I have used Krea2 with Stable Diffusion cpp with no problem, but when I try to use it in ComfyUI I get the following error:
>Krea2 expects conditioning with 12x2560=30720 features (a 12-layer Qwen3-VL stack) but got 2560. Load the text encoder with CLIPLoader type 'krea2'.
I use exactly the same text encoder as with sd-cpp which is: Huihui-Qwen3-VL-4B-Instruct-abliterated.i1-Q5_K_M.gguf
What am I supposed to do?
It seems that if someone wants to have easy prompts for it, the Hermes Agent together with a small model like Gemma4 12B (E4B even at q8_0 seems too stupid to properly tool call) works fine. I just put a few example JSON prompts plus the explanation from a prompting guide and it can do OK. Can ask it to add some more extra details as well.
In the HERMES.md file you can add some details if your generated images share something in common (like let's say you're making the images of some medieval city or something).
The example HERMES.md file:
```
The task is to generate, based on a description, a complete JSON prompt for Ideogram4 image generation model.
# The structure
- high_level_description: The overall visual goal in one clear sentence.
- aspect_ratio: The image shape: 1:1, 3:4, 4:3, 16:9.
- style_description: Medium, lighting, aesthetics, color palette, and mood.
- compositional_deconstruction: Background plus each text/object element in the scene.
- elements[].bbox: A rough placement box for important text or objects.
- elements[].text: Exact words that should appear in the image.
# Generation details
- the aspect ratio will always be 16:9 here, 1600x900 resolution
- the style is to be photorealistic, of a high fantasy setting
- in the \examples` directory there are a few example files of how they are supposed to look like`
# Input
The input is in the \prompt` file`
# Result
The result is to be saved in the \result-{datetime-now}.json` file in the current folder.`
```
I'm just looking for some advice on optimally setting up Qwen3.6 27B for OpenCode. The VRAM is a little bit scarce, but I ended up with this so far:
llama-server --model models/Qwen3.6-27B-IQ4_XS.gguf \
--port 8080 \
--host 127.0.0.1 \
--top-p 0.95 \
--top-k 20 \
--min-p 0.0 \
--temperature 0.6 \
--flash-attn on \
--cache-type-k q8_0 \
--cache-type-v q8_0 \
--presence-penalty 0.0 \
--repeat-penalty 1.0 \
--ctx-size 65536 \
--chat-template-kwargs '{"preserve_thinking": true}' \
With this my VRAM usage is around 18.6/20 GB. So potentially I could stretch it by about 0.5GB.
Of course there is Qwen3.6 35B that thanks to MoE can fit without KV cache quantization and in Q4_K_M or even K_XL or maybe even Q5, but I don't think for this goal it would be of benefit over 27B.