# VNAsset

Fast CLI pipeline for visual novel image asset generation.

Drop-in replacement for the ComfyUI workflow loop: generate base character sprites
with SDXL, then batch-edit variants (expressions, outfits) with Qwen Image Edit —
all in one warm session, no node-graph overhead.

## Hardware

Built for **AMD Strix Halo** (Ryzen AI Max 395 Pro, Radeon 8060S, 128 GB unified
memory). Also works on discrete AMD GPUs with ROCm. NVIDIA support is untested
but should work if you swap the torch backend.

## Install

```bash
git clone <repo> vnassets
cd vnassets

# Create venv (Python 3.12 required for ROCm torch compatibility)
python3.12 -m venv .venv
source .venv/bin/activate

# Install ROCm PyTorch (adjust index URL for your ROCm version)
pip install torch --index-url https://download.pytorch.org/whl/rocm7.2

# Install the rest
pip install -e .
```

### Models

Symlink your ComfyUI models into `models/`:

```bash
cd models
ln -s /path/to/ComfyUI/models/checkpoints/novaAnimeXL_ilV190.safetensors .
ln -s /path/to/ComfyUI/models/diffusion_models/qwen_image_edit_2509_fp8_e4m3fn.safetensors .
ln -s /path/to/ComfyUI/models/text_encoders/qwen_2.5_vl_7b_fp8_scaled.safetensors .
ln -s /path/to/ComfyUI/models/vae/qwen_image_vae.safetensors .
ln -s /path/to/ComfyUI/models/loras/Qwen-Image-Edit-2509-Lightning-4steps-V1.0-bf16.safetensors .
```

Or place the actual files there — the tool just reads whatever safetensors you
point it at.

## Usage

### Generate (SDXL text-to-image)

```bash
vnasset generate \
  --checkpoint models/novaAnimeXL_ilV190.safetensors \
  --prompt "1girl, solo, red hair, glasses, blue eyes, white crop top, standing, portrait" \
  --negative-prompt "deformed, ugly, bad quality, lowres" \
  --steps 20 \
  --seed 42 \
  --output output/character_base.png
```

| Option | Default | Description |
|--------|---------|-------------|
| `--checkpoint` | (required) | Path to SDXL `.safetensors` |
| `--prompt` | (required) | Positive prompt |
| `--negative-prompt` | `""` | Negative prompt |
| `--width` | `1024` | Image width |
| `--height` | `1024` | Image height |
| `--steps` | `20` | Inference steps |
| `--cfg` | `4.5` | CFG scale |
| `--seed` | `0` | RNG seed (use `random` for random) |
| `--output` | `output.png` | Output path |
| `--raw` | `false` | Disable Compel prompt weighting (fall back to plain diffusers encoding) |

When `--seed` is `random`, a random seed is generated and recorded in the
metadata file.

### Output

Each generation produces:
- `{output}.png` — the image
- `{output}.json` — metadata (prompt, seed, model path, timing, resolution)

Directories in `--output` are created automatically.

### Edit (Qwen Image Edit)

```bash
vnasset edit \
  --model models/qwen_image_edit_2509_fp8_e4m3fn.safetensors \
  --input character_base.png \
  --prompt "make her smile happily" \
  --steps 4 --cfg 1.0 \
  --lora models/Qwen-Image-Edit-2509-Lightning-4steps-V1.0-bf16.safetensors \
  --output character_happy.png
```

| Option | Default | Description |
|--------|---------|-------------|
| `--model` | (required) | Path to Qwen Image Edit `.safetensors` |
| `--input` | (required) | Input image to edit |
| `--prompt` | (required) | Edit instruction |
| `--steps` | `20` | Inference steps (`4` with Lightning LoRA) |
| `--cfg` | `4.0` | CFG scale (`1.0` with Lightning LoRA) |
| `--seed` | `random` | RNG seed |
| `--lora` | (none) | Path to LoRA `.safetensors` |
| `--output` | `output.png` | Output path |

## Current State

| Command | Status |
|---------|--------|
| `vnasset generate` | ✅ Working |
| `vnasset edit` | ✅ Working |
| `vnasset pipeline` | 🚧 Planned |

## Performance

### Generate (SDXL)

Radeon 8060S (Strix Halo iGPU), bfloat16, 1024×1024:
- ~1s per step
- ~20s for 20-step generation

Model loading adds ~5s cold-start overhead.

### Edit (Qwen Image Edit)

Radeon 8060S, bfloat16:
- ~120s per step at 512×512
- ~23s model loading

Larger resolutions scale proportionally. The SDPA math fallback in the text
encoder's visual branch and the transformer's attention blocks is the main
bottleneck.

**Turbo mode:** Use the Lightning 4-step LoRA with `--steps 4 --cfg 1.0` to
cut per-step time proportionally (4× fewer steps).

## Technical Notes

### bfloat16 required on RDNA 3.5

`float16` causes GPU kernel crashes (segfault) on the Radeon 8060S. The tool
uses `bfloat16` internally. This is transparent to the user.

### Custom attention

The default PyTorch SDPA backends (flash attention, mem-efficient attention) are
unstable on this AMD GPU. VNAsset uses a simple matmul-based attention
implementation that avoids the SDPA dispatch entirely.

### ROCm torch version

Tested with `torch 2.11.0+rocm7.2`. Newer ROCm nightlies (2.13+, 2.14+) may
cause GPU crashes. If you encounter segfaults, try matching this version.

## Documentation

- **[SDXL Generation](docs/sdxl-generation.md)** — checkpoint loading, attention patches, prompt encoding, and generation pipeline details
- **[ComfyUI Prompt Style Support](docs/comfyui-prompt-style.md)** — prompt weighting and BREAK syntax specification

## Prompt Syntax

VNAsset supports **ComfyUI-style prompt weighting** via the `compel` library.

### Weighting

| Syntax | Effect |
|--------|--------|
| `(word)` | Boost ×1.1 |
| `(word:1.5)` | Boost ×1.5 |
| `(word:0.6)` | De-emphasize ×0.6 |
| `[word]` | De-emphasize ×0.9 (shorthand) |
| `\(word\)` | Literal parentheses (escaped) |

```bash
vnasset generate \
  --checkpoint models/novaAnimeXL_ilV190.safetensors \
  --prompt "(masterpiece:1.2), 1girl, (red hair:1.3), blue eyes, [glasses]" \
  --negative-prompt "(bad quality, worst quality:1.4)" \
  --steps 20 --seed 42
```

### BREAK (condition chunking)

Split the prompt into independent conditioning chunks with `BREAK`:

```bash
vnasset generate \
  --prompt "1girl, red hair, standing BREAK blue sky, cherry blossoms" \
  --steps 20 --seed 42
```

Use `--raw` to bypass weighting and fall back to plain diffusers encoding.

## Future Improvements

- **Persistent model session** — keep models loaded between commands to
  eliminate the ~5s cold-start overhead per generation. A `vnasset serve`
  daemon or `vnasset batch` command.
- **`torch.compile` on UNet** — the UNet forward is identical each step; ROCm's
  `torch.compile` support is maturing and could cut per-step time in half.
- **Self-contained torch wheel** — bundle the known-working torch wheel file in
  the project (`wheels/torch-2.11.0+rocm7.2-cp312-cp312-linux_x86_64.whl`) so
  the install is reproducible without depending on PyTorch's nightly index
  availability or a ComfyUI installation.
- **Qwen Image Edit support** — `vnasset edit` and `vnasset pipeline` for
  batch expression/outfit variant editing.