Trained for 0 epochs and 1200 steps.

Trained with datasets ['text-embeds-sdxl-nofilter', 'training-test', 'dalle3', 'photo-concept-bucket']
Learning rate 4e-07, batch size 4, and 4 gradient accumulation steps.
Used DDPM noise scheduler for training with v_prediction prediction type and rescaled_betas_zero_snr=True
Using 'trailing' timestep spacing.
Base model: ptx0/terminus-xl-velocity-v2
VAE: madebyollin/sdxl-vae-fp16-fix

README.md

@@ -44,7 +44,7 @@ You may reuse the base model text encoder for inference.
 ## Training settings
 
 - Training epochs: 0
-- Training steps: 800
+- Training steps: 1200
 - Learning rate: 4e-07
 - Effective batch size: 16
   - Micro-batch size: 4
@@ -69,7 +69,7 @@ You may reuse the base model text encoder for inference.
 ### dalle3
 - Repeats: 1
 - Total number of images: 57744
-- Total number of aspect buckets: 2
+- Total number of aspect buckets: 37
 - Resolution: 1.0 megapixels
 - Cropped: False
 - Crop style: None

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random_states_0.pkl

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scheduler.bin

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training_state-photo-concept-bucket.json

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training_state-training-test.json

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training_state.json

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+{"global_step": 1200, "epoch_step": 375, "epoch": 1, "exhausted_backends": ["celebrities", "pixel-art", "movieposters", "moviecollection", "training-test"], "repeats": {"celebrities": 0, "pixel-art": 0, "movieposters": 0, "moviecollection": 0, "training-test": 0}}

unet/config.json

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 {
   "_class_name": "UNet2DConditionModel",
   "_diffusers_version": "0.29.0.dev0",
-  "_name_or_path": "/home/user/training/lite-models/checkpoint-400",
+  "_name_or_path": "/home/user/training/lite-models/checkpoint-1000",
   "act_fn": "silu",
   "addition_embed_type": "text_time",
   "addition_embed_type_num_heads": 64,

unet/diffusion_pytorch_model.safetensors

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