slat_flow_imgshape2tex_dit_1_3B_1024_bf16.json

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+{
+    "name": "ElasticSLatFlowModel",
+    "args": {
+        "resolution": 64,
+        "in_channels": 64,
+        "out_channels": 32,
+        "model_channels": 1536,
+        "cond_channels": 1024,
+        "num_blocks": 30,
+        "num_heads": 12,
+        "mlp_ratio": 5.3334,
+        "pe_mode": "rope",
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+        "qk_rms_norm_cross": true,
+        "image_attn_mode": "proj",
+        "proj_in_channels": 2048,
+        "dtype": "bfloat16"
+    }
+}

ckpts/slat_flow_imgshape2tex_dit_1_3B_1024_bf16.safetensors

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ckpts/ss_dec_conv3d_16l8_fp16.json

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+
+{
+    "name": "SparseStructureDecoder",
+    "args": {
+        "out_channels": 1,
+        "latent_channels": 8,
+        "num_res_blocks": 2,
+        "num_res_blocks_middle": 2,
+        "channels": [512, 128, 32],
+        "use_fp16": true
+    }
+}

ckpts/ss_dec_conv3d_16l8_fp16.safetensors

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ckpts/ss_flow_img_dit_1_3B_64_bf16.json

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+{
+    "name": "SparseStructureFlowModel",
+    "args": {
+        "resolution": 16,
+        "in_channels": 8,
+        "out_channels": 8,
+        "model_channels": 1536,
+        "cond_channels": 1024,
+        "num_blocks": 30,
+        "num_heads": 12,
+        "mlp_ratio": 5.3334,
+        "pe_mode": "rope",
+        "share_mod": true,
+        "initialization": "scaled",
+        "qk_rms_norm": true,
+        "qk_rms_norm_cross": true,
+        "image_attn_mode": "proj",
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+    }
+}

ckpts/ss_flow_img_dit_1_3B_64_bf16.safetensors

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ckpts/tex_dec_next_dc_f16c32_fp16.json

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+{
+    "name": "SparseUnetVaeDecoder",
+    "args": {
+        "out_channels": 6,
+        "model_channels": [1024, 512, 256, 128, 64],
+        "latent_channels": 32,
+        "num_blocks": [4, 16, 8, 4, 0],
+        "block_type": [
+            "SparseConvNeXtBlock3d",
+            "SparseConvNeXtBlock3d",
+            "SparseConvNeXtBlock3d",
+            "SparseConvNeXtBlock3d",
+            "SparseConvNeXtBlock3d"
+        ],
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+            "SparseResBlockC2S3d",
+            "SparseResBlockC2S3d",
+            "SparseResBlockC2S3d",
+            "SparseResBlockC2S3d"
+        ],
+        "block_args": [{}, {}, {}, {}, {}],
+        "pred_subdiv": false,
+        "use_fp16": true
+    }
+}

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pipeline.json

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+{
+    "name": "Trellis2ImageTo3DPipeline",
+    "args": {
+        "models": {
+            "sparse_structure_decoder": "ckpts/ss_dec_conv3d_16l8_fp16",
+            "sparse_structure_flow_model": "ckpts/ss_flow_img_dit_1_3B_64_bf16",
+            "shape_slat_decoder": "ckpts/shape_dec_next_dc_f16c32_fp16",
+            "shape_slat_flow_model_512": "ckpts/slat_flow_img2shape_dit_1_3B_512_bf16",
+            "shape_slat_flow_model_1024": "ckpts/slat_flow_img2shape_dit_1_3B_1024_bf16",
+            "tex_slat_decoder": "ckpts/tex_dec_next_dc_f16c32_fp16",
+            "tex_slat_flow_model_1024": "ckpts/slat_flow_imgshape2tex_dit_1_3B_1024_bf16"
+        },
+        "sparse_structure_sampler": {
+            "name": "FlowEulerGuidanceIntervalSampler",
+            "args": {
+                "sigma_min": 1e-5
+            },
+            "params": {
+                "steps": 12,
+                "guidance_strength": 7.5,
+                "guidance_rescale": 0.7,
+                "guidance_interval": [0.6, 1.0],
+                "rescale_t": 5.0
+            }
+        },
+        "shape_slat_sampler": {
+            "name": "FlowEulerGuidanceIntervalSampler",
+            "args": {
+                "sigma_min": 1e-5
+            },
+            "params": {
+                "steps": 12,
+                "guidance_strength": 7.5,
+                "guidance_rescale": 0.5,
+                "guidance_interval": [0.6, 1.0],
+                "rescale_t": 3.0
+            }
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+                -0.732996, 2.604095, -0.118341, -2.143904, 0.495076, -2.179512, -2.130751, -0.996944,
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+                -0.059621, 2.220780, 1.621212, 0.877230, 0.567247, -3.175944, -3.186688, 1.578665
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+                5.226681, 5.683095, 4.831436, 5.286469, 5.652043, 5.367606, 5.525084, 4.730578,
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+                5.634698, 5.235274, 6.110351, 5.511298, 6.237273, 4.879207, 5.347008, 5.405691
+            ]
+        },
+        "tex_slat_sampler": {
+            "name": "FlowEulerGuidanceIntervalSampler",
+            "args": {
+                "sigma_min": 1e-5
+            },
+            "params": {
+                "steps": 12,
+                "guidance_strength": 1.0,
+                "guidance_rescale": 0.0,
+                "guidance_interval": [0.6, 0.9],
+                "rescale_t": 3.0
+            }
+        },
+        "tex_slat_normalization": {
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+                0.029398, -0.339596, -0.869527, 1.038479, -0.972385, 0.126042, -1.129303, 0.455149,
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+                1.213905, 0.971588, -0.023631, 0.106750, 2.021786, 0.250524, -0.662387, -0.768862
+            ],
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+                2.969419, 2.501689, 2.154811, 3.163343, 2.621215, 2.381943, 3.186697, 3.021588,
+                2.295916, 3.234985, 3.233086, 2.260140, 2.874801, 2.810596, 3.292720, 2.674999,
+                2.680878, 2.372054, 2.451546, 2.353556, 2.995195, 2.379849, 2.786195, 2.775190
+            ]
+        },
+        "image_cond_model": {
+            "name": "DinoV3FeatureExtractor",
+            "args": {
+                "model_name": "facebook/dinov3-vitl16-pretrain-lvd1689m"
+            }
+        },
+        "rembg_model": {
+            "name": "BiRefNet",
+            "args": {
+                "model_name": "briaai/RMBG-2.0"
+            }
+        },
+        "default_pipeline_type": "1536_cascade"
+    }
+}