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Deploy Flux Kontext

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Flux Kontext Dev is a character consistency model from Black Forest Labs. Upload a reference photo and generate new scenes that preserve the character's identity — face, clothing, style, and features. Uses native in-context conditioning (no adapters or custom nodes), achieving 98% identity retention across generations. The FP8 variant runs on L4 (24GB VRAM) at $0.66/hr.

Deploy Flux Kontext in minutes

Starting at $0.66/hr on dedicated GPU

Specifications

ModelGPUVRAMPriceAction
Flux Kontext Dev FP8
Dev FP8 (Character Consistency)
L424 GB$0.66/hrDeploy

Prices include the service fee. Charges follow actual running time.

Requirements

ModelPilot assigns a 24GB cloud GPU to this deployment. Actual local VRAM requirements vary with model variant, precision, quantization, resolution, and workflow settings.

On ModelPilot, deploy on a dedicated cloud GPU (up to 80GB VRAM) starting at $0.66/hr with no setup required.

Includes full ComfyUI environment with custom node support.

Compare Flux Kontext

Source-backed GPU, VRAM, and cost comparisons for nearby deployment choices.

Use Cases

  • Character-consistent social media content
  • Brand mascot in different scenes
  • Visual storytelling and comics
  • Character design exploration
  • Consistent avatar generation

Related Models

Known Issues & Fixes

Real failures from running Flux Kontext and similar pipelines, with the mechanism behind each one.

Over-caution that fp8 degrades quality, so users force bf16 and double VRAM/download for no gain

Cause: fp8_e4m3fn_scaled checkpoints carry per-block scale factors that preserve dynamic range, matching bf16 quality for many modern diffusion/video models at ~half VRAM. Plain (unscaled) fp8_e4m3fn CAN visibly degrade — that's why the scaled repacks exist.

Fix: Prefer the 'scaled' fp8 variant; don't reflexively pick bf16. Do NOT generalize the claim to unscaled fp8.

A v2 workflow fails to load its encoder, or a user reuses v1 encoders and gets nothing

Cause: FLUX.2 dropped FLUX.1's dual T5-XXL+CLIP_L for a single Mistral-3-Small encoder (18GB fp8 / 36GB bf16) — and the smaller Klein 4B/9B variants use a Qwen-3 encoder instead. The encoder stack isn't stable across versions or even size tiers of one release.

Fix: Match encoder files to the exact variant (mistral*3_small_flux2 for FLUX.2 dev; qwen_3*\* for Klein). Don't reuse the previous version's encoders.

User expects an anime specialist but gets photorealism, or mis-sizes VRAM

Cause: Z-Image Turbo is a photoreal-leaning generalist, not an anime model; Z-Anime (SeeSee21 fine-tune) is a separate community model on the identical architecture and loads via the same zimage deploy path. Encoder architecture is Qwen-3-4B, VAE = Flux's ae.safetensors (335MB). fp8 diffusion 6.15GB (fits 12GB), bf16 12.3GB; GGUF Q3-Q8 for low VRAM.

Fix: A Turbo base and its fine-tunes share architecture + VAE (and the Qwen-3-4B encoder architecture — a fine-tune may ship its own tuned encoder weights, e.g. Z-Anime's qwen_3_4b-engineer-v4-fp8), so one zimage deploy path serves both.

A .gguf model downloaded fine but doesn't show in the Unet/loader dropdown

Cause: ComfyUI-GGUF's UnetLoaderGGUF reads a dedicated 'unet_gguf' folder key (fed from both unet and diffusion_models). models/unet is the reliably-covered location; the equivalent .safetensors goes in models/diffusion_models.

Fix: Route .gguf diffusion weights to models/unet (canonical, always covered). A GGUF in diffusion_models may not appear depending on ComfyUI/GGUF version — don't rely on it.

Subject renders with an unexpected color cast — e.g. a white marble statue goes green-bronze/metallic

Cause: A relight LoRA above strength 0 imposes a relighting color shift matching the driving scene instead of preserving source color (shipped default is 0.0; Kijai's enabled default is 1.0).

Fix: Zero the relight/style LoRA when you want source color preserved. A tinted/off-color output means suspect an auxiliary LoRA near strength 1.0.

A workflow that rendered perfectly once produces a BLACK VOID / garbage on a later redeploy — identical JSON, config, and GPU

Cause: Unpinned by default: models pull from floating HF /resolve/main (branch HEAD), custom nodes are git-clone-latest (no SHA), Docker tags are reused, and only ~95% file-size is validated, not content hash. An upstream re-upload or newer node code silently changed bytes/behavior behind unchanged names.

Fix: Pin weights to a commit/revision, nodes to a SHA, images to a digest. Verify a pinned HF URL with `curl -sI -r 0-0 -A 'Mozilla/5.0' -L '<url>'` — MUST include -L (HF /resolve/ 302-redirects to a CDN; without -L you see 302 not 206).

Hitting a specific error message? Browse ComfyUI error fixes.

Frequently Asked Questions

How much GPU memory is allocated for Flux Kontext?

The listed ModelPilot deployment uses a 24GB cloud GPU. Local memory needs can vary with precision, quantization, and workflow settings.

How much does it cost to run Flux Kontext?

Starting at $0.66/hr on a dedicated GPU. Charges are calculated from actual running time, with auto-stop when credits run out.

How long does Flux Kontext take to deploy?

Most deployments complete in 10–20 minutes including model download and environment setup.

Can I run Flux Kontext on my local GPU?

It depends on the selected variant, precision, quantization, and workflow settings. Compare the variants below with your available VRAM; the table shows ModelPilot's cloud GPU allocation, not a universal local minimum.

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