New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Qwen Txt2img

by artokun in artokun/comfyui-mcp

Build Qwen Image 2512 text-to-image workflows with QwenImageIntegratedKSampler, separate component loading, lightning LoRAs, and fine-tuned model variants

MITAuto-check passedAI & LLM Engineering

Install Qwen Txt2img

skills CLI
$ npx skills add artokun/comfyui-mcp --skill qwen-txt2img -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install artokun/comfyui-mcp qwen-txt2img --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/artokun/comfyui-mcp.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugin/skills/qwen-txt2img .claude/skills/qwen-txt2img && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
qwen-txt2img
GitHub stars
803
Token cost
~3.3k tokens
SKILL.md length
739 words
Files
1
Skills in repo
42
Repo updated
First seen
Licence
MIT

At a glance

Build Qwen Image 2512 text-to-image workflows with QwenImageIntegratedKSampler, separate component loading, lightning LoRAs, and fine-tuned model variants

  • Works in 2 steps: QwenImageIntegratedKSampler: All-in-one… → Separate component loading: UNETLoader +…
  • Tasks that involve Fine-tuning
  • SKILL.md covers Overview, Models, Lightning LoRAs and Sampler Settings, plus 10 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Qwen Txt2img is an agent skill from artokun/comfyui-mcp. Build Qwen Image 2512 text-to-image workflows with QwenImageIntegratedKSampler, separate component loading, lightning LoRAs, and fine-tuned model variants

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Fine-tuning, Image generation and Diffusion and image models. It works with Qwen. The repository describes itself as: Local-first, agent-native control plane for ComfyUI — MCP server + sidebar agent that generates images, video & audio, authors and runs workflows, and edits your live graph in… The licence is MIT.

When your agent uses it

  • Tasks that involve Fine-tuning
  • Tasks that involve Image generation
  • Tasks that involve Diffusion and image models

Example prompts

  • “/qwen-txt2img”

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. QwenImageIntegratedKSampler: All-in-one node (recommended for simplicity)
  2. Separate component loading: UNETLoader + CLIPLoader + VAELoader + standard KSampler (more flexible)

What it can do on your machine

Read from SKILL.md and the folder at commit 6ad6fc0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Qwen Txt2img loads about 3.3k tokens when it runs. Until then it costs about 42 tokens; SKILL.md has 739 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~42
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from artokun/comfyui-mcp at commit 6ad6fc0, republished under its MIT licence (© artokun). 739 words, ~3,295 tokens.

Download SKILL.mdSave it as .claude/skills/qwen-txt2img/SKILL.md (or your agent's skills folder).
name
qwen-txt2img
description
Build Qwen Image 2512 text-to-image workflows with QwenImageIntegratedKSampler, separate component loading, lightning LoRAs, and fine-tuned model variants
globs
**/*.json

Qwen Image 2512 Text-to-Image Workflows

Overview

Qwen Image 2512 is the latest (December 2025) text-to-image model from the Qwen family. It uses a vision-language model (Qwen2.5-VL) as the text encoder and generates high-quality images from natural language prompts. Two workflow approaches:

  1. QwenImageIntegratedKSampler: All-in-one node (recommended for simplicity)
  2. Separate component loading: UNETLoader + CLIPLoader + VAELoader + standard KSampler (more flexible)

Models

Standard Components
ComponentNodeModelNotes
UNETUNETLoaderqwen_image_2512_fp8_e4m3fn.safetensorsFP8, not currently installed — download if needed
CLIPCLIPLoader (type=qwen_image)qwen_2.5_vl_7b_fp8_scaled.safetensorsShared across all Qwen models, in clip/
VAEVAELoaderqwen_image_vae.safetensorsQwen-specific VAE (242MB)
Fine-tuned Variants (Installed)
ModelPathFocus
qwenImageEditRemix_v10diffusion_models/qwenImageEditRemix_v10.safetensorsGeneral-purpose remix
qwenUltimateRealism_v11UNETLoader pathProduct photography, hyper-realistic
copaxTimelessUNETLoader pathUltra-realistic portraits
qwnImageEdit_v16Bf16UNETLoader pathAbliterated (uncensored)

