Qwen-Image-2.1-Turbo-Quantized vs animagine-xl-3.0
Specs, VRAM requirements and download trends — updated 4 November 2026.
cagliostrolab · B · Open weights
Params
2.6 B
Context
—
30d
59 K
openrail++ licence · conditional
Specification comparison
Differences are highlighted; identical values are muted.
| Specification | Qwen-Image-2.1-Turbo-Quantized | animagine-xl-3.0 |
|---|---|---|
| Parameters | — | 2.6 B |
| Architecture | — | — |
| Context length | — | — |
| Licence | other | openrail++ |
| Commercial use | Unknown | Conditional |
| Languages | — | 1 |
| Downloads 30d | 89,977 | 58,935 |
| Downloads all time | 2.1 M | 5.0 M |
| Quantizations on the Hub | GGUF, SAFETENSORS | SAFETENSORS |
| Gated | No | No |
| First seen on the Hub | 2026-09-20 | 2023-11-23 |
Download trend
Daily snapshots, last 7 days (4 Nov – 10 Nov)
2.5 M49 K
Qwen-Image-2.1-Turbo-Quantized
animagine-xl-3.0
VRAM side by side
On RTX 4090 · 24 GB · 8K context unless noted
Qwen-Image-2.1-Turbo-Quantized · Q4_0
5.0 GB / 24 GB
animagine-xl-3.0 · fp16
16.1 GB / 24 GB
Qwen-Image-2.1-Turbo-Quantized · fp16
70.3 GB / 24 GB
Adjacent comparisons
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Qwen-Image-2.1-Turbo-Quantized vs SDXL-Lightning
Qwen-Image-2.1-Turbo-Quantized vs FLUX.1-dev-gguf
Qwen-Image-2.1-Turbo-Quantized vs Qwen-Image-2.1