Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sandyup/comfyui-mcp --skill qwen-image-editgit clone --depth 1 https://github.com/sandyup/comfyui-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sandyup/comfyui-mcp/qwen-image-edit)<a href="https://agentmods.dev/skills/sandyup/comfyui-mcp/qwen-image-edit"><img src="https://agentmods.dev/badge/skills/sandyup/comfyui-mcp/qwen-image-edit.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.03610 |
| Opus 5 | $0.00014 | $0.01805 |
| Sonnet 5 | $0.00006 | $0.00722 |
| Haiku 4.5 | $0.00003 | $0.00361 |
Grade A, and why
qwen-image-edit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
91% identical to qwen-image-edit — 143 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Qwen Image Edit Workflows
Overview
Qwen Image Edit uses a vision-language model (Qwen2.5-VL) to edit images based on natural language instructions. The model "sees" the source image through CLIP conditioning and generates an edited version.
Models
Required Components
| Component | Node | Model Name | Notes |
|---|---|---|---|
| UNET | UNETLoader |
qwen_image_edit_2511_bf16.safetensors |
Official 2511 edit model (bf16) |
| CLIP | CLIPLoader (type=qwen_image) |
qwen_2.5_vl_7b_fp8_scaled.safetensors |
Shared across all Qwen models |
| VAE | VAELoader |
qwen_image_vae.safetensors |
Qwen-specific VAE |
Alternative UNET Models
| Model | Path | Focus |
|---|---|---|
qwenImageEditRemix_v10 |
qwenImageEditRemix_v10.safetensors |
Community remix, general editing |
qwenUltimateRealism_v11 |
Qwen/imageized/qwenUltimateRealism_v11.safetensors |
Product photography, hyper-realistic |
copaxTimeless |
Qwen/realistic/copaxTimeless_qwenUltraRealistic.safetensors |
Ultra-realistic portraits |
qwnImageEdit_v16Bf16 |
Qwen/abliterated/qwnImageEdit_v16Bf16.safetensors |
Abliterated (uncensored) |
Conditioning Nodes
TextEncodeQwenImageEditPlusAdvance_lrzjason (Recommended)
From the qweneditutils custom node pack. The Advanced variant is preferred because it:
- Outputs a LATENT directly (no need for separate EmptyLatentImage)
- Has separate VL-resize and non-resize image slots for fine control
- Supports target_size control for output resolution
- Includes a pad/center/disabled crop method with pad_info output
Required Inputs:
- clip: CLIP
- prompt: STRING — natural language edit instruction
Optional Inputs:
- vae: VAE — needed for image encoding and latent output
- vl_resize_image1-3: IMAGE — images that get VL-resized (downscaled for vision encoder)
- not_resize_image1-3: IMAGE — images kept at full resolution
- target_size: [1024, 1344, 1536, 2048, 768, 512] (default 1024)
- target_vl_size: [392, 384] (default 384)
- upscale_method: [lanczos, bicubic, area]
- crop_method: [pad, center, disabled]
- instruction: STRING — system instruction template (has sensible default)
Outputs (10):
[0] conditioning_with_full_ref: CONDITIONING — use as positive conditioning
[1] latent: LATENT — auto-scaled latent, feed directly to KSampler
[2] target_image1: IMAGE — processed target-size image
[3] target_image2: IMAGE
[4] target_image3: IMAGE
[5] vl_resized_image1: IMAGE — VL-resized version
[6] vl_resized_image2: IMAGE
[7] vl_resized_image3: IMAGE
[8] conditioning_with_first_ref: CONDITIONING — conditioning with only first ref
[9] pad_info: ANY — padding info for later unpadding
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 283 lines · 28 tokens per session scan A 4673de3af3cc
qwen-image-edit is a skill published in the GitHub repository sandyup/comfyui-mcp (1 stars, last pushed 2mo ago), licensed MIT. It adds 28 tokens to every session and 3,610 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to qwen-image-edit, differing in 143 lines, and is treated as a copy.
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