Lightning LoRAs

4-Step Lightning (General Qwen / txt2img)
json
{
  "class_type": "LoraLoaderModelOnly",
  "inputs": {
    "model": ["<unet_node>", 0],
    "lora_name": "Qwen-Image-Lightning-4steps-V1.0.safetensors",
    "strength_model": 1.0
  }
}

Settings: steps=4, cfg=1.0, sampler=euler, scheduler=simple, denoise=1.0

8-Step Lightning (Higher Quality)
json
{
  "class_type": "LoraLoaderModelOnly",
  "inputs": {
    "model": ["<unet_node>", 0],
    "lora_name": "Qwen-Image-Lightning-8steps-V1.0.safetensors",
    "strength_model": 1.0
  }
}

Settings: steps=8, cfg=1.0 (or 2.5 for character detail), sampler=euler, scheduler=simple

Sampler Settings

PresetStepsCFGSamplerSchedulerDenoiseLoRANotes
Lightning 4-step41.0eulersimple1.0Lightning-4stepsFastest, good quality
Lightning 8-step81.0eulersimple1.0Lightning-8stepsBetter detail
Lightning character82.5eulersimple1.0Lightning-8stepsBest for portraits
Standard504.0eulersimple1.0noneOfficial ComfyUI
Golden quality504.5eulersimple1.0noneCommunity best
Character composition304.0euler_ancestralbeta1.0noneMulti-character scenes
CopaxTimeless304.0res_multistepsgm_uniform1.0noneUltra-realistic
UltimateRealism307.5eulersimple1.0noneProduct photography
ModelSamplingAuraFlow

For standard (non-lightning) presets, apply flow matching shift:

json
{
  "class_type": "ModelSamplingAuraFlow",
  "inputs": { "model": ["<unet_or_lora>", 0], "shift": 3.1 }
}

Shift=3.1 is the standard value for Qwen Image. Not needed with lightning LoRA (baked into the distillation).

Resolutions

Qwen operates at ~1.6 megapixels natively:

AspectResolutionUse Case
Square1328x1328General
Portrait 3:41104x1472Portraits
Portrait 2:31056x1584
Portrait 9:16928x1664Phone format
Landscape 4:31472x1104Landscape scenes
Landscape 3:21584x1056
Landscape 16:91664x928Widescreen
Ultra portrait1536x2048Tall format
Video-ready832x480For WAN 2.2 FLF pipeline

Approach 1: QwenImageIntegratedKSampler (All-in-One)

The QwenImageIntegratedKSampler custom node handles model patching, conditioning, sampling, and output in a single node. Simplest workflow: 4 nodes for model loading + 1 integrated sampler + 1 save.

Node Inputs
Required:
  - model: MODEL (from UNETLoader)
  - clip: CLIP (from CLIPLoader, type=qwen_image)
  - vae: VAE
  - positive_prompt: STRING
  - negative_prompt: STRING
  - generation_mode: "文生图 text-to-image" or "图生图 image-to-image"
  - batch_size: INT (default 1)
  - width: INT (default 0, step 8)
  - height: INT (default 0, step 8)
  - seed: INT
  - steps: INT (default 4)
  - cfg: FLOAT (default 1)
  - sampler_name: euler, dpmpp_2m, etc.
  - scheduler: simple, sgm_uniform, beta, etc.
  - denoise: FLOAT (default 1)

Optional:
  - image1-5: IMAGE (reference images for i2i or multi-ref)
  - latent: LATENT
  - controlnet_data: CONTROL_NET_DATA
  - auraflow_shift: FLOAT (default 3)
  - cfg_norm_strength: FLOAT (default 1)

