readme: ---
  license: other
  tags:
  - generated_from_trainer
  - stable diffusion
  - diffusion
  - text2image
  - prompt augment
  - prompt engineering
  datasets:
  - Gustavosta/Stable-Diffusion-Prompts
  model-index:
  - name: opt-350m-magicprompt-SD
    results: []
  widget:
   - text: "morning sun over Jakarta"
     example_title: "morning sun"
   - text: "WARNING: pip is"
     example_title: "pip"
   - text: "sentient cheese"
     example_title: "sentient cheese"
   - text: "cheeps are"
     example_title: "cheeps"
   - text: "avocado armchair"
     example_title: "creative prompt"
   - text: "Landscape of"
     example_title: "landscape"
  parameters:
    min_length: 16
    max_length: 96
    no_repeat_ngram_size: 1
    do_sample: True
  ---
  
  
  # opt-350m-magicprompt-SD
  
  Generate/augment your prompt, stable diffusion style.
  
  This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/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
  
  ![jakarta](https://i.imgur.com/TP3HQOA.png)
  
  output (_on DALL-E 2, but as words are words, works anywhere_)
  
  ![dalle2-jakarta](https://i.ibb.co/BKVxwmJ/DALL-E-2022-11-09-12-37-56-morning-sun-over-Jakarta-by-Simon-St-lenhag-and-Gaston-Bussiere-Matte-pai.png)
  
  ## 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
  