Character Dataset Creation — Workflow Guide

186 nodes · 10 functional groups · 214 links · 60 saved base images at the published defaults; either optional editor adds 62 edited images

Builds a multi-angle character reference set from one source image
Uses a detected face crop to stabilize identity across generated views
Creates frontal and rear anchors, 10 upper-body angles, 10 full-body angles, and 40 portrait variations
Includes two independent, optional Flux 2 and Krea 2 batch-edit stages
Saves organized PNG batches with the complete workflow embedded

What Is This?

Character Dataset Creation turns one character image into a broad, consistently framed reference set for LoRA training, character sheets, identity testing, and later image-edit workflows.

The graph first detects and crops the face from the selected source image. It then generates a frontal full-body anchor and a rear full-body anchor. Those references are reused to create separate 10-view upper-body and full-body angle sequences plus two independent sets of 20 portrait variations. Eclipse Set/Get nodes carry the model, VAE, seed, dimensions, images, and output path between groups without covering the canvas in long links.

Original Workflow and Rebuild Credit

The original workflow concept comes from AI Influencer Dataset Maker for Consistent Character.

This Eclipse version is not a copy of that workflow. I rebuilt it node by node around Eclipse's nodes, list handling, Set/Get routing, model loaders, previews, and save system. The original graph uses Qwen Image Edit with SDXL plus several detailer stages intended to work around weaknesses in that model combination. This rebuild replaces that edit and correction stack with two independent optional branches: Flux 2 Edit and/or Krea 2 Edit. Either editor can run alone, both can run together, or both can be muted for base-dataset generation only.

The workflows share the goal of producing a consistent multi-view character dataset, while their node layout, routing, model stack, and editing approach are different.

At the saved defaults, the workflow writes:

10 images from Angled Views: Upper Body
10 images from Angled Views: Full Body
20 images from Variation (1)
20 images from Variation (2)

The frontal and rear anchors are retained as workflow variables and can be included in either optional edit batch, but they are not saved separately by the base generation groups. The result at the saved defaults is 60 base PNG files. Enabling either editor adds 62 edited PNG files for a total of 122; enabling both adds 124 edited PNG files for a total of 184.

The Complete Flow

Source character image
        ↓
Smart face detection and masked crop
        ↓
FLUX.2 Klein model, text encoder, VAE, dimensions, and shared seed
        ↓
Rear anchor + frontal anchor
        ↓
10 upper-body angle views + 10 full-body angle views
        ↓
20 Variation (1) images + 20 Variation (2) images
        ↓
Collect all active anchors and batches (62 working images)
        ↓
Optional parallel editors: Krea 2 and/or Flux 2
        ↓
PNG dataset folders with embedded workflow metadata

The generation stages reuse one shared 64-bit seed. A connected seed input overrides the large saved seed values still visible on the individual KSampler widgets.

Important Content Notice

Both NSFW-named edit groups are optional and independent. They consume the same 62-image working set in parallel rather than feeding one editor into the other. You can run either editor alone, run both together, or mute both and generate only the base dataset.

The saved workflow has both Krea2 Edit (NSFW) and Flux2 Edit (NSFW) muted (Never). Their saved instructions remove the subject's clothing and request explicit nudity. Leave both groups muted for the clothed 60-image base dataset. If you enable either edit pipeline but want to change its purpose, replace its instruction with a non-explicit edit such as an outfit, background, hairstyle, or lighting change.

Despite their saved group names, Flux2 Edit and Krea2 Edit are general image-editing stages. Both are muted by default, and either can instead be enabled as an optional quality pass over all 62 working images. Replace the saved NSFW instruction before enabling the group. Ask the editor to preserve identity, pose, expression, clothing, framing, and the featureless white background while correcting only unwanted generation defects. Retain natural skin texture and the realistic casual-photo character so the result does not become an overly polished studio portrait.

For example:

Keep the same person, identity, pose, expression, clothing, framing, and
featureless white background. Improve image coherence and technical quality
only: correct malformed details, excessive compression, missed focus, and harsh
artifacts while retaining natural skin texture and a realistic casual-photo look.