Outputs:
  [0] IMAGE — generated image
  [1] LATENT — output latent (optional)
  [2] IMAGE — scaled input image (for i2i)
Complete Workflow: Integrated Sampler (Lightning 4-Step)
json
{
  "1": { "class_type": "UNETLoader", "inputs": { "unet_name": "qwenImageEditRemix_v10.safetensors", "weight_dtype": "default" }},
  "2": { "class_type": "LoraLoaderModelOnly", "inputs": { "model": ["1", 0], "lora_name": "Qwen-Image-Lightning-4steps-V1.0.safetensors", "strength_model": 1.0 }},
  "3": { "class_type": "CLIPLoader", "inputs": { "clip_name": "qwen_2.5_vl_7b_fp8_scaled.safetensors", "type": "qwen_image" }},
  "4": { "class_type": "VAELoader", "inputs": { "vae_name": "qwen_image_vae.safetensors" }},
  "5": { "class_type": "QwenImageIntegratedKSampler", "inputs": {
    "model": ["2", 0],
    "clip": ["3", 0],
    "vae": ["4", 0],
    "positive_prompt": "<detailed natural language prompt>",
    "negative_prompt": "",
    "generation_mode": "文生图 text-to-image",
    "batch_size": 1,
    "width": 1024,
    "height": 1344,
    "seed": 42,
    "steps": 4,
    "cfg": 1,
    "sampler_name": "euler",
    "scheduler": "simple",
    "denoise": 1,
    "auraflow_shift": 3,
    "cfg_norm_strength": 1
  }},
  "6": { "class_type": "SaveImage", "inputs": { "images": ["5", 0], "filename_prefix": "qwen_t2i" }}
}

Approach 2: Separate Component Loading (Standard Pipeline)

More flexible, since it allows inserting additional processing nodes between stages.

Pipeline Flow
UNETLoader → [LoraLoaderModelOnly] → [ModelSamplingAuraFlow (shift=3.1)] → MODEL
CLIPLoader (qwen_image) → CLIP
VAELoader → VAE

CLIPTextEncode (positive) → CONDITIONING
ConditioningZeroOut → negative CONDITIONING

EmptySD3LatentImage (1024x1344) → LATENT

KSampler → VAEDecode → SaveImage

Latent node: use EmptySD3LatentImage, matching the official Comfy-Org image_qwen_image template. Qwen Image’s latent format is Wan21, so its latent is 16-channel; EmptyLatentImage emits 4. A bare EmptyLatentImage → KSampler still renders, because ComfyUI’s fix_empty_latent_channels (comfy/sample.py, called by every sampler node) repeats an all-zero latent up to the model’s channel count. But that rescue is gated on torch.count_nonzero(latent) == 0, so it stops applying the moment a node inserted here writes into the latent — which is exactly what this approach is for. Start 16-channel and the question never arises.

