README.md

---
license: apache-2.0
language:
- en
pipeline_tag: image-to-image
library_name: pytorch
tags:
- image-to-image
- pose-transfer
- pose-guided
- human-pose
- keypoints
- diffusion
- dit
- rectified-flow
- vtuber
- anime
- windows
- cuda
---

# VTM-Elf 0.01

Pose-conditioned character generation for VTuber pipelines.  
**Status:** beta · **Platform:** Windows + NVIDIA CUDA only

---

## Architecture

- **Dual-stream DiT-30M** (pose + identity in one backbone)
- Pose from keypoints; identity from a reference image
- Rectified flow + SD VAE @ **768×768**
- Checkpoint: `VTM-elf.pt-000060000` (~436 MB, step 60k)

---

## Measured latency

| Hardware | Resolution | Observed rate |
|---|---|---|
| **RTX 5060 Ti** | 768×768 | **~7 FPS** |

---

## Data

Training used a small private character set:

- ~**1,500** characters  
- ~**16–30** images per character  

Generation quality in this release is limited mainly by **model capacity (DiT-30M)** and **dataset scale**.

---

## Getting started

### Requirements

- **Windows**
- **NVIDIA GPU + CUDA**
- Other OS / AMD / CPU: **not supported** in this release

### Download

```bash
hf download sinBoo1/VTM-Elf-0.01 VTM-elf.pt-000060000 --local-dir ./VTM-Elf-0.01
```

```python
from huggingface_hub import hf_hub_download

ckpt = hf_hub_download(
    repo_id="sinBoo1/VTM-Elf-0.01",
    filename="VTM-elf.pt-000060000",
)
```

### Files

| File | Description |
|---|---|
| `VTM-elf.pt-000060000` | DiT-30M weights (step 60k) |
| `README.md` | This card |

Runtime code for live camera → keypoints → inference is separate from this weight repo.

---

## Limitations

- Beta: soft detail, identity drift, and pose errors are common  
- Small data + DiT-30M capacity constrain image quality  
- ~7 FPS on RTX 5060 Ti at 768  
- Windows + CUDA only  
- **Framing:** torso-up only (roughly head → mid-torso). Legs / most of the waist are not supported  
- **Hands:** not supported  
- **Character types not supported:** realistic humans; non-humanoid / furries  
- **Accessories:** glasses and hats generally work; most other accessories are not supported  

---

## Intended use

Research and prototyping of VTuber pose → image pipelines (live drive, pose retarget experiments). Not a finished production renderer.