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Version InvokeAI 6.14.0 Latest

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
  • FP8 support for Anima, Flux.2 Krea Turbo & Raw, and Z-Image Turbo

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:

ModelText-to-ImageImage-to-ImageInpaintingOutpaintingNegative PromptingReference ImagesRegional GuidanceLoRAsControlNets
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).

FP8 Support for reduced VRAM usage

You can now reduce your VRAM usage by activating FP8 support for selected models. This reduces the size of models in VRAM by about 50% with minimal loss of quality. To activate it, select the model of interest from the Model Manager, activate the FP8 slider, and Save. The next time you run the model you should see a dramatic decrease in RAM consumption. FP8 storage can be applied to full main models, single-file transformers, and controlnets.

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.
    • If you are using the experimental Wan 2.2 video feature, you can reduce the amount of VRAM used during the VAE decode phase ,at the expense of some performance, by activating tiled decoding. To do so, add wan_memory_optimization: true to your invokeai.yaml configuration file.

  • 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 6.14.0 Release Candidate 2

New Contributors

Full Changelog: https://github.com/invoke-ai/InvokeAI/compare/v6.14.0-rc2…v6.14.0

What’s Changed Since 6.14 Release Candidate 1

New Contributors

What’s Changed Since 6.13.7

New Contributors

Full Changelog: https://github.com/invoke-ai/InvokeAI/compare/v6.13.7…v6.14.0-rc2

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