We have completed the training of the first version. The training was conducted with a total pixel count of `1024*1024` at multi-scale. We trained for 30k steps using a batch size of 8*8.

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The training of union controlnet requires a significant amount of computational power. The current release is the first beta version checkpoint that maybe not been fully trained. The fully trainedbeta version is in the training process. We have conducted ablation studies that have demonstrated the validity of the code. The open-source release of the first beta version is solely to facilitate the rapid growth of the open-source community and the Flux ecosystem; it is common to encounter bad cases (please accept my apologies). It is worth noting that we have found that even a fully trained Union model may not perform as well as specialized models, such as pose control. However, as training progresses, the performance of the Union model will continue to approach that of specialized models.

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FLUX.1-dev-IP-Adapter

InstantX

This repository contains a IP-Adapter for FLUX.1-dev model released by researchers from InstantX Team, where image work just like text, so it may not be responsive or interfere with other text, but we do hope you enjoy this model, have fun and share your creative works with us on Twitter. This is a regular IP-Adapter, where the new layers are added into 38 single and 19 double blocks. We use google/siglip-so400m-patch14-384 to encode image for its superior performance, and adopt a simple MLPProjModel of 2 linear layers to project. The image token number is set to 128. The currently released model is trained on the 10M open source dataset with a batch size of 128 and 80K training steps. This model supports image reference, but is not for fine-grained style transfer or character consistency, which means that there exists a trade-off between content leakage and style transfer. We don't find similar properties in FLUX.1-dev (DiT-based) as in InstantStyle (UNet-based). It may take several attempts to get satisfied results. Furthermore, current released model may suffer from limited diversity, thus cannot cover some styles or concepts,

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InstantID

InstantX

InstantID is a new state-of-the-art tuning-free method to achieve ID-Preserving generation with only single image, supporting various downstream tasks. You can directly download the model in this repository. You also can download the model in python script: For face encoder, you need to manutally download via this URL to `models/antelopev2`.

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MiniMax-H3-Turbo-Lora-Diffusers

InstantX

Diffusers / PEFT conversion of `larryvrh/MiniMax-H3-Turbo-Lora`: a LoRA that lets MiniMax-H3 render joint **video + synchronized stereo audio** in about **4 sampling steps** instead of the usual ~20. > ⚠️ **Early prototype.** Same caveat as the upstream release: under-trained preview weights, not production quality. They already beat the base model at 4 steps (sharper detail, cleaner / better-synced audio), but treat this as a work-in-progress taste, not a finished product. Prefer the non-EMA `ckpt500` weights by default. Converted from the upstream Turbo LoRA (bf16, `W_eff = W + lora_B @ lora_A`, **alpha = rank** so scale is 1). QKV is split into `to_q` / `to_k` / `to_v`, and SwiGLU `fc1` halves are swapped to match Diffusers' `[value; gate]` layout.

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Qwen-Image-ControlNet-Inpainting

InstantX

This repository provides a ControlNet that supports mask-based image inpainting and outpainting for Qwen-Image. ComfyUI offers native support for Qwen-Image-ControlNet-Inpainting. The official workflow can be found here. Make sure your ComfyUI version is >=0.3.59. Liblib AI offers native support for Qwen-Image-ControlNet-Inpainting. Visit for online WebUI or ComfyUI inference.

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Qwen-Image-ControlNet-Union

InstantX

This repository provides a unified ControlNet that supports 4 common control types (canny, soft edge, depth, pose) for Qwen-Image. You can adjust control strength via controlnet_conditioning_scale. - Canny: use cv2.Canny, set controlnet_conditioning_scale in [0.8, 1.0] - Soft Edge: use AnylineDetector, set controlnet_conditioning_scale in [0.8, 1.0] - Depth: use depth-anything, set controlnet_conditioning_scale in [0.8, 1.0] - Pose: use DWPose, set controlnet_conditioning_scale in [0.8, 1.0] We strongly recommend using detailed prompts, especially when include text elements. For example, use "a poster with text 'InstantX Team' on the top" instead of "a poster".

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