Skip to main content

Flux.1 Kontext Image Edit

When your latent needs a glow-up — with precision, style, and zero tolerance for bad prompts.

🧩 What is Flux.1 Kontext Image Edit?​

The Flux.1 Kontext Image Edit node lets you edit images with surgical-level precision by manipulating latent space using a prompt. Unlike traditional text-to-image models, this one starts from a LATENT input — not a blank canvas — allowing you to transform an existing image while preserving its structure and composition.

It supports both flux-kontext-pro and flux-kontext-max models, and outputs both a freshly generated IMAGE and the updated LATENT, making it ideal for chaining edits across multiple stages of a workflow.

Think of it as the "Photoshop Liquify" tool, but powered by diffusion and a few thousand GPU cycles.

Flux.1 Kontext Image Edit

🚧 Special Requirements​

  • ✅ Requires a valid LATENT input (e.g., from a prior generation or encoding step).
  • ✅ Needs a compatible model (flux-kontext-pro or flux-kontext-max) loaded and wired up via UNet, CLIP, and VAE.
  • ✅ CLIP 1 and 2, UNet, and VAE must match the model family or your outputs will look like Picasso on acid.
  • ✅ This is not a standalone image editor — it’s one piece in a latent-space editing pipeline.

🔌 Inputs and Outputs​

Inputs​

  • LATENT – The latent representation of the image you want to modify.

Outputs​

  • IMAGE – The resulting image after applying the edit.
  • LATENT – The updated latent representation after generation.

⚙️ Node Settings & Parameters​

Each field has its own quirks, strengths, and "how-did-this-make-things-worse" settings. Let's dig in.

🔢 seed​

  • Controls the randomness.
  • Same prompt + same seed = same result.
  • Useful for reproducibility or batch processing.

🔁 control_after_generate​

  • Options:
    • fixed – Keeps the same seed every time.
    • increment – Adds 1 to the seed on each generation.
    • decrement – Subtracts 1 on each generation.
    • randomize – Full chaos mode; fresh seed every time.

🐾 steps​

  • Determines the number of inference steps (aka: how long the model refines the image).
  • Lower = faster, but coarser.
  • Higher = slower, but more detailed.
  • Sweet spot: 20–40 steps for most edits.

🧪 sampler_name​

📅 scheduler​

  • Schedulers determine how noise levels are distributed during diffusion.
  • Examples: normal, karras, exponential, ddim_uniform, kl_optimal
  • Some samplers work best with specific schedulers. Choose wisely or expect unholy artifacts.

🧭 guidance​

  • AKA "Classifier-Free Guidance Scale" or "CFG."
  • Higher values force the image to obey the prompt more strictly.
  • Range: ~1–20
    • Low (1–5) = Loose interpretations
    • Medium (6–12) = Balanced
    • High (13+) = Obsessive rule-following (sometimes at the cost of quality)

🗂️ filename_prefix​

  • Customizes the filename of the generated image.
  • Handy for batch runs or tracking changes across iterations.
  • Examples: "edit_pass1_", "cat_armor_variant_"

📝 prompt​

  • This is where you tell the model what changes you want.
  • More detail = better edits.
  • Vague nonsense = latent hallucinations.

🧠 unet_name​

  • Selects the diffusion backbone (UNet).
  • Must match the chosen flux-kontext model.
  • Wrong UNet = broken generations or mismatched results.

🔬 weight_dtype​

  • Options:
    • default – Uses the default precision (typically FP16)
    • fp8_e4m3fn – Fastest, lowest precision
    • fp8_e4m3fn_fast – Even faster, still low precision
    • fp8_e5m2 – Slightly better balance
  • Why this matters: Impacts speed vs. accuracy vs. VRAM.
  • Use default for most cases unless you're fine-tuning for performance.

🧠 clip_name1 / clip_name2​

  • Dual CLIP encoders that handle your text prompt.
  • Must match your model’s architecture. If you're not sure, refer to the model card/documentation.
  • Using the wrong ones can cause weird interpretations or semantic confusion.

⚙️ device​

  • Options:
    • default – Use whatever is available (ideally CUDA/GPU)
    • cpu – For when you're testing... or into self-punishment
  • Note: Flux-Kontext models are large. Running on CPU = slow, sad days.

🖼️ Image Preview​

  • A compact thumbnail preview of the output image.
  • Fast visual feedback to confirm you're not making visual soup.

✅ Use Cases​

  • Prompt-guided transformation of existing latent outputs.
  • Multi-stage image editing workflows (e.g., generation → inpainting → stylization).
  • Style changes, detail enhancement, or object replacement without losing layout.
  • Controlled batch editing with reproducible seeds.

🧪 Prompting Tips​

  • Be specific. “Change the dress to red” > “make it better.”
  • Include modifiers like lighting, mood, material, or art style for more directed edits.
  • Use negative prompting in your pipeline if needed (e.g., “no blur, no text”).
  • Lower guidance and steps for light edits. Higher values for total overhauls.

🔥 What-Not-To-Do-Unless-You-Want-a-Fire​

  • ❌ Feed it raw images instead of LATENTs.
  • ❌ Mismatch your unet_name / clip_name1/2 with the actual model.
  • ❌ Forget to load a VAE — you’ll get no image output.
  • ❌ Use CPU for full-size editing unless you enjoy 10-minute render times.
  • ❌ Assume fp8 will always save you. Precision matters in high-detail edits.

⚠️ Known Issues​

  • Missing components: Forgetting to load CLIPs or VAE will break the node.
  • Precision loss: Lower weight_dtype settings can cause loss of subtle details.
  • Latent drift: High guidance or many steps can deviate too far from the original image.
  • No preview update: Some changes (e.g., device) may not reflect immediately in the preview section.

📝 Final Notes​

The Flux.1 Kontext Image Edit node is a cornerstone of editable, prompt-driven diffusion workflows. It brings powerful latent manipulation into a composable node-based system that gives you control — with just enough room for chaos if you want it.

Plug it into your workflow, match your components properly, and enjoy prompt-guided editing that doesn't feel like rolling the dice.