Available Models

Built-in Models

These models can be downloaded directly from inside the app. Each model has variants for different chips — see Getting Started for how to pick one.

Model	Type	CPU/GPU	NPU	Clip Skip	Source
SDXL Base 1.0	SDXL	❌	✅	-	HuggingFace
Illustrious v16	SDXL	❌	✅	-	CivitAI
AnythingV5	SD1.5	✅	✅	2	CivitAI
ChilloutMix	SD1.5	✅	✅	1	CivitAI
Absolute Reality	SD1.5	✅	✅	2	CivitAI
QteaMix	SD1.5	✅	✅	2	CivitAI
CuteYukiMix	SD1.5	✅	✅	2	CivitAI

TIP

Refer to each model's original page for recommended prompts, samplers, and CFG values.

Pre-converted Community Models

Community-maintained NPU model collections (for SD1.5 and SDXL):

• xororz/sd-qnn
• xororz/sdxl-qnn
• Mr-J-369
• YuuiKurata

The suffix on each file indicates the minimum chip tier required to run that model. A higher-tier chip can run any model at or below its tier; pick the highest suffix your device supports for the best performance.

SD1.5:

• _min — any Snapdragon NPU with Hexagon V68 or newer (non-flagship chips)
• _8gen1 — Snapdragon 8 Gen 1 / 8+ Gen 1
• _8gen2 — Snapdragon 8 Gen 2 or newer (2/3/4/5)

SDXL:

• _8gen3 — Snapdragon 8 Gen 3 or newer (SDXL only supports 8 Gen 3/4/5, so this is the only SDXL suffix)

Custom Models

You can also use your own checkpoints.

CPU/GPU mode (in-app)

Import any SD1.5 checkpoint directly in the app — conversion runs on-device. LoRA weights can be merged at import time.

NPU mode (host-side conversion)

NPU models must be converted on a Linux/WSL host before they can be loaded. See:

• SD1.5 Conversion Guide
• SDXL Conversion Guide (experimental)

Technical Details

NPU

• SDK: Qualcomm QNN SDK on the Hexagon NPU
• Quantization: W8A16 static quantization
• Resolution: SD1.5 fixed at 512×512 base; additional resolutions via patches. SDXL fixed at 1024×1024.
• Performance: Extremely fast inference

CPU/GPU

• Framework: MNN
• Quantization: W8 dynamic quantization
• Resolution: 128×128, 256×256, 384×384, 512×512
• Performance: Moderate speed, high compatibility