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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+opt-350m-magicprompt-SD - AWQ
+- Model creator: https://huggingface.co/pszemraj/
+- Original model: https://huggingface.co/pszemraj/opt-350m-magicprompt-SD/
+
+
+
+
+Original model description:
+---
+license: other
+tags:
+- generated_from_trainer
+- stable diffusion
+- diffusion
+- text2image
+- prompt augment
+- prompt engineering
+datasets:
+- Gustavosta/Stable-Diffusion-Prompts
+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
+base_model: facebook/opt-350m
+model-index:
+- name: opt-350m-magicprompt-SD
+  results: []
+---
+
+
+# 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
+
+