Show full SKILL.md (284 more words)Show less
Complete Workflow: Separate Loading (Lightning 4-Step)
json
{
  "1": { "class_type": "UNETLoader", "inputs": { "unet_name": "qwenImageEditRemix_v10.safetensors", "weight_dtype": "default" }},
  "2": { "class_type": "LoraLoaderModelOnly", "inputs": { "model": ["1", 0], "lora_name": "Qwen-Image-Lightning-4steps-V1.0.safetensors", "strength_model": 1.0 }},
  "3": { "class_type": "CLIPLoader", "inputs": { "clip_name": "qwen_2.5_vl_7b_fp8_scaled.safetensors", "type": "qwen_image" }},
  "4": { "class_type": "VAELoader", "inputs": { "vae_name": "qwen_image_vae.safetensors" }},
  "5": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["3", 0], "text": "<detailed natural language prompt>" }},
  "6": { "class_type": "ConditioningZeroOut", "inputs": { "conditioning": ["5", 0] }},
  "7": { "class_type": "EmptySD3LatentImage", "inputs": { "width": 1024, "height": 1344, "batch_size": 1 }},
  "8": { "class_type": "KSampler", "inputs": {
    "model": ["2", 0],
    "positive": ["5", 0],
    "negative": ["6", 0],
    "latent_image": ["7", 0],
    "seed": 42, "steps": 4, "cfg": 1, "sampler_name": "euler", "scheduler": "simple", "denoise": 1
  }},
  "9": { "class_type": "VAEDecode", "inputs": { "samples": ["8", 0], "vae": ["4", 0] }},
  "10": { "class_type": "SaveImage", "inputs": { "images": ["9", 0], "filename_prefix": "qwen_t2i" }}
}
Complete Workflow: Standard Quality (50-Step)
json
{
  "1": { "class_type": "UNETLoader", "inputs": { "unet_name": "qwenImageEditRemix_v10.safetensors", "weight_dtype": "default" }},
  "2": { "class_type": "ModelSamplingAuraFlow", "inputs": { "model": ["1", 0], "shift": 3.1 }},
  "3": { "class_type": "CLIPLoader", "inputs": { "clip_name": "qwen_2.5_vl_7b_fp8_scaled.safetensors", "type": "qwen_image" }},
  "4": { "class_type": "VAELoader", "inputs": { "vae_name": "qwen_image_vae.safetensors" }},
  "5": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["3", 0], "text": "<detailed natural language prompt>" }},
  "6": { "class_type": "ConditioningZeroOut", "inputs": { "conditioning": ["5", 0] }},
  "7": { "class_type": "EmptySD3LatentImage", "inputs": { "width": 1328, "height": 1328, "batch_size": 1 }},
  "8": { "class_type": "KSampler", "inputs": {
    "model": ["2", 0],
    "positive": ["5", 0],
    "negative": ["6", 0],
    "latent_image": ["7", 0],
    "seed": 42, "steps": 50, "cfg": 4, "sampler_name": "euler", "scheduler": "simple", "denoise": 1
  }},
  "9": { "class_type": "VAEDecode", "inputs": { "samples": ["8", 0], "vae": ["4", 0] }},
  "10": { "class_type": "SaveImage", "inputs": { "images": ["9", 0], "filename_prefix": "qwen_t2i_hq" }}
}

Negative Conditioning

Always use ConditioningZeroOut for Qwen txt2img:

json
{
  "class_type": "ConditioningZeroOut",
  "inputs": { "conditioning": ["<positive_cond>", 0] }
}

Or use an empty string in CLIPTextEncode, but ZeroOut is more explicit and reliable.

QwenImageDiffsynthControlnet

For ControlNet support with Qwen models. Patches the model with a DiffSynth control signal:

Required Inputs:
  - model: MODEL
  - model_patch: MODEL_PATCH (from DiffSynth ControlNet loader)
  - vae: VAE
  - image: IMAGE (control image)
  - strength: FLOAT (default 1.0)

Optional:
  - mask: MASK

Outputs:
  [0] MODEL (patched)

DiffSynth ControlNets support: canny, depth, inpaint only (NOT pose).

Concept/Style LoRAs (Installed)

Located in loras/Qwen/:

  • style/: Figure makers, reality transform, panel painter
  • concept/: Various concept LoRAs
  • poses/: Pose-specific LoRAs
  • character/: Character enhancement
  • anime/: Anime style LoRAs
  • tool/: Utility LoRAs (anything2real, gaussian splash)
  • equirectangular projection/: 360 panorama LoRA

Apply with LoraLoaderModelOnly:

json
{
  "class_type": "LoraLoaderModelOnly",
  "inputs": {
    "model": ["<unet_or_lightning_lora>", 0],
    "lora_name": "Qwen\\concept\\hinaQwenImageAsianMixLora_v2.safetensors",
    "strength_model": 0.8
  }
}

Prompt Style

Natural language, 1 to 3 sentences. Be descriptive:

Good: "Professional portrait of an Asian woman in her late 20s, wearing a cream linen blazer at a Tokyo rooftop café during golden hour, holding a matcha latte, editorial fashion photography, shot on Sony A7III 85mm f/1.4"
Bad: "1girl, cafe, blazer, matcha"

Tips:

  • Put text to render in quotes within the prompt
  • "photograph" works better than "photorealistic"
  • Negative prompts: use NLP-style descriptions, not keyword spam (or use ZeroOut)

