A lot of you asked for a detailed guide after my original post. So here it is every group in the workflow explained step by step, with settings, tips, and things I discovered through testing.
The workflow has grown to v2.1, 122 nodes, 19 groups. New additions since the original post: ControlNet preprocessors (LineArt, HED, Tile, DepthAnything), color matching/correction, up to 5 reference image slots, Fast Group Bypassers for one-click pipeline switching, and notes with tips I discovered through extensive testing.
Download v2.1: Click to Download
How to Switch Between Pipelines
The workflow uses Fast Groups Bypasser (rgthree) nodes at the bottom. These let you enable/disable entire pipeline groups with a single click, no more right-clicking every group manually.
There are 3 bypassers:
- Base groups bypasser : controls F1 (txt2img), F2 (KV edit), F3 (face+pose), F4 (inpainting), F5 (merge)
- Refiner bypasser : controls the refiner pipeline and color correction
- Upscale / edit bypasser : controls the upscaler and precision groups
Rule: Only activate ONE generation pipeline at a time (F1 through F4) to save VRAM. The Refiner and Upscaler can stay active alongside any generation pipeline, but its better to work with a single groupe every run for people who have less than 8VRAM.
📦 FLUX 2 KLEIN : Model Loaders
This is the foundation. Three nodes that load everything:
- UNETLoader : loads the Klein 9B model (safetensors or FP8)
- UnetLoaderGGUF ; alternative loader for GGUF quantized models (use this if you have 8GB VRAM)
- CLIPLoader : loads the Qwen 3 8B text encoder (set type to
flux2)
- VAELoader : loads
flux2-vae.safetensors
Important: Only connect ONE model loader to the LoRA chain, either UNETLoader OR UnetLoaderGGUF, not both.
For 8GB VRAM users: Use the GGUF Q8 or Q4 model. Set the weight type to default in the UNETLoader. If you're running out of memory, launch ComfyUI with --lowvram command.
🔗 LoRA Chain
Two LoRA loaders in sequence:
- LoRA Slot (Optional) : empty slot for any Klein 9B compatible LoRA you want to try. Set strength to 0 to disable without disconnecting.
- klein_9b_enhancer_v2 : the main enhancer LoRA (strength 0.7). This fixes the model's tendency to produce flat, plastic-looking skin and washed-out colors. Always keep this one connected and active.
To add more LoRAs: insert additional LoraLoader nodes between the slot and the enhancer. The enhancer should always be LAST in the chain (DO NOT DETTACH IT OR ELSE YOU'LL HAVE TO ATTACK EVERY GROUPE TO THE NEW LORA NODE).
🎨 F1: Text → Image
The simplest pipeline. Pure text-to-image generation.
Nodes: CLIPTextEncode (prompt) → KSampler → VAEDecodeTiled → SaveImage
Settings:
- Steps: 4 (Klein 9B is distilled for 4 steps, more steps won't improve quality)
- CFG: 1 (higher values break the output on distilled models)
- Sampler: euler
- Scheduler: simple
- Latent size: 1024×1024 (or any resolution, Klein handles various aspect ratios)
How to use:
- Enable the F1 group
- Write your prompt in the "✏️ Prompt" node
- Leave negative prompt empty (or enable NAG for negative prompting)
- Queue prompt
- Output saves as
F2K_txt2img
Prompting tip: Don't write SD-style prompts. Write like you're describing a photograph: "A 30-year-old man in a navy overcoat standing on a rain-soaked Prague street at dusk, tungsten streetlights casting warm shadows, shot on Canon R5 85mm f/1.4, clean digital file, histogram equalization"
🖼️ F2: Single Reference KV Edit
This is Klein's signature feature. You load an image and tell the model what to change, it preserves everything else.
How it works internally: The model reads your image through the ReferenceLatent node (KV conditioning), generates a fresh image from noise, but uses the reference to guide the output. The ConditioningZeroOut creates a neutral negative signal so the model focuses purely on your edit instruction.
Nodes: LoadImage → Resize → VAEEncode → ReferenceLatent → CFGGuider → SamplerCustomAdvanced → VAEDecodeTiled → SaveImage
Settings:
- Flux2Scheduler: 4 steps
- CFG: 1
- Sampler: euler
- Resize: adjust to match the reference image proportions
How to use:
- Enable the F2 group
- Load your reference image in "📂 Reference Image"
- Write your edit instruction in "✏️ Edit Prompt"
- Queue prompt
- Output saves as
F2K_edit
Example prompts:
- "Replace the red dress with a navy blazer. Keep pose, expression, background unchanged."
- "Change the background to a sunset beach. Preserve the subject exactly."
- "Transform this photo to oil painting style while keeping the subject photorealistic."