Assuming every source and edit succeeds, the available modes save:

Both editors muted: 60 base images
Krea 2 only: 60 base images + 62 Krea 2 edits = 122 files
Flux 2 only: 60 base images + 62 Flux 2 edits = 122 files
Both editors active: 60 base images + 124 edits = 184 files

Use the explicit edit stages only with clearly adult subjects and material you are authorized to process. Never use them with minors or age-ambiguous subjects.

Before You Run It

Update ComfyUI

This workflow uses current ComfyUI model support, subgraphs, ReferenceLatent, list execution, and Krea 2 conditioning. Update ComfyUI before troubleshooting missing core nodes.

Restart ComfyUI after installing or updating custom nodes.

Required External Repositories

The complete graph uses these custom-node packages:

ComfyUI Eclipse — image loading and cropping, folder and filename controls, prompt lists, Set/Get routing, previews, joining, seeds, sampling, and image saving.
ComfyUI Smart Model Loader — model, text encoder, VAE, LoRA, PIPE, conditioning, and sampler infrastructure.
ComfyUI SmartLLM — Smart Detection and its registered segmentation model.
ComfyUI-KJNodes — the optional SageAttention model patch in the main model group.
comfyui-krea2edit — Krea 2 source-preservation patch and image-grounded edit conditioning.

The SageAttention patch is optional. Its node is bypassed in the saved workflow. Leave it bypassed when SageAttention is unavailable or when you do not want to use it. The Krea 2 edit package is required only while the Krea2 Edit group is enabled.

Required Base Models

The main character-generation path is configured for the following files.

Diffusion model — ComfyUI/models/diffusion_models/

flux-2-klein-9b.safetensors

The official FLUX.2 Klein repository is gated. Sign in, accept its terms, and review the FLUX non-commercial license before downloading or publishing outputs.

Text encoder — ComfyUI/models/text_encoders/

qwen_3_8b_fp8mixed_abliterated.safetensors

VAE — ComfyUI/models/vae/

flux2-vae.safetensors

The workflow intentionally selects the abliterated Qwen text encoder rather than the standard qwen_3_8b_fp8mixed.safetensors. Select the exact saved filename or deliberately change the loader to an encoder you have tested with your prompts.

Smart Detection Model

The Load Image group selects:

Model: Anzhc Face -seg [segm]

SmartLLM manages this registered detection model. If it is not installed, allow the package's verified model-acquisition flow to download it, then rerun the workflow. The detector returns both an image and a mask; the mask is what drives the face crop.

Models for the Optional Krea 2 Edit Group

These files are needed only while Krea2 Edit (NSFW) is active.

Diffusion model — ComfyUI/models/diffusion_models/

Krea2DarkBeast3.0-INT8-Convrot-ComfyUI.safetensors

Text encoder — ComfyUI/models/text_encoders/

qwen3vl_4b_fp8_scaled.safetensors

VAE — ComfyUI/models/vae/

qwen_image_vae.safetensors

Identity edit LoRA — ComfyUI/models/loras/krea2/

krea2_identity_edit_v1_2.safetensors

The workflow expects the LoRA inside the krea2 subfolder and enables it at model strength 1.0. If you store it elsewhere, select the actual path in the LoRA Stack node. Review the Krea 2 community license and every finetune or LoRA license before commercial use.

Optional Flux 2 Edit Model

The muted Flux2 Edit group is prepared for:

darkBeastMar0326Latest_dbkleinv2BFS.safetensors in ComfyUI/models/diffusion_models/
The same qwen_3_8b_fp8mixed_abliterated.safetensors text encoder used by the base graph
The same flux2-vae.safetensors VAE used by the base graph

You do not need this checkpoint when the complete Flux2 Edit group stays muted.

Select Your Own Source Image

The workflow uses input/example.png as a portable placeholder. Open Load Image (Metadata Pipe) and choose your own source before queuing.

