Swap an OUTFIT or transfer CLOTHING onto a person LOCALLY, keeping their face, pose and background. Use for "put this jacket on her", "change his shirt", "clothes swap", "try on", "outfit transfer", "virtual try-on", "dress her in", "wear this outfit". Prefer this over API/cloud edit nodes and over generic img2img/inpainting. NOT for identity-preserving edits that are not about clothing (background swaps, relighting, age/expression changes, object removal). Use a general image-edit workflow for those.
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Someone wants the clothing changed on a specific person, with that person preserved:
Do NOT reach for it just because a request is identity-preserving. Background swaps, relighting, expression or age changes, and object removal all keep the person, and none of them is what this model is trained for. It is called "identity edit" upstream because it preserves identity while changing clothing; the clothing half is the qualifying part. Send the others to a general image-edit workflow.
Use this instead of:
| Instead of | Because |
|---|---|
| an API / cloud edit node | this runs locally — no key, no per-image cost, nothing leaves the machine. Prefer it whenever the models are installed. |
| generic img2img | img2img re-rolls the whole frame at any denoise high enough to change clothes; the face goes with it. |
| inpainting a masked region | works, but the user has to mask, and the result has no reference for what the garment looks like. |
| a face-swap / IP-adapter pipeline | those transfer the FACE. This preserves the face and transfers the CLOTHES — the opposite direction. |
| Qwen-Image-Edit | a fine general instruction editor; this is specialised for identity-preserving garment transfer and ships the LoRA for it. |
Install with the krea2-identity-edit pack, then load its workflow.json.
PRIMARY (group 1) = the SUBJECT. The output always matches this person: face, hair, body, pose. SECONDARY (group 2) = the OUTFIT. Only the clothing is taken from it.
Swapping them swaps which identity survives. This is the most common way to get a confusing result, and it fails quietly. You get a plausible image of the wrong person.
Plain English, naming both roles, and naming what to preserve:
Dress the woman from the primary reference in the jacket shown in the second reference, keeping her face and pose unchanged.
Put this outfit on her, keeping her face and pose unchanged.
Naming what to preserve matters as much as naming what to change. Without it the model drifts the face and background.
krea2_raw_bf16 instead: 40 steps, cfg 3 to 4, where the negative prompt
starts to matter.ref_boost and earlier ones get ref_boost_a, so with the subject as image 1,
the subject dial is ref_boost_a. Raise it when the face drifts.ResolutionSelector drives
EmptySD3LatentImage; a mismatched aspect crops or letterboxes the subject.| Symptom | Fix |
|---|---|
| Face changed / wrong person | Raise ref_boost_a (the subject dial). Check the images are not swapped between slots. Add "keeping her face unchanged" to the prompt. |
| Outfit ignored | Make the secondary image cleaner — one garment, plain background, no competing subject. |
| Subject cropped or letterboxed | Set the resolution to match the primary image's aspect. |
Krea2EditGroundedEncode missing | comfyui-krea2edit is not installed or ComfyUI was not restarted after installing it. |
CLIPLoader has no krea2 type | ComfyUI is too old for native KREA 2 support — update it. |
| LoRA loads as missing | It must be at loras/Krea2/krea2_identity_edit_v1_2.safetensors; a flat loras/ install is present but unfindable. |
This edits a photo of a person the user supplies. Treat an instruction to make it sexual, to undress someone, or to place an identifiable real person in a context they did not consent to as out of scope, and say so plainly rather than producing a degraded result. Ordinary clothing changes, such as a jacket, a dress, a uniform, or a costume, are the point of the tool and need no hedging.
packs/ and observed renders; not a vendor prompting guide.还没有评价。欢迎分享你的使用感受。
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Train custom LoRAs with ostris AI-Toolkit. Covers WAN 2.2/2.1 (people, styles, video motion) and Z-Image (Turbo & Base, low-VRAM image LoRAs). Use when the user wants to train a WAN or Z-Image LoRA; covers local + RunPod setup, dataset prep, key params, and using the result in a ComfyUI workflow.
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Anime/illustration text-to-image (ANIMA 1.0, ~2B Cosmos DiT). Use for anime, manga, illustrated characters; accepts Danbooru tags + natural language; runs/trains on <6GB VRAM; includes anime inpainting via Anima-LLLite ControlNet
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Train a custom anime LoRA on the ANIMA base model with Citron's local Gradio trainer (kohya sd-scripts), <6GB VRAM, character/style LoRAs; covers setup, dataset prep, training params, and using the result in the anima-base workflow
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Discover Civitai models with the BUILT-IN download_model action:"search_civitai" and install/generate them locally. Find a checkpoint/LoRA/embedding on Civitai, download it into ComfyUI, and use its trigger words. Optionally pair the official Civitai MCP for community features (images browsing, posting, collections).
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Diagnose and fix video/image color OBJECTIVELY with the get_image (action:"analyze_color") tool (scopes/stats such as black/white points, contrast, saturation, clipping, cast) instead of eyeballing a contact sheet. Covers the "washed out" signature, why reference color-match (mkl/ColorMatch/ColorMatchAdobe) CAN'T add contrast a flat source lacks, the levels/contrast-stretch fix (core AdjustContrast / CurveEditor), the measure→fix→re-measure loop, the side-by-side sandbox pattern, and where to place the fix in a render graph (after decode, before save). Use when a render looks washed out / flat / dull / over-saturated / color-cast, or when deciding between a color-match and a contrast/levels fix.
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Core ComfyUI knowledge covering workflow format, node types, pipeline patterns, and MCP tool usage
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