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Release Sun, Aug 16, 2026 8 min read

InvokeAI 6.14.0

Original release notes

This is a big release that adds many new user visible features including:

  • Video generation support via Wan 2.2.
  • Krea.2-Turbo and Raw model support
  • Flux.2 Dev support
  • Ernie Turbo model support
  • Ideogram 4 support
  • Anima controlnets and inpainting
  • Flux.2 PiD support (super resolution up to 4K)
  • Multi-GPU support
  • Native Intel XPU support

Video Generation

You can now generate short videos using the Wan 2.2 video model. We support text-to-video, image-to-video (use a still image to initiate the video), and image-to-image-video (interpolate video between two images). Video generation is available through the workflow editor, and includes a series of template workflows that allow you to generate videos and concatenate them together into longer productions. See Video Generation to get started.

New models

We now provide support for Krea.2-Turbo, Krea.2-Raw, Ernie Image Turbo, and Ideogram 4.

These are pure text-to-image models (no image editing capabilities). and LoRA/ControlNet/IPAdapter support is being rolled out in stages. Not all LoRAs will currently load. Please report those that don't in Issues.

Current capabilities are:

| Model | Text-to-Image | Image-to-Image | Inpainting | Outpainting | Negative Prompting | Reference Images | Regional Guidance | LoRAs | ControlNets | |---|---|---|---|---|---|---|---|---|---| | Krea-2-Turbo | | | | | needs CFG > 1 (off-spec for Turbo) | | positive only | | | | Krea-2-Raw | | | | | (CFG > 1) | | positive only | | | | Ernie-Image-Turbo | | | | | (CFG > 1) | | | | | | Ideogram-4 | | | | | | | prompt + bbox only | | | | Anima | | | | | (CFG > 1) | | pos + neg | | | | Flux.2 Dev | | | | | (CFG > 1) | | pos + neg | | |

Notes

  • Krea-2-Turbo vs Krea-2-Raw - identical feature support; they share one graph builder. The difference is sampling: Turbo is the distilled checkpoint (~8 steps, guidance disabled, fixed mu), Raw is undistilled (~28 steps, CFG ~4.5, dynamic mu).
  • Krea-2 negative prompting - honored only when CFG > 1. The variant does not gate CFG, so the negative prompt is live on Turbo too, but Turbo is meant to run with guidance off; raising CFG to reach the negative prompt is off-spec and degrades output.
  • Krea-2 regional guidance - regional *positive* text only. Regional negative prompts, auto-negative, and regional reference images raise "unsupported" warnings on canvas. The denoise node supports masked negative conditioning in workflows; the canvas graph does not wire it.
  • Ernie-Image-Turbo - text-to-image only. The denoise node has no denoise_mask input, so masked modes are impossible and image-to-image is not offered; any non-txt2img mode is rejected at graph build. Global negative prompt and the built-in prompt enhancer are supported.
  • Ideogram-4 - text-to-image only; raster layers or inpaint masks with content block generation before enqueue. There is no negative prompt at any CFG: the sampler uses asymmetric CFG with a zeroed unconditional branch, so the denoise node has no negative conditioning input. Regional guidance contributes a positive prompt plus a bounding box to the structured JSON caption; regional negatives, auto-negative, and reference images are dropped with warnings.
  • Anima ControlNets - via kohya-ss's ControlNet-LLLite adapters (8-66 MB), available as one-click starter installs: sketch (mixed scribble/HED/lineart/grayscale), depth, scribble, lineart, and pose. Control layers work the same way as for other model families. Note that the depth/scribble/lineart/pose adapters were trained on the Preview3 build and are weaker on Anima Base 1.0 - the mixed-conditioning sketch adapter is the strongest general-purpose choice. Each LLLite model may be applied only once per generation. A separate LLLite Inpaint Adapter (Advanced settings) conditions the model on surrounding image content during inpainting/outpainting for cleaner seams.
  • Reference images - unsupported across all five. Anima is explicitly rejected in the canvas validators; the other four have no IP-adapter model config for their base and no IP-adapter wiring in their graph builders.

Multi-GPU Support

If you are lucky enough to have two or more GPUs installed in your system, you can configure InvokeAI to parallelize generation across the GPUs. Two or more queued generation jobs will execute simultaneously on the GPUs, and the preview screen will be split into tiles to show you the progress of each rendering job. This also works when multiple users are logged in: the system will split GPU time fairly among users in a round-robin fashion. If only one generation is queued, then its model's text encoder will be run on one GPU and the denoiser will run on another, thereby preventing the denoiser from evicting the encoder from VRAM and speeding up the rendering of subsequent images (credits to jacid23 for this concept).