For the most reliable identity crop, use an image with:

One clearly visible adult subject
A face that is not heavily occluded
Enough resolution for the detector to isolate facial detail
Neutral or simple lighting
No other prominent faces competing for selection

The source can be a portrait rather than a full-body image. The workflow uses the detected face to construct the later full-body views.

Group-by-Group Guide

Load Image

This group prepares the identity reference.

Load Image reads the chosen source.
Smart Detection finds the face and returns a segmentation mask.
Image Crop by Mask expands context by 1.2 (<zoom in, >zoom out)
Preview Image (DOM) [Stop] displays the result for review.
ImageScaleToTotalPixels scales the accepted crop to approximately 1 megapixel with Lanczos.
The final image is published as img_face.

The preview's Stop control is off in the saved graph. Turn it on when you want to inspect the crop before paying the cost of all later generations.

The face crop is the identity foundation for every later group. If it includes the wrong person, too much background, or a clipped face, stop here and fix detection before continuing.

Model

This is the central configuration group for base character generation.

The saved model settings are:

Model type: UNet Model
Diffusion model: flux-2-klein-9b.safetensors
Text encoder: qwen_3_8b_fp8mixed_abliterated.safetensors
CLIP type: flux2
VAE: flux2-vae.safetensors
Canvas: 848 × 1248
Batch size: 1
Loader LoRAs: none enabled

The loader publishes the model, CLIP, VAE, width, height, and a format-aware empty latent through named Set nodes. The empty latent is created from the loaded VAE's detected channel count and downscale, then published as lat_empty for the generation groups. A separate Eclipse Seed node publishes one 64-bit seed as Seed for the complete graph.

Character Name and Output Folder

The Character Name text node is saved as ushi. Change it before running.

The Folder Path node uses:

Root folder: images/Datasets
Date folder: off
Batch folder: off

The character name is joined to each image-category prefix, producing an output layout under:

ComfyUI/output/images/Datasets/<character-name>/

Choose a short filesystem-safe name. A unique name prevents multiple subjects from being mixed in one training folder.

Optional SageAttention

The KJNodes Patch Sage Attention node is wired between the model loader and Set_MODEL. It is bypassed in the saved workflow. Leave it bypassed when SageAttention is unavailable or when you do not want to use it.

Do not mute this node (Never). Bypass reroutes the unchanged model to Set_MODEL, while Mute/Never drops the connection and would leave Set_MODEL without a model.

Only change the node from Bypass after SageAttention is installed and compatible with your GPU, CUDA, PyTorch, and ComfyUI versions. If the patched graph crashes at the first attention call, return the node to Bypass.

Rear View

The rear group creates the first full-body anchor from the detected face.

Its saved prompt is:

full body shot of a woman from behind, fully white background

The prompt is encoded, the positive conditioning receives the face latent as a reference, and the shared VAE-aware 848 × 1248 lat_empty is sampled with:

Steps: 8
CFG: 1.0
Sampler: res_2s
Scheduler: Beta
Denoise: 1.0

The decoded result is shown in a reviewable DOM preview and published as img_rear. It is not written to disk by this group.

A rear view generated from a frontal face reference is inherently interpretive. Check hair length, clothing, body proportions, and background before letting it guide 60 later outputs.

Frontal View

The frontal group generates the matching front-facing anchor.

Its saved prompt is:

full body shot of a woman, fully white background

It uses the central 848 × 1248 lat_empty from the Model group and samples with 8 steps, CFG 1.0, res_2s, beta57, and denoise 1.0. The face crop is VAE-encoded and attached to the positive conditioning with ReferenceLatent.

The group publishes:

img_front — decoded frontal anchor
lat_face — encoded face reference

These variables feed the later angle and variation stages. Frontal View consumes the reusable lat_empty published by the Model group. The frontal anchor is previewed but not saved separately by the base graph.

