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

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/:

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)

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)

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.

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)

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:

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.