Anatomy Correction in Diffusion Models¶

Methods for detecting and fixing anatomy mutations (extra fingers, distorted hands, missing limbs) in diffusion-generated images. Covers post-processing fixes, during-sampling guidance, training-time solutions, and ComfyUI integration for FLUX Klein 9B.

Detection Tools¶

YOLO-based Hand/Face Detection (ComfyUI Impact Pack)¶

UltralyticsDetectorProvider node (from ComfyUI-Impact-Subpack)
Models: hand_yolov8s.pt, hand_yolov8n.pt (bbox), face_yolov8m.pt
Location: ComfyUI/models/ultralytics/bbox/

HADM - Human Artifact Detection¶

Dataset: HAD - 37,000+ images annotated for human artifact localization. Detects local (distorted faces/hands) and global (missing/extra limbs) artifacts. Generalizes across unseen generators.

MediaPipe Hand Landmarks¶

21 hand keypoints per hand. Finger tips at landmarks 4, 8, 12, 16, 20. Can validate finger count programmatically. Not a ComfyUI node natively - needs custom wrapper.

During-Sampling Solutions¶

NAG - Normalized Attention Guidance¶

The most promising during-sampling fix for FLUX/Klein. Training-free, works at inference time.

Package: ComfyUI-NAG (github.com/ChenDarYen/ComfyUI-NAG)
Supports: FLUX, Flux Kontext, Wan, HunyuanVideo, Chroma, SD3.5, SDXL

Key parameters for FLUX:

Tune nag_tau + nag_alpha first, then nag_scale. Acts as negative guidance improving structural coherence.

Other Guidance Methods¶

• PAG (Perturbed-Attention Guidance): fixes ~80% of finger errors in SDXL at scale=0.3. FLUX support NOT confirmed.
• SEG, SWG, PLADIS, TPG, FDG, MG: available via sd-perturbed-attention package, FLUX compatibility varies.

Post-Processing: Detect-and-Inpaint¶

HandFixer (ComfyUI Native)¶

One-click hand repair. Uses FLUX.1-Fill as inpainting backbone.

cd ComfyUI/custom_nodes
git clone https://github.com/Xiangyu-CAS/HandFixer
pip install -r HandFixer/requirements.txt
# Requires: ae.safetensors, clip_l.safetensors, t5xxl_fp16.safetensors, fluxl-fill-dev.safetensors

Supports realistic + anime, images from SDXL/DiT/Midjourney. FLUX-Fill recommended over FLUX-dev (better boundary handling).

HandRefiner (MeshGraphormer)¶

Most reliable approach for guaranteed correct finger count. Reconstructs 3D hand mesh -> depth map -> ControlNet-conditioned inpainting.

ComfyUI node: MeshGraphormer Hand Refiner (in comfyui_controlnet_aux)
Key params: detect_thr=0.6, presence_thr=0.6, control_strength=0.4-0.8

Control strength 1.0 causes texture loss. Use 0.4-0.8 range.

FaceDetailer (Impact Pack)¶

Key parameters:
  guide_size: 512-768 (reference size for enlarging detected regions)
  denoise: 0.3-0.5 (subtle fix) | 0.5-0.7 (moderate correction)
  noise_mask: True
  force_inpaint: True
  feather: 5-20px
  cycle: 1-2 (2 for badly corrupted faces)
  crop_factor: 3.0
  bbox_threshold: 0.5
  dilation: 0-20

For hands: use DetailerForEach with hand_yolov8s.pt, denoise 0.4-0.6 (lower than faces).

Generic Inpainting Approach¶

1. Detect hands with hand_yolov8s.pt -> get mask
2. Expand mask with Mask Grow/Dilate node (10-20px)
3. Inpaint with FLUX Fill at denoise 0.75-0.90
4. Prompt override: "detailed hand, five fingers, correct anatomy, perfect hand"

Academic Methods (2024-2026)¶

Post-Processing Papers¶

Training-Time Papers¶

Hand-Specific Foundation Model¶

FoundHand-10M: 10M hand images with 2D keypoints + segmentation masks. Capabilities: repose hands, transfer appearance, synthesize novel views, zero-shot fix malformed hands. License: CC BY-NC 4.0.