Angled Views: Upper Body

This group generates a 10-image turntable-style set:

Front
Rear
Front-left three-quarter
Left profile
Rear-left three-quarter
Front-right three-quarter
Right profile
Rear-right three-quarter
High-angle front
Low-angle front

Each prompt requests consistent waist-up framing, a fitted white long-sleeve shirt, and a plain white wall. In side and rear three-quarter views, the head and gaze follow the body's direction rather than turning back toward the viewer. The front three-quarter prompts use visible diagonal orientation instead of numeric degrees. The final two prompts mirror the full-body bank's explicit high-angle and worm's-eye-view language, but use upper-body perspective cues: the elevated view exposes the crown and shoulder tops, while the low view places the lower torso nearer than the receding head and shoulders. The conditioning chains three reference latents in this order:

Detected face → generated frontal anchor → generated rear anchor

The list is sampled with:

Steps: 6
CFG: 1.0
Sampler: Euler
Scheduler: Beta
Denoise: 1.0

The resulting PNG files use the Angles_Upper prefix and are saved beneath the active character folder. The complete workflow is embedded in every PNG; A1111-style generation data is disabled.

The first two angle prompts overlap with the separately generated anchors, but they are new samples with a different sampler profile. Treat them as additional dataset examples rather than exact copies.

Angled Views: Full Body

This group repeats the same 10 viewpoints as the upper-body group while explicitly requesting head-to-toe framing. The first eight prompts require the complete standing figure and both feet inside the frame. They also keep the outfit consistent across the turntable sequence:

Fitted white long-sleeve shirt
Straight-leg blue jeans
Simple white sneakers

The side and rear three-quarter prompts direct the subject's gaze along the body's orientation rather than toward the viewer. The final two prompts use explicit high-angle and worm's-eye-view language plus visible perspective cues instead of numeric degrees: the elevated view shows more floor around the figure, while the floor-level view brings the shoes into the near foreground. Both still require the complete figure inside the frame.

This group uses the same three-reference chain and 6-step Euler/Beta sampling profile as Angled Views: Upper Body. Its files use the Angles_FullBody prefix and are published as img_angles_full for the optional edit stages.

Together, the two angle groups contribute 10 closer identity views and 10 full-body views, reducing the risk that a character LoRA learns only upper-body framing.

Variation (1)

Variation (1) contains a String Multiline List node with 20 line-separated prompts covering different:

Tops, sweaters, jackets, dresses, and colors
Front, side, three-quarter, over-shoulder, high-angle, and low-angle framing
Flash, daylight, ambient, warm, and synthetic lighting
Clean, candid, grainy, blurred, and phone-camera aesthetics

The String Multiline List is the main place to control the outcome of this group. Each non-empty line is one complete variation prompt and produces one image. Edit a line to change an image's clothing, pose, framing, expression, lighting, background, or camera style; add or remove lines to change the number of generated variations.

The included variation prompts keep an uninterrupted featureless white wall across the entire background. Their casual realism comes from capture characteristics such as uneven automatic exposure, sensor noise, JPEG compression, slight focus softness, sharpening halos, and mild lens distortion. When editing the banks, describe the visible lighting effect rather than naming a room feature or physical light source; words such as window, lamp, interior, furniture, or room corner can encourage the edit model to add those elements to the image.

The Prefix node prepends a female character, to every non-empty prompt line so the complete variation batch stays anchored to the intended gender. Without an explicit gender prefix, some of the otherwise neutral variation prompts can produce male subjects. Change this shared Prefix when generating another gender—for example, a male character,—instead of repeating the gender in all 20 multiline prompts.

The reference chain uses the stored face latent, another encoding of img_face, and the generated rear anchor. This effectively gives the face reference extra weight while retaining rear-side information.

The saved sampling profile is:

Steps: 4
CFG: 1.0
Sampler: Euler
Scheduler: Beta
Denoise: 1.0

The 20 PNG files use the Variation_1 prefix.

Variation (2)

Variation (2) has its own String Multiline List and is a second, independent 20-prompt bank. It follows the same wiring, duplicated face-reference emphasis, rear reference, and 4-step Euler/Beta sampling profile as Variation (1), but varies garments, camera angles, lighting, and capture style again.

