Trained for 0 epochs and 10000 steps.

Trained with datasets ['text-embeds-pixart-filter', 'photo-concept-bucket', 'text-1mp', 'ideogram', 'midjourney-v6-520k-raw', 'sfwbooru', 'nijijourney-v6-520k-raw', 'dalle3']
Learning rate 1e-06, batch size 24, 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: terminusresearch/pixart-900m-1024-ft-v0.6
VAE: madebyollin/sdxl-vae-fp16-fix

README.md

@@ -1562,7 +1562,7 @@ You may reuse the base model text encoder for inference.
 ## Training settings
 
 - Training epochs: 0
-- Training steps: 9000
+- Training steps: 10000
 - Learning rate: 1e-06
 - Effective batch size: 192
  - Micro-batch size: 24
@@ -1585,6 +1585,14 @@ You may reuse the base model text encoder for inference.
 - Cropped: True
 - Crop style: random
 - Crop aspect: square
+### text-1mp
+- Repeats: 15
+- Total number of images: ~13056
+- Total number of aspect buckets: 1
+- Resolution: 1.0 megapixels
+- Cropped: True
+- Crop style: random
+- Crop aspect: square
 ### ideogram
 - Repeats: 15
 - Total number of images: ~36096

optimizer.bin

@@ -1,3 +1,3 @@
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random_states_0.pkl

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+size 16100

scheduler.bin

@@ -1,3 +1,3 @@
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+oid sha256:ec3386c1a1e3165e95e087cc5fed219f9a936cfdaf927dc9d05a2255eb24e135
 size 1000

training_state-dalle3.json

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-size 11534817
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+size 11809752

training_state.json

@@ -1 +1 @@
-{"global_step": 9000, "epoch_step": 9000, "epoch": 1, "exhausted_backends": ["sfwbooru"], "repeats": {"ideogram": 7, "sfwbooru": 0}}
+{"global_step": 10000, "epoch_step": 10000, "epoch": 1, "exhausted_backends": ["sfwbooru"], "repeats": {"ideogram": 8, "sfwbooru": 0, "text-1mp": 2}}

transformer/config.json

@@ -1,7 +1,7 @@
 {
  "_class_name": "PixArtTransformer2DModel",
  "_diffusers_version": "0.30.0.dev0",
- "_name_or_path": "/home/ubuntu/training/models/checkpoint-1000",
+ "_name_or_path": "/home/ubuntu/training/models/checkpoint-9000",
  "activation_fn": "gelu-approximate",
  "attention_bias": true,
  "attention_head_dim": 72,

transformer/diffusion_pytorch_model.safetensors

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