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User seeks optimal settings for training Ideogram and ZIT models

A user on Reddit is seeking advice on training image generation models, specifically Ideogram and ZIT, with a dataset of 30,000 high-quality, realistic photographic images. They are experimenting with various configurations, including different resolutions, learning rates, optimizers, batch sizes, and training methods like full fine-tune versus LoRA. The user reports struggling with convergence for ZIT and poor output quality for Ideogram, despite trying numerous settings. AI

IMPACT Guidance sought on optimizing training parameters for image generation models.

RANK_REASON User is asking for advice on training existing models, not announcing a new release or significant development.

Read on r/StableDiffusion →

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COVERAGE [1]

  1. r/StableDiffusion TIER_2 English(EN) · /u/Dry_Check8093 ·

    Training Ideogram or ZIT with 30,000 images Q

    <!-- SC_OFF --><div class="md"><p>I have 30,000 images all captioned with natural language and I have another version of captions that are all captioned in JSON format for Ideogram style captions.<br /> Can anyone shed any light on configurations that worked for them for a datase…