ComfyUI-DiffusersImageOutpaint

ComfyUI-DiffusersImageOutpaint
★ 91

图像扩展ComfyUI插件SDXL兼容ControlNet支持
基于 diffusers 的 ComfyUI outpainting 插件，提供一组节点（含模型加载等），可在本地用 SDXL/ControlNet 模型对图像进行向外扩展，支持显存优化与常见模型兼容。
💡 在ComfyUI中用本地SDXL/ControlNet模型对图片进行外扩生成。
🍴 5 Forks💻 Python🔄 2025-07-07
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https://pan.quark.cn/s/af9fbf81e746
📦 requirements.txt
torch
numpy==1.26.4
transformers==4.45.0
accelerate
diffusers==0.32.2
fastapi<0.113.0
opencv-python
image
📄 README

ComfyUI nodes for outpainting images with diffusers, based on diffusers-image-outpaint by fffiloni.

Now also catalogued on plugin.aix.ink! The site is mostly chinese, but it is convenient to consult this and many other extensions from a single, simple catalog. Other infos here. You can find this extension on the catalogue here.

Updates:

  • 15/05/2025: Fixed missing 'loaded_keys' error. More details below.
  • 17/11/2024:
  • Added more options to Pad Image node (resize image, custom resize image percentage, mask overlap percentage, overlap left/right/top/bottom).
  • Side notes:
  • Now images with round angles work, since the new editable mask covers them, like in the original huggingface space.
  • You can use “mask” and “diffusers outpaint cnet image” outputs to preview mask and image.
  • You can find in the same workflow file the workflow with the checkpoint-loader-simple node and another one with clip + vae loader nodes.
  • 22/10/2024:
  • Unet and Controlnet Models Loader using ComfYUI nodes canceled, since I can’t find a way to load them properly; more info at the end.
  • Guide to change model used.
  • 20/10/2024: No more need to download tokenizers nor text encoders! Now comfyui clip loader works, and you can use your clip models. You can also use the Checkpoint Loader Simple node, to skip the clip selection part.
  • 10/2024: You don’t need any more the diffusers vae, and can use the extension in low vram mode using sequential_cpu_offload (also thanks to zmwv823) that pushes the vram usage from *8,3 gb* down to _6 gb_.

    • Installation

  • Download this extension or git clone it in comfyui/custom_nodes, then (if comfyui-manager didn’t already install the requirements or you have missing modules), from comfyui virtual env write cd your/path/to/this/extension and pip install -r requirements.txt.
  • Download a sdxl model (example) in comfyui/models/diffusion_models;
  • Download a sdxl controlnet model (example) in comfyui/models/controlnet.

    • ⚠ Choosing model and controlnet: As of now, I only tried RealVisXL_V5.0_Lightning and controlnet-union-promax_sdxl. Mixing RealVisXL with controlnet-union (non promax version) gave error, so it could be that other models/controlnets give error as well, but I haven’t tried much combinations so I can’t tell.

    Some considerations

    Flux is still beyond me (even if I was quite there, I think). I haven’t tried integrating other model types, and after my flux failure I don’t think I’ll try adding other model types.

    Since for now only sdxl models work, the configs are hardcoded.

  • (Dual) Clip Loader node: if you use the Clip Loader instead of Checkpoint Loader Simple, and want to use an sdxl type model like RealVisXL_V5.0_Lightning, you can download clip_I and clip_g from here. You can use this workflow (change model.fp16 with clip_g).

    • Overview

  • Minimum VRAM: 6 gb with 1280×720 image, rtx 3060, RealVisXL_V5.0_Lightning, sdxl-vae-fp16-fix, controlnet-union-sdxl-promax using sequential_cpu_offload, otherwise 8,3 gb;
  • ~As seen in this issue, images with square corners are required~.

    • The extension gives 5 nodes:

  • Load Diffuser Model: a simple node to load diffusion models. You can download them from Huggingface (the extension doesn’t download them automatically). Put them inside the diffusion_models folder. It supports only unets; for whole checkpoints (unet+clip+vae) you can use the checkpoint loader simple by comfyanonimous;
  • Load Diffuser Controlnet: a simple node to load diffusion models. You can download them from Huggingface (the extension doesn’t download them automatically). Put them inside the controlnet folder;
  • Paid Image for Diffusers Outpaint: this node resizes the image based on the specified width and height, then resizes it again based on the resize_image percentage, and if possible it will put the mask based on the alignment specified, otherwise it will revert back to the default “middle” alignment;
  • Encode Diffusers Outpaint Prompt: self explanatory. Works as clip text encode (prompt), and specifies what to add to the image;
  • Diffusers Image Outpaint: This is the main node, that outpaints the image. Currently the generation process is based on fffiloni’s one, so you can’t reproduce a specific a specific outpaint, and the seed option you see is only used to update the UI and generate a new image. You can specify the amount of steps to generate the image.

    • You _can_ also pass image and mask to vae encode (for inpainting) node, then pass the latent to a sampler, but controlnets and ip-adapters won’t always give good results like with diffusers outpaint, and they require a different workflow, not covered by this extension.

    Missing ‘loaded_keys’ error

    Recent versions of transformers and diffusers broke somethings, you need to revert back, command with some working versions (found here) (do it inside your comfyui env): pip install transformers==4.45.0 --upgrade diffusers==0.32.2 --upgrade, or if you use the portable version, run this in ComfyUI_windows_portable -folder:

    python_embeded\python.exe -m pip install transformers==4.45.0 --upgrade diffusers==0.32.2 --upgrade.

    Credits

    diffusers-image-outpaint by fffiloni