This second multiline list controls the outcome of Variation (2) in exactly the same way: one non-empty line produces one image. Edit these lines when you want different outfits, views, compositions, expressions, lighting, or photographic styles. Adding or removing lines changes this group's output count without affecting Variation (1).

Its saved Prefix node also says a female character, and applies that gender anchor to every line in this second list. Edit the Prefix nodes in both variation groups when changing gender so their batches remain consistent; editing only one Prefix can make the two sets disagree.

The 20 PNG files use the Variation_2 prefix.

Together, the two Angled Views groups and the two variation groups save the 60-image base dataset at the published prompt counts. If you add or remove multiline prompts, the final dataset and optional editor-batch counts change accordingly.

Flux2 Edit (NSFW)

Every node in this group is muted (Never) in the published workflow. While muted, it does not load its model, edit images, stop for review, or save files.

When enabled, the group:

Collects img_front, img_rear, img_angles_upper, img_angles_full, img_variation_1, and img_variation_2.
Joins the active values into one 62-image batch.
Converts the batch to a list for per-image processing.
Uses the F2Edit subgraph to create noise, guider, sampler, sigmas, target latent, and VAE outputs.
Runs SamplerCustomAdvanced and decodes the edited images.
Optionally stops after decoding for review.
Saves accepted files with the nsfw prefix.

The saved Flux 2 edit loader supplies 5 steps, CFG 1.0, Euler, and the Simple scheduler. The F2Edit subgraph also contains a muted alternative Flux 2 scheduler and latent route; those nodes receive the same dynamic width and height connections, while the active route uses the loader-driven Basic Scheduler path.

Flux2 Edit can run alone or in parallel with Krea2 Edit. For a first test, enabling only one editor makes model, memory, and output problems easier to isolate; after both branches work independently, they can be active in the same queue.

Krea2 Edit (NSFW)

This optional group is muted (Never) in the saved workflow. It can be enabled alone, enabled alongside Flux2 Edit, or left muted for a base-only dataset.

It gathers the same six active image variables as the Flux 2 editor:

1 frontal anchor
+ 1 rear anchor
+ 10 upper-body angle images
+ 10 full-body angle images
+ 20 Variation (1) images
+ 20 Variation (2) images
= 62 source images

The Preprocessing subgraph applies the Krea 2 Identity Edit LoRA, VAE-encodes each source, patches the Krea 2 model with the source-preservation path, performs image-grounded prompt encoding, creates zeroed negative conditioning, and prepares a target latent matching the source dimensions.

Important saved edit settings are:

Diffusion model: Krea2DarkBeast3.0-INT8-Convrot-ComfyUI.safetensors
Text encoder: qwen3vl_4b_fp8_scaled.safetensors
VAE: qwen_image_vae.safetensors
Identity edit LoRA: krea2/krea2_identity_edit_v1_2.safetensors at 1.0
Grounding resolution: 768 px
Reference boost: 4.0
Secondary reference boost: 1.0
Fit mode: fit
Steps: 8
CFG: 1.0
Sampler: Euler
Scheduler: Beta
Denoise: 1.0
Seed: connected to the workflow's shared Seed node

ref_boost = 4.0 is a strong identity-preservation setting. Reduce it if edits look too close to the source or resist the instruction. The node pack's own guidance treats grounding resolution as another tradeoff: lower values can strengthen edit adherence, while higher values can favor likeness.

When this group is enabled, the Image Comparer receives the source list and the edited output so you can compare them. The edited PNG files use the nsfw_kr2 prefix and are saved beneath the same character folder.

Turn This Into a General-Purpose Editor

The Krea 2 stage is not limited to its saved explicit prompt. Replace it with instructions such as:

Keep the same person, face, pose, framing, and white background. Change only
the outfit to a dark green training tracksuit with no logos.

or:

Keep the character identity and clothing unchanged. Replace the flat white
background with a softly lit neutral gray studio backdrop.

Use one clear edit per pass. Instructions that simultaneously change clothing, hair, pose, camera, lighting, and background make identity drift harder to diagnose.

