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@@ -53,12 +53,12 @@ It's tailored for especially for Ideogram 4 image-reference-prompt datasets, wit
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  What we changed in AI-Toolkit besides the dataset editor:
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  We added reference-latent token concatenation for Ideogram 4: each clean reference image is VAE-encoded and appended to the packed sequence as
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- [text | noisy target | clean reference], with its own indicator, MRoPE time coordinate, and clean timestep. The transformer output and
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  diffusion loss are sliced to target tokens only, while bounding-box JSON prompts provide spatial edit conditioning.
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  These changes have to be mirrored in ComfyUI as well:
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- ComfyUI core: Extended the native Ideogram 4 model to accept reference latents and reproduce the training sequence [text | noisy output | clean reference],
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  including the separate indicator, MRoPE coordinate, clean timestep, and output-only prediction slicing.
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  Custom node: Ideogram4ReferenceConditioning resizes and VAE-encodes a reference image to match the target latent, then attaches it only to positive
 
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  What we changed in AI-Toolkit besides the dataset editor:
54
 
55
  We added reference-latent token concatenation for Ideogram 4: each clean reference image is VAE-encoded and appended to the packed sequence as
56
+ `[text | noisy target | clean reference]`, with its own indicator, MRoPE time coordinate, and clean timestep. The transformer output and
57
  diffusion loss are sliced to target tokens only, while bounding-box JSON prompts provide spatial edit conditioning.
58
 
59
  These changes have to be mirrored in ComfyUI as well:
60
 
61
+ ComfyUI core: Extended the native Ideogram 4 model to accept reference latents and reproduce the training sequence `[text | noisy output | clean reference]`,
62
  including the separate indicator, MRoPE coordinate, clean timestep, and output-only prediction slicing.
63
 
64
  Custom node: Ideogram4ReferenceConditioning resizes and VAE-encodes a reference image to match the target latent, then attaches it only to positive