Other Features

  • *Performance improvements:*
  • We have improved the VRAM consumption estimates for multiple models, which should reduce the number of OOMs.
  • VAEs can now be run on the CPU, reducing the amount of VRAM contention, similar to the text encoders. You can set this option by selecting the VAE in the Model Manager.
  • Improved support for LoRA sliders. We now support UNet-only sliders. In addition, you can now edit the sliders' min, max and default values from within the Model Manager.
  • *Canvas improvements:*
  • Pressure-sensitive brush opacity on tablets and touch screens.
  • Fine-grid hint added to move tool.
  • A new transparency lock for Gradients and Shapes.
  • Clip Strokes have been extended to the Bbox and Rect/Oval shapes.
  • *Workflow management:*
  • Graph execution has been optimized, speeding up complex workflows.
  • There is now a LoRA collection picker node which allows you to stack multiple LoRAs together without adding additional nodes.
  • You can now call one workflow from another, making it easier to create and maintain complex workflows.

And lots more! See below for a complete list of What's New in this release.

Installation and Upgrading

See Installation for various ways to install and run InvokeAI.

What's Changed Since 6.14 Release Candidate 1

  • Feat(canvas): Add temporary bbox move with C key hold by @DustyShoe in https://github.com/invoke-ai/InvokeAI/pull/9104
  • docs(canvas): add documentation about generation bounding box behavior by @DustyShoe in https://github.com/invoke-ai/InvokeAI/pull/9435
  • fix(backend): ignore ComfyUI model_sampling keys when loading Anima checkpoints by @kappacommit in https://github.com/invoke-ai/InvokeAI/pull/9404
  • Serialize imports behind startup restoration by @JPPhoto in https://github.com/invoke-ai/InvokeAI/pull/9433
  • ui: translations update from weblate by @weblate in https://github.com/invoke-ai/InvokeAI/pull/9445
  • tests: relax 5s pytest-timeout marks that flake on slow CI runners by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9448
  • feat(multi-gpu): mark the remaining text encoders as idle-GPU offloadable by @Pfannkuchensack in https://github.com/invoke-ai/InvokeAI/pull/9428
  • Feature: HiDiffusion integration by @DustyShoe in https://github.com/invoke-ai/InvokeAI/pull/8787
  • fix: don't hold VRAM in an idle server (CUDA context at startup) by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9447
  • feat(ui): raise dimension slider max to 2048 by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9446
  • chore(docs): Update sponsor list by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9455
  • Fix HiDiffusion with SDXL ControlNet by @DustyShoe in https://github.com/invoke-ai/InvokeAI/pull/9454
  • ci: check aarch64 dependency resolution in uv-lock-checks by @stellarfeline in https://github.com/invoke-ai/InvokeAI/pull/9357
  • ci: keep pins.json in sync with pyproject.toml by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9351
  • docs: fix $Home -> ~ in Linux/macOS manual install command by @latent-9 in https://github.com/invoke-ai/InvokeAI/pull/9425
  • docs: fix typo 'teh' to 'the' in controlLayers/README.md by @lunar-me in https://github.com/invoke-ai/InvokeAI/pull/9471
  • docs: fix typo 'outut' to 'output' in api/README.md by @lunar-me in https://github.com/invoke-ai/InvokeAI/pull/9472
  • docs: fix spelling 'approachs' to 'approaches' in gallery/README.md by @lunar-me in https://github.com/invoke-ai/InvokeAI/pull/9473
  • perf(db): streamline gallery membership queries by @dexhunter in https://github.com/invoke-ai/InvokeAI/pull/9385
  • ci: attach provenance and SBOM attestations to the published container by @kobihikri in https://github.com/invoke-ai/InvokeAI/pull/9398
  • Feat: flux2 dev support by @Pfannkuchensack in https://github.com/invoke-ai/InvokeAI/pull/9234
  • fix(fp8): resolve compute dtype instead of reading model.dtype by @Pfannkuchensack in https://github.com/invoke-ai/InvokeAI/pull/9412
  • fix(model-loaders): stop materializing scaled-fp8 checkpoints in float32 by @Pfannkuchensack in https://github.com/invoke-ai/InvokeAI/pull/9429
  • fix(ui): seed Wan params fields in the v3->v4 persist migration (release-upgrade params wipe) by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9408
  • fix(download): finalize completed range resumes by @ShiroKSH in https://github.com/invoke-ai/InvokeAI/pull/9432
  • Declare networkx as a runtime dependency by @DustyShoe in https://github.com/invoke-ai/InvokeAI/pull/9457
  • ci(pins): make the python-classifier check advisory, not fatal by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9466
  • fix(api): run single-video delete/update off the event loop by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9392
  • fix(video): enforce one absolute deadline for streaming frame decode by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9393
  • perf(boards): batch the owner lookup in admin board listings by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9395
  • feat: Adding Support for SD.Next Quantization Engine (SDNQ) (Flux1&Flux2klein4B/9B&Z-Image) by @Pfannkuchensack in https://github.com/invoke-ai/InvokeAI/pull/9228
  • docs: expand the testing guide with frontend commands by @wunianze666-netizen in https://github.com/invoke-ai/InvokeAI/pull/9294
  • fix(session queue): count only rows actually transitioned by a bulk cancel by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9387
  • docs(config): disclose legacy device precedence in generation_devices auto copy by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9388
  • fix(model cache): release shared weights when a cache goes away by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9403
  • Add seeded Text LLM sampling by @JPPhoto in https://github.com/invoke-ai/InvokeAI/pull/9451
  • fix(download): remove the download_queue REST API; block non-public URLs for server-side downloads by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9492
  • [Fix]: generator node connectored to batch node by @dunkeroni in https://github.com/invoke-ai/InvokeAI/pull/9453
  • Add opt-in PiD memory optimizations by @JPPhoto in https://github.com/invoke-ai/InvokeAI/pull/9460
  • Fix image move recovery for unsupported thumbnail modes by @JPPhoto in https://github.com/invoke-ai/InvokeAI/pull/9497
  • Feat: pid followup by @Pfannkuchensack in https://github.com/invoke-ai/InvokeAI/pull/9474
  • Disable thinking in LLMs so prompt expansion will work properly with thinking models by @shanedk in https://github.com/invoke-ai/InvokeAI/pull/9380
  • Fix collector scoping and invocation validation by @JPPhoto in https://github.com/invoke-ai/InvokeAI/pull/9483
  • fix(canvas): restore eyedropper hotkey after tool changes by @DustyShoe in https://github.com/invoke-ai/InvokeAI/pull/9482
  • fix(wan): pair Wan 2.2 A14B GGUF experts by wiring, not just filename by @Pfannkuchensack in https://github.com/invoke-ai/InvokeAI/pull/9505
  • feat: add native Intel XPU (torch.xpu) device support by @LexiconCode in https://github.com/invoke-ai/InvokeAI/pull/9401
  • Support single-file Wan 2.2 checkpoints by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9503