Hand Fix LoRAs¶

Concept Slider LoRA (Zero Images)¶

Text-based slider requiring no training data:

positive: "perfect anatomy, correct hands, five fingers"
unconditional: "broken hands, extra fingers, deformed anatomy"
# Training: 25-50 steps only, rank 4, alpha 1
# Total time: ~5 minutes

Pre-trained LoRAs¶

Apply at strength 0.5-0.8 (not 1.0 - can over-correct). Can stack with style LoRA if total weight stays under 1.5.

Training Data for Anatomy Correction¶

DPO Pipeline (Automated)¶

1. Take 5K-10K prompts with humans/hands
2. Generate 8 images per prompt with different seeds
3. Score with PickScore/HPSv2 for quality ranking
4. Best = winner, worst = loser
5. Optionally: use real photos as winners (hard stage)
6. Effort: ~40K-80K generations, ~2-3 hours on 4x GPU

Edit LoRA Pairs (Semi-Automated)¶

Method: HandRefiner as labeler

1. Generate batch of images with Klein 9B
2. Run HandRefiner/HandCraft (detect + fix)
3. Before = original, After = HandRefiner output
4. Auto-filter: keep only pairs where hand detector found issues
5. Need human QA pass on results

Summary Table¶

Klein 9B Best Anatomy Pipeline¶

Recommended order (effectiveness ranking):

1. Generation: Base 9B, 25-30 steps, euler, CFG 3.5-4.0
2. During sampling: NAG (nag_sigma_end=0.75)
3. Post-gen detection: hand_yolov8s.pt + face_yolov8m.pt via Impact Pack
4. If hands bad: HandFixer or MeshGraphormer -> FLUX Fill inpaint at denoise 0.7
5. If face bad: FaceDetailer at denoise 0.4-0.5
6. Quality gate: aesthetic score + hand detection confidence
7. If still bad: regenerate with new seed (up to 3-4 attempts)

Klein 9B Distilled vs Base for Anatomy¶

Installation¶

cd ComfyUI/custom_nodes

# Impact Pack (face/hand detection + detailing)
git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack
git clone https://github.com/ltdrdata/ComfyUI-Impact-Subpack

# HandFixer
git clone https://github.com/Xiangyu-CAS/HandFixer
pip install -r HandFixer/requirements.txt

# ControlNet Auxiliary (MeshGraphormer, OpenPose)
git clone https://github.com/Fannovel16/comfyui_controlnet_aux

# NAG (during-sampling guidance)
git clone https://github.com/ChenDarYen/ComfyUI-NAG

# Required models:
# ComfyUI/models/ultralytics/bbox/hand_yolov8s.pt
# ComfyUI/models/ultralytics/bbox/face_yolov8m.pt
# ComfyUI/models/unet/fluxl-fill-dev.safetensors

Gotchas¶

• NAG bug for FLUX: the ComfyUI-NAG package had a FLUX guidance degradation bug fixed in June 2025. Must use the updated version - older versions make FLUX outputs worse, not better.
• MeshGraphormer control_strength=1.0 kills texture: using full control strength from the depth map produces plasticky, over-smoothed hands. Use 0.4-0.8 range for realistic results.
• Klein distilled CFG must be 1.0: the distilled model has guidance baked in. CFG > 2.0 produces "deep-fried" artifacts. The base model supports CFG 3.5-5.0 for better anatomy control.
• 4B models have significantly worse anatomy than 9B: neither 4B variant (distilled or base) achieves acceptable anatomy quality for professional use. Budget for 9B.

See Also¶

• FLUX Klein 9B Inference - optimal inference settings including anatomy hierarchy
• face detection filtering pipeline - detection tools for quality gating
• face beautify edit lora - edit LoRA for facial correction
• Diffusion LoRA Training - training parameters for Klein LoRAs
• LoRA Fine Tuning for Editing Models - MMDiT editing model patterns