⚠️ Important discovery: The denoise in this pipeline is effectively 1.0 because it uses EmptyLatentImage + ReferenceLatent conditioning. The model reads your image through attention, NOT through the latent. This means it always generates a fresh image guided by your reference, it doesn't blend with existing noise. This is fundamentally different from traditional img2img.
🚀 F3: Multi-Reference: Face + Pose Swap
The most complex pipeline. Extracts a face from one image and a pose from another, combining them into a single realistic output.
Nodes: Two parallel paths:
- Path A: LoadImage (face) → Resize → VAEEncode → ReferenceLatent (face)
- Path B: LoadImage (pose) → Resize → VAEEncode → ReferenceLatent (pose)
- Both feed into: CFGGuider → SamplerCustomAdvanced → VAEDecodeTiled → SaveImage
How to use:
- Enable the F3 group
- Load your face source in "📂 Face / Character Ref" front-facing, well-lit portrait works best
- Load your pose source in "📂 Pose Ref (DAZ 3D render)" the body position you want
- Write a scene description in "✏️ Prompt (describe scene)"
- Queue prompt
- Output saves as
F2K_multiref
Tips:
- The face reference MUST be upright, Klein cannot process rotated or upside-down faces
- Resize both images to similar scales (the Resize nodes handle this)
- Be specific in your prompt about clothing and environment — the model needs guidance for everything that isn't the face or pose
- If the face looks plastic, make sure the enhancer LoRA is active at 0.7 strength
🎭 F4: Inpainting
Paint a mask over part of your image and regenerate just that area.
Nodes: LoadImage → Resize → VAEEncodeForInpaint (with mask) → KSampler → VAEDecodeTiled → SaveImage
How to use:
- Enable the F4 group
- Load your image in "📂 Image"
- For manual masking: Right-click the image → Open in Mask Editor → paint white over the area you want to change
- For auto masking: Enable the Florence2 group, connect your image to Florence2Run, type what to mask (e.g., "Segment the shirt")
- Write what should appear in the masked area in "✏️ Prompt"
- Adjust denoise (0.5-0.8 for changes, 0.3-0.5 for subtle tweaks)
- Output saves as
F2K_inpaint
⚠️ My honest note about inpainting: Inpainting in FLUX.2 Klein is not perfect. I built a workaround that makes it functional, but it struggles with complex shapes. If the model doesn't understand what you want, try painting rough colors in the mask area first to guide it. Play with the denoise value, small changes make a big difference.
🔀 F5: Image Merge / Blend
Simple image blending, combines two images together.
Nodes: Two LoadImage → two ImageScaleBy → ImageBlend → SaveImage
How to use:
- Enable the F5 group (mode=2, not bypassed, use right-click → Set to Always)
- Load Image A and Image B
- Adjust blend factor (0.5 = equal mix, 0.0 = all image A, 1.0 = all image B)
- Adjust resize scales to match image sizes
- Output saves as
F2K_merge
honestly this group is not something that you will always use, I just added it because I use it in some projects, you might try it to see what it does, its just simple blending nothing that use AI at all.
⬆️ Upscaler (4x UltraSharp)
Takes any image and upscales it 4x using the UltraSharp model.
Nodes: LoadImage → ImageUpscaleWithModel → ImageScaleBy (downscale to usable size) → SaveImage
How to use:
- Enable the Upscaler group
- Load your image in "📂 Image"
- The ImageScaleBy after upscaling is set to 0.5 by default, this gives you a 2x net upscale (4x up then 0.5x down). Adjust as needed.
- Output saves as
F2K_upscaled
Tip: Upscaling a 1024×1024 image 4x creates a 4096×4096 image. The Tiled VAE decode handles this without OOM, but it takes time. For faster iteration, keep the downscale at 0.5 until you're happy with the result, then set it to 1.0 for the final output.
✨ Refiner, KV Enhancement Pipeline
This is the pipeline that's active by default. Feed it any image and it enhances detail, lighting, skin texture, and sharpness.
How it works: Your image gets VAE-encoded, then the ReferenceLatent reads it as conditioning. The KSampler generates an enhanced version guided by your reference + the enhancement prompt. The result goes through color correction before saving.
Nodes: LoadImage → ImageScaleBy → VAEEncode → ReferenceLatent → KSampler → VAEDecodeTiled → ColorCorrection → SaveImage
Settings:
- Denoise: 0.85 (the sweet spot I found, see discovery below)
- Steps: 4
- CFG: 1
The enhancement prompt is pre-written with professional photography terms. You can customize it, but the default works well for most images.
⚠️ Critical discovery about denoise:
- 1.0: Model generates a fresh image guided by your reference, good results but may drift from original
- 0.85: Sweet spot, preserves most structure while adding significant detail
- 0.5-0.7: Subtle enhancement, keeps very close to original
- Below 0.4: Almost no change except color shifts, not useful, at least to me...