New Contributors

  • @latent-9 made their first contribution in https://github.com/invoke-ai/InvokeAI/pull/9425
  • @lunar-me made their first contribution in https://github.com/invoke-ai/InvokeAI/pull/9471
  • @kobihikri made their first contribution in https://github.com/invoke-ai/InvokeAI/pull/9398
  • @ShiroKSH made their first contribution in https://github.com/invoke-ai/InvokeAI/pull/9432
  • @shanedk made their first contribution in https://github.com/invoke-ai/InvokeAI/pull/9380
  • @LexiconCode made their first contribution in https://github.com/invoke-ai/InvokeAI/pull/9401

What's Changed Since 6.13.7

  • chore(version): bump version by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9318
  • Add crash-recoverable image storage maintenance for moving existing images into the active image_subfolder_strategy by @JPPhoto in https://github.com/invoke-ai/InvokeAI/pull/9165
  • feat(lora): per LoRA-model configurable weight range by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9154
  • fix(qwen): estimate Qwen Image VAE working memory so the cache frees room before decode/encode by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9305
  • Add database migration discovery and graph-based precedence by @JPPhoto in https://github.com/invoke-ai/InvokeAI/pull/9319
  • Creation of Videos help document by @sarashinai in https://github.com/invoke-ai/InvokeAI/pull/6235
  • [feat] Round robin job scheduling in multiuser mode by @lstein in https://github.com/invoke-ai/InvokeAI/pull/9086

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