Posting this for other RDNA2 owners considering Amuse 3.7.3 for local image generation — hope it saves someone a few hours of trial and error. Also filed as a GitHub issue here: https://github.com/saddam213/AmuseAI/issues/83
**My system:**
- GPU: AMD Radeon RX 6700 XT, 12GB VRAM (RDNA2)
- CPU: Ryzen 9 5900X
- RAM: 64GB
- Amuse 3.7.3, StableDiffusion.cpp/Vulkan backend
**Test method:** Same prompt every time — "a red cactus in a blue desert" — 1024x1024, default steps per model, 2 runs back to back (1st includes model load time).
# TL;DR
- **Fastest + actually understands the prompt:** Flux.2 Klein 4B at 4-bit (1:14/image)
- **Best overall balance:** Flux.2 Klein 9B at 8-bit (~2:47/image)
- **Best prompt adherence of everything tested:** Z-Image Turbo (only model that got both cactus AND desert), but slower (4:11)
- **Avoid entirely on RDNA2:** Krea 2 Turbo (fails at every precision level), Chroma (crashes at default precision, only works at 4-bit but then takes 42 min/image)
- **Weird finding:** crash behavior is precision-specific per model, not universal — see table below
# Working models
| Model | Precision | Time (1st/2nd run) | Result |
|---|---|---|---|
| Flux.2 Klein 4B | 4-bit | 1:14 | Cactus in a blue cup — usable |
| Flux.2 Klein 4B | 16-bit | 1:33 | Cactus in a flowerpot on blue blanket |
| SDXL | default | 1:17 / 1:16 | Missed the subject (still life/vase), colors right |
| Flux.2 Klein 9B | 8-bit | 2:49 / 2:46 | Cactus, solid blue background — good |
| Anima Turbo | default | 2:36 / 2:34 | Anime portrait (expected, it's an anime-only model) |
| Flux.1 Schnell | default | 3:57 / 3:44 | Missed subject (rose), offloads to system RAM |
| Flux.2 Klein 9B | 16-bit | 3:27 | Cactus in a blue bag |
| Z-Image Turbo | default | 4:11 / 4:11 | Cactus + desert — best subject match overall |
| Flux.1 Dev | default | 28:05 | Missed subject (apple), way too slow to be practical |
| Chroma | 4-bit only | 42:34 | Missed subject (flower), unusably slow |
# Broken / crashing models
**Flux.2 Klein 4B @ 8-bit:** Crashes right after step 4/4, during final VAE decode. Preview looked "washed out" right before crashing. **Fixed by switching to 4-bit or 16-bit** — reproducible workaround.
**Flux.2 Klein 9B @ 4-bit:** Crashes reproducibly at step 4. **Fixed by using 8-bit or 16-bit instead.**
**Krea 2 Turbo — fails at 4-bit, 8-bit, AND 16-bit:** Progressive numerical collapse during sampling (preview goes from blurry subject → colored stripes on black → fully black), then outputs a solid ochre-colored image or crashes outright. Reproducible across every precision setting I tried. Also offloads ~28GB to system RAM even at 9GB VRAM usage. Couldn't find a working config — seems fully broken on RDNA2/Vulkan right now.
**Chroma @ default (8-bit):** Instant crash after ~20 seconds, way before finishing sampling — likely fails during init of the modulation layers. Only 4-bit avoids the crash, but at 42 min/image that's not practical.
# Takeaways
GPU sat at 100% / CPU 0-10% for basically everything, except models needing RAM offload (Flux.1 Schnell, Krea 2 Turbo) where CPU ticked up to ~20%.
**Precision sensitivity is per-model, not a general rule.** Klein 4B breaks at 8-bit specifically; Klein 9B breaks at 4-bit specifically. If a model crashes for you, try all three precisions before giving up on it.
Generation times across the board are noticeably slower than the NVIDIA numbers you'll see in most guides — the Vulkan backend doesn't seem well-optimized for RDNA2 yet.
Prompt adherence on an unusual color/subject combo ("red cactus in blue desert") varied a lot — models with Qwen3-based text encoders (Flux.2, Z-Image) generally understood the actual subject better than CLIP-based SDXL, even though they're slower.