An advanced conditioning node for Flux2 Kontext - Klein image editing workflows in ComfyUI. Encodes up to 3 reference images through the Flux2 VAE, attaches them as reference latents for Kontext attention, and encodes text via the Qwen3-8B LLM-based text encoder — all in a single node.

• 🖼️ Up to 3 reference images — each independently VAE-encoded and attached as a Kontext reference latent
• 🎛️ Per-image strength control — scale each reference latent individually to tune its influence
• 📐 Per-image resolution control — set the VAE encoding resolution independently for each image slot
• 🧠 Qwen3-8B text encoder — pure text encoding, no vision tokens, compatible with Flux2 architecture
• 💬 System prompt modes — optionally wrap the prompt with a Qwen3 instruct chat template (default, creative, minimal, custom, none)
• ⚡ Guidance embedding — bakes the Flux2 guidance scale directly into the conditioning
• 🔲 VAE tiling — optional tiled encoding for high-resolution images to reduce VRAM usage
• 🖼️ Text-to-image support — generates a correctly sized empty latent when no images or VAE are connected
• 🔗 All-in-one pipeline — replaces the need for separate Scale Image to Pixels, VAE Encode, and Reference Latent nodes. Connect your reference images directly to this node — it handles resizing, encoding, and latent attachment internally.
• 🔍 Debug image outputs — inspect resized input images before encoding

⚠️ No extra nodes needed: This node fully replaces Scale Image to Pixels, VAE Encode, and Reference Latent nodes. Connect your reference images directly to image1 / image2 / image3 — do not pre-process them through those nodes first.

Search for ComfyUI-Flux2KontextConditioner in the ComfyUI Manager and install.

cd ComfyUI/custom_nodes
git clone https://github.com/KursatAs/ComfyUI-Flux2KontextConditioner

1. Add the Flux2 Kontext Conditioner node to your workflow (flux/conditioning category).
2. Connect your CLIP (Qwen3-8B), VAE (Flux2 VAE), and at least one reference image.
3. Write your edit prompt.
4. Connect conditioning → KSampler, latent → KSampler.
5. Decode the KSampler output with a VAE Decode node.

1. Connect only CLIP — leave VAE and all image slots disconnected.
2. Set target_resolution to your desired canvas size.
3. Connect conditioning → KSampler, latent → KSampler.
4. Decode with a separate VAE Decode node.

Note: output_image1/2/3 are for debugging only. Connect to a Preview Image node to inspect the resized input. They are not required for generation.

• VAE tiling is useful when encoding images at 1536px or higher. It reduces peak VRAM at the cost of slightly slower encoding.
• image_strength values above 1.0 amplify the reference latent beyond its original scale — useful for stronger style transfer but may introduce artifacts.
• system_prompt_mode has a minor effect. Flux2 does not natively process system prompts, but Qwen3-8B was trained with these templates, so wrapping can subtly shift the token distribution. Use none if unsure.
• The alignment setting affects all image slots simultaneously. Set to 16 for maximum resolution fidelity.

• ComfyUI
• Python packages: torch
• Flux2 flux-2-klein-9b.safetensors, Qwen3-8B text encoder, Flux2 VAE

See LICENSE.

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• 📖 Documentation / Wiki
• 🔍 Comfy Registry