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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- stable diffusion |
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- diffusion |
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- text2image |
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- prompt augment |
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- prompt engineering |
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datasets: |
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- Gustavosta/Stable-Diffusion-Prompts |
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model-index: |
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- name: distilgpt2-magicprompt-SD |
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results: [] |
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thumbnail: https://i.ibb.co/WkmTnZD/image.png |
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widget: |
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- text: "morning sun over Jakarta" |
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example_title: "morning sun" |
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- text: "WARNING: pip is" |
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example_title: "pip" |
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- text: "sentient cheese" |
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example_title: "sentient cheese" |
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- text: "cheeps are" |
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example_title: "cheeps" |
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- text: "avocado armchair" |
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example_title: "creative prompt" |
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- text: "Landscape of" |
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example_title: "landscape" |
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parameters: |
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min_length: 16 |
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max_new_tokens: 24 |
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no_repeat_ngram_size: 1 |
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do_sample: True |
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--- |
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# distilgpt2-magicprompt-SD |
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[](https://colab.research.google.com/gist/pszemraj/bdddf9c3fe92d1ac2654730016d64c80/demo-distilgpt2-magicprompt.ipynb) |
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Generate/augment your prompt, stable diffusion style. |
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This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the Gustavosta/Stable-Diffusion-Prompts dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3089 |
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- eval_steps_per_second = 17.201 |
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- perplexity = 3.7022 |
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## example |
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Results in (_DALL-E, but you get the idea_): |
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<br> |
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this `distilgpt2` version is probably small/fast enough to be used locally on CPU! |
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## basic usage |
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install transformers as needed: |
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```bash |
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pip install -U transformers |
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``` |
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load and query through a `pipeline` object: |
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```python |
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from transformers import pipeline |
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model_tag = "pszemraj/distilgpt2-magicprompt-SD" |
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generator = pipeline( |
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"text-generation", |
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model=model_tag, |
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) |
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prompt = "The Answer to Why" |
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result = generator( |
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prompt, |
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max_new_tokens=24, |
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) # generate, adjust/add kwargs as needed |
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print(result[0]["generated_text"]) |
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``` |
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## Training and evaluation data |
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refer to the `Gustavosta/Stable-Diffusion-Prompts` dataset. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 16 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 256 |
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- total_eval_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 10.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 2.7061 | 0.99 | 33 | 2.5859 | |
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| 2.08 | 1.99 | 66 | 1.9965 | |
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| 1.7623 | 2.99 | 99 | 1.7248 | |
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| 1.5408 | 3.99 | 132 | 1.5449 | |
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| 1.4147 | 4.99 | 165 | 1.4437 | |
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| 1.3593 | 5.99 | 198 | 1.3768 | |
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| 1.2703 | 6.99 | 231 | 1.3362 | |
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| 1.2528 | 7.99 | 264 | 1.3175 | |
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| 1.1981 | 8.99 | 297 | 1.3091 | |
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| 1.2117 | 9.99 | 330 | 1.3089 | |
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### Framework versions |
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- Transformers 4.25.0.dev0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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