opt-350m-magicprompt-SD
Generate/augment your prompt, stable diffusion style.
This model is a fine-tuned version of facebook/opt-350m on the Gustavosta/Stable-Diffusion-Prompts dataset. It achieves the following results on the evaluation set:
- Loss: 1.2987
- eval_steps_per_second = 16.623
- perplexity = 3.6644
example
output (on DALL-E 2, but as words are words, works anywhere)
Training and evaluation data
refer to the Gustavosta/Stable-Diffusion-Prompts
dataset.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10.0
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
2.8568 | 0.95 | 16 | 2.5937 |
2.2487 | 1.95 | 32 | 2.1050 |
1.9011 | 2.95 | 48 | 1.8082 |
1.6837 | 3.95 | 64 | 1.6178 |
1.4887 | 4.95 | 80 | 1.4897 |
1.3812 | 5.95 | 96 | 1.4017 |
1.2944 | 6.95 | 112 | 1.3437 |
1.2574 | 7.95 | 128 | 1.3127 |
1.2325 | 8.95 | 144 | 1.3009 |
1.2223 | 9.95 | 160 | 1.2987 |
Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1
- Downloads last month
- 32
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for pszemraj/opt-350m-magicprompt-SD
Base model
facebook/opt-350m