pszemraj_-_opt-350m-multiprompt-8bits

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opt-350m-multiprompt - bnb 8bits

• Model creator: https://huggingface.co/pszemraj/
• Original model: https://huggingface.co/pszemraj/opt-350m-multiprompt/

Original model description:

license: other tags:

• generated_from_trainer
• text generation
• stable diffusion
• midjourney
• text2image
• text to image
• prompt augment
• prompt engineering thumbnail: https://i.imgur.com/DeKNHtC.jpg datasets:
• pszemraj/text2image-multi-prompt 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

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pszemraj/opt-350m-multiprompt

<a href="https://colab.research.google.com/gist/pszemraj/bdd1238ee4b8330aeec6774a16f9a677/opt-350m-multiprompt-demo.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>

Generate/augment your prompt with a model trained on a large & diverse prompt dataset.

This model is a fine-tuned version of facebook/opt-350m on the pszemraj/text2image-prompts-multi dataset. It achieves the following results on the evaluation set:

• Loss: 1.6669
• eval steps per second: 16.21
• perplexity: 5.29

Example

<br>

The above example was created with DALL-E 2 but will of course work with any text2image model.

Intended uses & limitations

• The model will generate augmentations that are biased towards the training data, i.e. what people already asked for in the SD/midjourney discords, etc. Creating a larger dataset was an attempt at mitigating this through more data from different datasets.

Training and evaluation data

See the pszemraj/text2image-prompts-multi dataset card for details. The dataset is a compilation of several text-to-image prompt datasets on huggingface :)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 8
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 16
• total_train_batch_size: 256
• total_eval_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.04
• num_epochs: 4.0

Training results

Framework versions

• Transformers 4.25.0.dev0
• Pytorch 1.13.0+cu117
• Datasets 2.6.1
• Tokenizers 0.13.1