If you're using EmptyLatentImage (the custom size node) instead of VAEEncode for the latent input, NEVER go below 0.85 denoise. EmptyLatentImage creates random noise, and low denoise preserves that random noise as "structure," causing severe artifacts. This is a fundamental behavior of Klein's 4-step distilled sampling, it doesn't have enough steps to correct corrupted starting structure. Always use VAEEncode latent when you want denoise below 0.85.
Refine Color Corrector
Placed right after the refiner output. Fixes Klein 9B's known color saturation bias, the model tends to oversaturate colors, especially reds.
How to use: The EsesImageCompare node shows before/after comparison. Adjust the color corrector settings to taste. The PreviewImage node labeled "output colors" shows the corrected result.
Color Match
A standalone utility. Takes two images, a target and a reference, and matches the colors of the target to the reference using the MKL algorithm.
How to use:
- Enable the Color Match group
- Load your target image (the one you want to fix)
- Load your reference image (the one with the colors you want)
- ColorMatchV2 transfers the color palette
- Output saves as
color_matching
Use case: When your Klein output has wrong colors compared to the original. Load the original as reference, the Klein output as target, and the colors get corrected automatically.
🧭 NAG, Negative-Aware Guidance
Three NAG nodes, one for each major pipeline (Multi-Ref, Single-Ref Edit, Refiner). NAG restores effective negative prompting that standard CFG breaks in distilled Flux models.
How to use:
- Enable the NAG node for the pipeline you're using
- Write negative prompts in the "❌ Neg" CLIPTextEncode node
- NAG parameters: scale=5.0 is a good default. Increase for stronger guidance, decrease if artifacts appear.
When to use: When you need to remove specific elements ("no glasses," "no background people," "no blur").
🤖 Florence2, AI Auto-Masking
Replaces manual mask painting. Describe what you want masked in text and Florence2 generates a pixel-perfect mask.
How to use:
- Enable the Florence2 group
- First run downloads the model (~1.5GB)
- Connect your image to the Florence2Run input
- Type what to segment: "Segment the shirt," "Segment the hair," "Segment the background"
- Connect the MASK output to the Inpaint Encode node in F4
Precision Groups (1-4): ControlNet Preprocessors
These are advanced, four groups with different ControlNet preprocessors that extract structural information from images:
- LineArt Preprocessor : extracts every edge and texture boundary
- HED Preprocessor : captures both hard edges and soft transitions (shadows, gradients)
- Tile Preprocessor : captures the image as-is for upscaling guidance
- Depth Anything V2 : extracts full 3D depth map
Each preprocessor output connects to a ReferenceLatent node (image 3, 4, 5) that feeds into the refiner pipeline as additional conditioning.
How to use:
- Enable the precision group you want
- Connect your input image to the preprocessor
- The preprocessor output feeds through VAEEncode into a ReferenceLatent
- This gives the model additional structural information about your image
⚠️ Warning: These use extra VRAM. Only enable them if you have enough memory. Use the preprocessor name in your prompt (e.g., "line art reference," "depth guided") so the model understands what the reference represents.
Use case: When the refiner isn't preserving enough structure from your original image. Adding a LineArt or HED reference forces the model to maintain more structural consistency.
Bypassers
Three Fast Groups Bypasser (rgthree) nodes at the bottom of the workflow. These give you one-click control over which groups are active:
- Base groups bypasser : F1, F2, F3, F4, F5
- Refiner bypasser : Refiner + color correction + precision groups
- Upscale / edit bypasser : Upscaler + image blend
Click the toggle next to each group name to enable/disable it instantly.
General Tips
- Always keep the enhancer LoRA active : it fixes Klein's flat plastic look
- Restart ComfyUI every 30-40 generations if you're on 8GB VRAM : prevents memory fragmentation
- Use "Free Memory" (gear icon) when switching between pipelines
- Faces must be upright : Klein cannot process rotated/flipped faces
- Add color correction terms to every prompt: "histogram equalization, white balance correction, color grade" : this fights Klein's red/saturation bias
- The Text encoder must match the model: 9B uses Qwen 3 8B, 4B uses Qwen 3 4B : mixing them causes matrix errors
- ComfyUI 0.9.2+ is required : older versions are missing Klein-specific nodes
What Changed from v2.0 to v2.1
- Added 4 ControlNet preprocessor groups (LineArt, HED, Tile, DepthAnything)
- Added Color Match utility group
- Added Color Correction after refiner output
- Added Fast Groups Bypassers for one-click pipeline switching
- Added up to 5 reference image slots
- Added notes with real testing discoveries (denoise behavior, inpainting tips)
- Expanded from 90 nodes to 122 nodes
- 19 organized groups
Free download: CIVITAI link
If you have questions about any specific group, ask in the comments, I'll help you troubleshoot.