VRAM Considerations

ConfigVRAMNotes
FP8 UNET + fp8 CLIP + VAE~17-18GBFits comfortably on RTX 4090
bf16 UNET (edit model)~10GB UNET + 7GB CLIPAlso fits well
  • Always clear_vram before switching to Qwen from another model family
  • Lightning 4-step takes ~3-5s per image

Tips

  1. QwenImageIntegratedKSampler is the simplest approach for basic txt2img. One node handles everything
  2. For LoRA stacking or ControlNet, use the separate component pipeline instead
  3. The integrated sampler's auraflow_shift defaults to 3 (close to the recommended 3.1). Adjust only if needed
  4. For video pipeline output (feeding into WAN FLF), set resolution to 832x480
  5. CopaxTimeless pick: res_multistep + sgm_uniform at CFG 4.0 for ultra-realistic results
  6. Multiple concept LoRAs can stack. Reduce individual strength to 0.5-0.7 when combining

Sources

© artokun, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugin/skills/qwen-txt2img of artokun/comfyui-mcp.

Open the folder on GitHubat commit 6ad6fc0

Compare with similar skills

Qwen Txt2img next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Qwen Txt2img compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Qwen Txt2img this skillartokun/comfyui-mcp803—~3.3kAutomated safety check: PassMIT
Flux2 Klein PromptingAnastasiyaW/codex-claude-code-config154—~2.8kAutomated safety check: PassMIT
LoRA Space Builderhuggingface/skills11k2 repos~8.4kAutomated safety check: PassApache-2.0
Setupguaardvark/guaardvark258—~1.2kAutomated safety check: PassMIT
Workflow Template BuilderMooshieblob1/MooshieUI207—~640Automated safety check: PassAGPL-3.0
Character Refseternityspring/shuohao-skills4.3k—~1.7kAutomated safety check: WarnApache-2.0

Similar skills

  • Flux2 Klein Prompting

    AnastasiyaW/codex-claude-code-config

    Expert prompt engineering for FLUX.2 [klein] image generation and editing model.

    154 GitHub stars~2.8k tokensUpdated yesterday
    Media & CreativeAuto-check passed

  • LoRA Space Builder

    huggingface/skills

    Official

    Builds and publishes a Gradio demo on Hugging Face Spaces for a LoRA, with the pipeline, UI and settings chosen to match that LoRA's task and model card.

    11k GitHub starsUsed in 2 repos~8.4k tokens
    AI & LLM EngineeringAuto-check passed

  • Setup

    guaardvark/guaardvark

    Connect this agent to a running Guaardvark (self-hosted AI studio) and check what it can do right now.

    258 GitHub stars~1.2k tokensUpdated today
    AI & LLM EngineeringAuto-check passed

  • Workflow Template Builder

    Mooshieblob1/MooshieUI

    Builds or modifies ComfyUI workflow JSON templates in MooshieUI's Rust backend (src-tauri/src/templates).

    207 GitHub stars~640 tokensUpdated today
    AI & LLM EngineeringAuto-check passed

  • Character Refs

    eternityspring/shuohao-skills

    给任何故事里的角色真出参考图（小说改编、自己原创的故事、单独设计一个角色都行，不需要小说原文）： 一段话描述角色，拆成分层字段、补全后确认， 先出一张正面全身锚点，其余视图（大头照、90° 侧面、背面、细节、45° 大头照）都只参考这张锚点， 按需分档出图。每张图带标识、可单独重出，重出后自动标出哪些图过期。

    4.3k GitHub stars~1.7k tokensUpdated 3 days ago
    Media & CreativeAuto-check: warnings

  • Image Gen

    open-octo/octo-agent

    Acquire images as files — generate them with an AI image model (14 providers: OpenAI/gpt-image, Gemini, Qwen, Zhipu, Volcengine, Stability, FLUX, Ideogram, MiniMax, and more), search openly-licensed…

    125 GitHub stars~3.1k tokensUpdated yesterday
    Media & CreativeAuto-check: notes

More from artokun/comfyui-mcp

All 42 skills in this repo

  • AI Toolkit Trainer

    artokun/comfyui-mcp

    Train custom LoRAs with ostris AI-Toolkit. An agent skill from artokun/comfyui-mcp.

