comfyui-nvidia/deployments/ai-stack/openwebui-models/image_studio.md

Image Studio — dedicated image-generation chat model

A custom Open WebUI model preset that wraps a base LLM with a system prompt heavily biased toward calling the smart_image_gen tool. Users pick Image Studio from the chat-model dropdown when they want to generate or edit images, and the LLM treats every message as an image request — calling generate_image for new images and edit_image for modifications to attached ones.

This exists because general-purpose chat models often "describe" an image in text instead of calling the tool, especially when the request is conversational ("can you draw me…", "I'd like a picture of…"). A dedicated preset removes the ambiguity.

Two ways to install

Option A: Import the JSON (fast)

Workspace → Models → Import (top right) → upload image_studio.json.

This drops the preset in fully configured: base model, system prompt, tool attachment, function-calling mode, temperature, suggestion prompts. Verify after import:

• The smart_image_gen tool is actually attached (Tools list under the model's edit screen). If not, the tool ID Open WebUI assigned doesn't match the toolIds: ["smart_image_gen"] in the JSON — re-attach manually.
• Base Model is set to mistral-nemo:12b. Adjust if you want a different LLM (Qwen3.6 or Llama 3.1 also work well; smaller parameter counts may struggle with native tool calling).

Option B: Create manually (table below)

Workspace → Models → + (top right).

Field	Value
Name	Image Studio
Base Model	huihui_ai/qwen3.5-abliterated:9b (vision-capable, 256K context, abliterated). Pull via init-models.sh first.
Description	Image generation and routing across SDXL checkpoints.
System Prompt	Paste the block from System prompt below.
Tools	enable only smart_image_gen

In the Advanced Params section:

Field	Value
Function Calling	Native (mandatory)
Temperature	0.5 (lower = more reliable tool-calling)
Top P	0.9
Context Length	leave default

Save. The new model appears in the chat-model dropdown for any user with access.

System prompt

/no_think

You are an image-tool dispatcher. You do not respond in prose. Every
user message MUST result in exactly one tool call.

ROUTING:
- If the user attached an image → call edit_image(edit_instruction, style, denoise)
- Otherwise → call generate_image(prompt, style)

Fire the tool on the FIRST message, with no preamble. Do not write a
'plan', 'approach', 'steps', 'breakdown', or any explanation before
calling. Do not ask clarifying questions. Do not say what you are
about to do. If the request is vague, pick reasonable defaults and
call the tool — the user iterates after.

STYLES (pick one):
  photo         photorealistic photo / portrait / cinematic
  juggernaut    alternate photoreal — sharper, more saturated
  pony          anime, cartoon, manga, stylised illustration
  general       catch-all when nothing else fits
  furry-nai     anthropomorphic, NAI-trained mix
  furry-noob    anthropomorphic, NoobAI base
  furry-il      anthropomorphic, Illustrious base (default for any
                furry/anthro request)

For edit_image, pick style for the DESIRED OUTPUT, not the input
image. Default denoise 0.7. Use 0.3–0.5 for subtle changes (recolor,
style transfer) and 0.85–1.0 for radical reimaginings.

Write rich, descriptive prompts (subject, action, environment,
lighting, mood, framing). Do NOT add quality tags like 'masterpiece',
'best quality', 'score_9', 'absurdres' — the tool prepends the
correct tags per style. Do NOT set sampler, CFG, steps, scheduler —
the tool picks them.

AFTER the tool returns, write at most one short sentence noting your
style choice and offering one iteration idea. The image is already
shown to the user; do not describe it.

The first line /no_think disables Qwen 3.x's reasoning phase. If your base model isn't Qwen 3, leaving it in is a no-op (other models ignore it). Drop it only if it actually causes problems.

Vision capability

The shipped preset sets meta.capabilities.vision: true so Open WebUI allows users to attach images to chats with this model. Two paths:

Default — huihui_ai/qwen3.5-abliterated:9b

The shipped preset uses Qwen 3.5 abliterated 9B as the base — vision- capable, 256K context, no censorship hedging. Preseed via init-models.sh (an s3_pull line is already in place; see Mirroring models to S3 for the mirror workflow).

Important Qwen 3.x quirk: thinking mode is on by default and breaks native function calling — the model "thinks" about how to use the tool instead of just calling it. The shipped system prompt starts with /no_think to suppress this. If the model still plans instead of firing the tool, also set enable_thinking: false in Advanced Params → Custom Parameters (API-level enforcement).

Alternatives

If Qwen 3.5 isn't a fit (size, language preferences, abliteration caveats), other vision-capable Ollama tags worth trying:

• qwen2.5vl:7b — smaller, no thinking mode, very reliable tool-caller
• llama3.2-vision:11b — Meta's vision variant, ~7 GB
• minicpm-v:8b — fast, capable

To swap, change base_model_id in image_studio.json (or the Base Model field if you imported manually) and pull the model via init-models.sh or the Open WebUI model UI.

Non-vision base model

If you'd rather use a text-only LLM (e.g. mistral-nemo:12b), keep vision: true in the preset so Open WebUI still permits image attachments; the image flows through to edit_image via __messages__ / __files__ and ComfyUI does the visual work. The LLM can't see the image, but for explicit edit instructions ("change the background to a sunset") that doesn't matter.

Why this works when a generic chat model didn't

• The system prompt is unambiguous. No room for the model to decide "I'll just describe it in text instead."
• Only one tool is attached. No competing tools to choose between.
• Native function calling is mandatory. The "Default" mode in Open WebUI uses prompt-injection tool emulation that fails silently on a lot of local models.
• Lower temperature. Tool calling is more reliable with less sampling randomness.

Iterating on the system prompt

If users ask for things you didn't anticipate (specific aspect ratios, multi-image batches, particular checkpoints not in the routing rules), edit the system prompt above and re-paste into the Workspace → Models entry. It's the highest-leverage place to tune behaviour without touching the Tool's Python.