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--- |
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license: creativeml-openrail-m |
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tags: |
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- stable-diffusion |
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- prompt-generator |
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- distilgpt2 |
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datasets: |
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- FredZhang7/krea-ai-prompts |
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- Gustavosta/Stable-Diffusion-Prompts |
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- bartman081523/stable-diffusion-discord-prompts |
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--- |
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# DistilGPT2 Stable Diffusion Model Card |
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DistilGPT2 Stable Diffusion is a text-to-text model used to generate creative and coherent prompts for text-to-image models, given any text. |
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This model was finetuned on 2.03 million descriptive stable diffusion prompts from [Stable Diffusion discord](https://huggingface.co/datasets/bartman081523/stable-diffusion-discord-prompts), [Lexica.art](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts), and (my hand-picked) [Krea.ai](https://huggingface.co/datasets/FredZhang7/krea-ai-prompts). I filtered the hand-picked prompts based on the output results from Stable Diffusion v1.4. |
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Compared to other prompt generation models using GPT2, this one runs with 50% faster forwardpropagation and 40% less disk space & RAM. |
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### PyTorch |
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```bash |
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pip install --upgrade transformers |
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``` |
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```python |
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from transformers import GPT2Tokenizer, GPT2LMHeadModel |
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# load the pretrained tokenizer |
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tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2') |
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tokenizer.add_special_tokens({'pad_token': '[PAD]'}) |
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tokenizer.max_len = 512 |
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# load the fine-tuned model |
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model = GPT2LMHeadModel.from_pretrained('FredZhang7/distilgpt2-stable-diffusion') |
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# generate text using fine-tuned model |
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from transformers import pipeline |
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nlp = pipeline('text-generation', model=model, tokenizer=tokenizer) |
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ins = "a beautiful city" |
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# generate 10 samples |
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outs = nlp(ins, max_length=80, num_return_sequences=10) |
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# print the 10 samples |
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for i in range(len(outs)): |
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outs[i] = str(outs[i]['generated_text']).replace(' ', '') |
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print('\033[96m' + ins + '\033[0m') |
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print('\033[93m' + '\n\n'.join(outs) + '\033[0m') |
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``` |
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Example Output: |
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