Trained for 2 epochs and 10000 steps.

Trained with datasets ['text-embed-cache', 'mnmlsmo_architecture_photography_style-512', 'mnmlsmo_architecture_photography_style-768', 'mnmlsmo_architecture_photography_style-1024', 'mnmlsmo_art_photography_style-512', 'mnmlsmo_art_photography_style-768', 'mnmlsmo_art_photography_style-1024', 'mnmlsmo_furniture_photography_style-512', 'mnmlsmo_furniture_photography_style-768', 'mnmlsmo_furniture_photography_style-1024', 'mnmlsmo_homewares_photography_style-512', 'mnmlsmo_homewares_photography_style-768', 'mnmlsmo_homewares_photography_style-1024', 'mnmlsmo_interiors_photography_style-512', 'mnmlsmo_interiors_photography_style-768', 'mnmlsmo_interiors_photography_style-1024', 'mnmlsmo_lighting_photography_style-512', 'mnmlsmo_lighting_photography_style-768', 'mnmlsmo_lighting_photography_style-1024', 'mnmlsmo_moods_photography_style-512', 'mnmlsmo_moods_photography_style-768', 'mnmlsmo_moods_photography_style-1024', 'mnmlsmo_technology_photography_style-512', 'mnmlsmo_technology_photography_style-768', 'mnmlsmo_technology_photography_style-1024']
Learning rate 0.0001, batch size 2, and 1 gradient accumulation steps.
Used DDPM noise scheduler for training with epsilon prediction type and rescaled_betas_zero_snr=False
Using 'trailing' timestep spacing.
Base model: FLUX.1-dev
VAE: None

pytorch_lora_weights.safetensors

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