AsymFlux.2 Klein 9B pixel-space model debuts with low-VRAM ComfyUI workflow
AsymFlux.2 Klein 9B, a pixel-space flow model optimized for realistic image generation, is now available with bf16 and GGUF formats for low-VRAM users. A new ComfyUI workflow includes a head-to-head comparison against FLUX 9B-KV.
AsymFlux.2 Klein 9B is a pixel-space flow model from the AsymFlow project designed to generate realistic images with fine detail and rich visual styles. The 9B parameter model runs natively in ComfyUI and ships with bf16 and GGUF quantized weight formats, enabling inference on consumer GPUs without 24GB+ VRAM.
A community workflow now available on Civitai pairs the model with custom node installation instructions and includes a direct comparison against FLUX 9B-KV, the latest iteration from the FLUX team. Testing shows mixed results: both models excel in different prompt categories, with neither dominating across the board. The main tradeoff is inference speed—38 steps on the FLUX diffusion architecture push generation time higher than single-pass alternatives, though the workflow author notes the quality justifies the wait in many cases.
What stands out
- 01Pixel-space architecture. AsymFlux.2 Klein 9B operates in pixel space rather than latent space, a design choice that affects both memory usage and output fidelity compared to competing models.
- 02Low-VRAM quantization. bf16 and GGUF formats let users run the full 9B parameter model on consumer GPUs without high-end hardware.
- 0338-step inference cost. The FLUX diffusion backbone requires more sampling steps than some alternatives, pushing generation time higher but delivering richer detail in outputs.
- 04Head-to-head benchmark. The workflow includes direct comparisons against FLUX 9B-KV across multiple prompt categories, showing strengths and weaknesses for each model.
- 05 The Civitai release includes a ready-to-run workflow with all necessary custom nodes, lowering setup friction for practitioners already in that ecosystem.