Output Structure

With the saved folder controls and character name ushi, the base outputs—and the optional Krea output when that group is enabled—are organized approximately like this:

ComfyUI/output/images/Datasets/ushi/
├── Angles_Upper_0001.png ... Angles_Upper_0010.png
├── Angles_FullBody_0001.png ... Angles_FullBody_0010.png
├── Variation_1_0001.png ... Variation_1_0020.png
├── Variation_2_0001.png ... Variation_2_0020.png
└── nsfw_kr2_0001.png ... nsfw_kr2_0062.png  (only when Krea2 Edit is enabled)

Counters continue from existing matching files rather than overwriting them. Change the character name or clear/move an earlier dataset when you need a fresh numbering sequence.

All Save Images nodes use:

PNG output
4-digit counters
Workflow embedding enabled
Generation-data embedding disabled
Disk saving enabled
Internal Save Images preview display disabled

The separate DOM Preview nodes still show the important intermediate batches on the canvas.

Quick Start

Safest Base-Dataset First Run

Update ComfyUI and install Eclipse, Smart Model Loader, and SmartLLM.
Install the FLUX.2 Klein base model, abliterated Qwen text encoder, and Flux 2 VAE.
Select your own source image in Load Image.
Change Character Name from ushi to a unique folder name.
Confirm that the complete Krea2 Edit group remains muted.
Leave Flux2 Edit muted and SageAttention bypassed.
Turn on Stop in the face-crop preview.
Queue once, inspect the crop, then turn Stop off if it is correct.
Queue the complete base graph.
Inspect identity consistency across the 10 upper-body angles, 10 full-body angles, and 40 variations before using them for training.

Full Published Route With Krea 2 Editing

Complete a successful base-only run first.
Install comfyui-krea2edit and all four Krea edit model files.
Confirm the identity LoRA resolves at models/loras/krea2/krea2_identity_edit_v1_2.safetensors.
Enable Krea2 Edit and keep Flux2 Edit muted.
Review or replace the saved edit instruction.
Queue the workflow.
Compare the 62 sources and edits in Image Comparer.
Review the saved nsfw_kr2 batch before mixing it into a training dataset.

Switching to Flux 2 Edit

Install the prepared DarkBeast Flux 2 checkpoint.
Mute Krea2 Edit.
Enable all intended nodes in Flux2 Edit.
Keep its review Stop active for the first pass.
Queue with the same source image and shared seed.
Inspect the decoded results before allowing the Save Images stage to continue.

Running Both Editors Together

Install the model requirements for both edit groups.
Enable Flux2 Edit and Krea2 Edit.
Review the separate instruction in each branch.
Queue the workflow; both branches independently receive the same 62 source images.
Compare the nsfw and nsfw_kr2 output batches separately.

Running both editors does not chain their results. At the saved structure, neither branch edits the other branch's output.

Dataset Curation Tips

Generating files is only the first half of dataset preparation. Before training:

Remove images with identity drift, extra limbs, distorted hands, duplicated features, or inconsistent age.
Check that left/right and rear views still depict the same character.
Avoid overrepresenting one outfit, crop, or camera angle unless that bias is intentional.
Separate explicit and non-explicit subsets if you want independent control during training.
Caption visible traits accurately; do not caption details that the image does not contain.
Keep a copy of the original source and the two anchor previews for comparison.
Review embedded workflow metadata before distributing images if you do not want the graph and local generation settings included.

The workflow creates variety through prompts, but it cannot decide which samples are suitable training data. Manual curation has a larger effect on a small character LoRA than keeping every generated image.

Troubleshooting

The Workflow Loads With Missing Nodes

Install or update the custom-node repositories listed above, restart ComfyUI, and reload the workflow. If only Krea 2 or KJNodes nodes are missing, keep their groups disabled while testing the base graph.

The Source Image Is Still `example.png`

Open Load Image (Metadata Pipe) and select your own file. The workflow points to input/example.png as a portable placeholder.

