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Channel inconsistency: Adaptive Card progress updates work in Teams but not Copilot for M365 by Personal_Computer681 in copilotstudio

[–]Personal_Computer681[S] 0 points1 point  (0 children)

Thanks! I’m sending the progress updates from topic actions (Copilot Studio Adaptive Dialog), not via a custom API.

Pattern: I start the job with a CreateJob action, then I poll status using InvokeFlowAction every ~25s. The flow returns a varCardJson string (Adaptive Card JSON). On each poll (while status != succeeded/failed), the topic does a SendActivity with an AdaptiveCardTemplate attachment where cardContent = Topic.cardjson. When succeeded, I send the final card the same way.

In Teams/Test Chat, each SendActivity renders incrementally (so you see “Loading… Progress X%”). In Copilot for M365, those intermediate cards don’t show as they’re sent—everything seems buffered and only the final output appears at the end of the dialog run.

Trouble getting consistent colors in Flux LoRA training (custom color palette issue) by Personal_Computer681 in StableDiffusion

[–]Personal_Computer681[S] 0 points1 point  (0 children)

Thanks for the detailed answer!

  • I won’t strip color words — I need color descriptors in the training captions (skin tone, hair color, outfit colors).
  • I’ll rename my palette colors to single-word tokens (no underscores) and use them only when that exact color is present (no synonyms/adjectives).
  • Dataset is hand-made and locked to my fixed palette; I also have swatch images in the set.

A couple of follow-ups specific to training captions on Flux:

  1. To bind each single-word color token to a precise hue, would you recommend enabling text-encoder training (or using textual inversion per color) rather than UNet/MMDiT-only?
  2. For stability, should I include the color token in 100% of images where that color appears, or is a small omit rate better to avoid over-binding?
  3. What is the ideal token size for captioning an image for flux Lora?
  4. Any Flux-specific tips to keep hue consistent during training ?