    803 GitHub stars~2.7k tokensUpdated 6 days ago
    Auto-check passed

  • Anima Base

    artokun/comfyui-mcp

    Anime/illustration text-to-image (ANIMA 1.0, ~2B Cosmos DiT).

    803 GitHub stars~4k tokensUpdated 6 days ago
    Auto-check passed

  • Civitai

    artokun/comfyui-mcp

    Discover Civitai models with the BUILT-IN downloadmodel action:"searchcivitai" and install/generate them locally.

    803 GitHub stars~1.1k tokensUpdated 6 days ago
    Auto-check passed

  • Color Correction

    artokun/comfyui-mcp

    Diagnose and fix video/image color OBJECTIVELY with the getimage (action:"analyzecolor") tool (scopes/stats such as black/white points, contrast, saturation, clipping, cast) instead of eyeballing a…

    803 GitHub stars~2.4k tokensUpdated 6 days ago
    Auto-check passed

  • Comfyui Frontend Extensions

    artokun/comfyui-mcp

    Authoring ComfyUI v2 frontend extensions with @comfyorg/extension-api, covering defineNode/defineExtension/defineWidget, shell UI (sidebar tabs, commands, hotkeys), typed events, and handles.

    803 GitHub stars~5.4k tokensUpdated 6 days ago
    Auto-check passed

  • Comfyui Launch Flags

    artokun/comfyui-mcp

    Pick the right ComfyUI startup flags for VRAM, attention, caching, and speed.

    803 GitHub stars~3.1k tokensUpdated 6 days ago
    Auto-check passed

Works with

Categories

Questions about Qwen Txt2img

What does Qwen Txt2img do?

Build Qwen Image 2512 text-to-image workflows with QwenImageIntegratedKSampler, separate component loading, lightning LoRAs, and fine-tuned model variants. Qwen Txt2img is an agent skill from artokun/comfyui-mcp.

When should I use Qwen Txt2img?

Qwen Txt2img fits situations like: tasks that involve Fine-tuning; tasks that involve Image generation; tasks that involve Diffusion and image models.

How do I install Qwen Txt2img in Claude Code?

Run `npx skills add artokun/comfyui-mcp --skill qwen-txt2img -a claude-code`. Or copy the skill folder (plugin/skills/qwen-txt2img in artokun/comfyui-mcp) into .claude/skills/qwen-txt2img in your project. Claude Code loads it when a task matches its description.

How do I install Qwen Txt2img in Codex?

Run `npx skills add artokun/comfyui-mcp --skill qwen-txt2img -a codex`. Or copy the skill folder (plugin/skills/qwen-txt2img in artokun/comfyui-mcp) into .agents/skills/qwen-txt2img in your project. Codex loads it when a task matches its description.

Can I use Qwen Txt2img in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add artokun/comfyui-mcp --skill qwen-txt2img -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/qwen-txt2img, .gemini/skills/qwen-txt2img, .github/skills/qwen-txt2img and .opencode/skills/qwen-txt2img in your project.

What does Qwen Txt2img need to run?

SKILL.md names no scripts, command-line tools or credentials: Qwen Txt2img is instructions for the agent only.

Does Qwen Txt2img access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Qwen Txt2img safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Qwen Txt2img use?

Qwen Txt2img is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Qwen Txt2img use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Qwen Txt2img?

Skills that share tags, products or a category with Qwen Txt2img: Flux2 Klein Prompting (AnastasiyaW/codex-claude-code-config, 154 stars), LoRA Space Builder (huggingface/skills, 11k stars), Setup (guaardvark/guaardvark, 258 stars) and Workflow Template Builder (Mooshieblob1/MooshieUI, 207 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Qwen Txt2img?

artokun (a GitHub user) maintains it in artokun/comfyui-mcp, which has 803 GitHub stars. The repository holds 42 skills in this directory. The repository was last updated on October 5, 2026.

Source: artokun/comfyui-mcp on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.