Smart Detection Downloads or Fails on First Run

The selected Anzhc Face -seg [segm] model may need to be acquired by SmartLLM. Allow the verified download to finish, confirm the chosen Q4_K_M variant is available, and rerun. If detection finds the wrong face, use a source with one clearer subject or adjust the detection controls.

The Face Crop Is Wrong

Turn on Stop in the crop preview. Adjust select_index, crop context, rotation, or the source image until the output contains the intended face with enough surrounding context. Do not continue with a bad crop.

A Model Is Marked Missing

Select the exact installed filename in Smart Model Loader. For Civitai-backed finetunes, Smart Model Loader can preserve the workflow's AIR identity and expose its download action when the referenced model is absent. Confirm the destination role before downloading.

Output Goes Into the Wrong Character Folder

Change the Character Name text node in the Model group. Folder Path supplies only the common images/Datasets root; each Add Filename Prefix node turns the character name into a subdirectory.

Frontal and Rear Images Are Not Saved

This is the published graph's design. They are previews and named variables used by downstream groups. The four base Save Images nodes write only Angled Views: Upper Body, Angled Views: Full Body, Variation (1), and Variation (2). Add a deliberate save branch if you also want permanent anchor PNGs.

The Base Run Produces 60 Files, Not 62

That is expected. The graph generates 62 working images, but the base groups save only 10 upper-body angles, 10 full-body angles, and 40 variations. The frontal and rear anchors are included when either optional 62-image edit batch is enabled.

Krea 2 Edit Fails Immediately

Check all of the following:

ComfyUI has current native Krea 2 support.
comfyui-krea2edit is installed and updated.
The selected diffusion model, Qwen3-VL text encoder, and Qwen Image VAE exist in their expected folders.
The identity edit LoRA exists under models/loras/krea2/ or is reselected.
The Krea2 Edit group is active. Flux2 Edit may be active or muted independently.

Identity Is Weak in Krea Edits

Keep the identity LoRA at 1.0, retain fit mode, and confirm the grounded encoder receives the same source batch as the model patch. The saved ref_boost is already a strong 4.0; raising it further can reduce editability rather than solve a poor source or bad base generation.

The Edit Instruction Is Ignored

Try a shorter instruction with one explicit change. Lower ref_boost, reduce grounding resolution from 768 toward 512, or use a less restrictive source. Compare at a fixed seed so prompt changes are the only variable.

Out of Memory

Try these changes in order:

Run the base graph with both edit groups muted.
Reduce the main 848 × 1248 canvas while preserving dimensions suitable for the model.
Increase block swapping or reduce blocks kept on GPU in Smart Model Loader.
Run Angled Views: Upper Body, Angled Views: Full Body, Variation (1), and Variation (2) as separate queues by muting unused groups.
Process Krea edits in smaller source subsets instead of the full 62-image collection.
Enable SageAttention only after verifying a compatible installation; it is not part of the known-safe published route.

Outputs Differ Even With the Same Seed

Model, encoder, VAE, custom-node versions, attention backend, dtype, and prompt-list order can all affect results. Keep the same installed files and group modes when making a fixed-seed comparison.

Custom Node Packages Used

Workflow infrastructure

ComfyUI Eclipse — folders, filenames, images, prompts, Set/Get channels, previews, joins, seeds, Kargim sampling, and PNG saving.
ComfyUI Smart Model Loader — model pipelines, CLIP/VAE extraction, conditioning helpers, LoRA Stack, and model acquisition.
ComfyUI SmartLLM — face segmentation through Smart Detection.

Optional optimization and editing

ComfyUI-KJNodes — bypassed SageAttention model patch.
comfyui-krea2edit — optional Krea 2 identity-edit preprocessing.

Core ComfyUI supplies empty latents, ReferenceLatent, standard KSamplers, advanced sampling, VAE encode/decode, image scaling, and the subgraph system.

Start with the base 60-image route, review the face crop and both anchors, and curate both generated angle sets before enabling either or both optional batch editors. A smaller clean dataset is more useful than a larger set with repeated identity errors.