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ComfyDeploy: How ComfyUI_VisualStylePrompting works in ComfyUI?

What is ComfyUI_VisualStylePrompting?

ComfyUI Version of '[a/Visual Style Prompting with Swapping Self-Attention](https://github.com/naver-ai/Visual-Style-Prompting)'

How to install it in ComfyDeploy?

Head over to the machine page

  1. Click on the "Create a new machine" button
  2. Select the Edit build steps
  3. Add a new step -> Custom Node
  4. Search for ComfyUI_VisualStylePrompting and select it
  5. Close the build step dialig and then click on the "Save" button to rebuild the machine

ComfyUI_VisualStylePrompting

ComfyUI Version of "Visual Style Prompting with Swapping Self-Attention"

image credits to @pamparamm

[!NOTE] This is WIP.

Major changes were made. Please make sure to update your workflows. An updated workflow can be found in the workflows directory.

Implements the very basics of Visual Style Prompting by Naver AI.

Getting Started

Clone the repository into your custom_nodes folder, and you'll see Apply Visual Style Prompting node. It should be placed between your sampler and inputs like the example image. This has currently only been tested with 1.5 based models.

  • reference_latent: VAE-encoded image you wish to reference,
  • positive: Positive conditioning describing output image.
  • reference_cond: Conditioning describing reference image.
  • enabled: Enables or disables the effect. Note that this node will still be hooked even after disabling unless you remove it.
  • denoise: Works the same way Img2Img works, but utilized with reference and / or init images (this is experimental).
  • input_blocks: Focuses attention on the encoder layers.
  • skip_input_layers: Number of layers in the input block that will not have swapping self-attention applied to them.
  • middle_block: Focuses attention on the middle layers.
  • skip_middle_layers: Number of layers in the middle block that will not have swapping self-attention applied to them.
  • output_blocks: Focuses attention on the decoder layers.
  • skip_output_layers: Number of layers in the output block that will not have swapping self-attention applied to them.

[!TIP] In order to get the best results, you must engineer both positive and reference_cond prompts correctly. Focus on the details you want to derive from the image reference, and the details you wish to see in the output.

The example workflow uses the following for the positive cond:

orange fox, origami, deep colors, shading, canon 60d.

And for the reference_cond:

origami figurine

Notes

  • Currently, this method utilized the VAE Encode & Inpaint method as it needs to iteralively denoise on each step. Due to how this method works, you'll always get two outputs. To remove the reference latent from the output, simple use a Batch Index Select node.

  • For legacy functionality, please pull this PR.