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Model: FredZhang7/distilgpt2-stable-diffusion
Source: Original Platform

README.md

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+---
+license: creativeml-openrail-m
+tags:
+- stable-diffusion
+- prompt-generator
+- distilgpt2
+datasets:
+- FredZhang7/krea-ai-prompts
+- Gustavosta/Stable-Diffusion-Prompts
+- bartman081523/stable-diffusion-discord-prompts
+widget:
+- text: "amazing"
+- text: "a photo of"
+- text: "a sci-fi"
+- text: "a portrait of"
+- text: "a person standing"
+- text: "a boy watching"
+---
+# DistilGPT2 Stable Diffusion Model Card
+
+
+<a href="https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion-v2"> <font size="4"> <bold> Version 2 is here! </bold> </font> </a>
+
+
+DistilGPT2 Stable Diffusion is a text generation model used to generate creative and coherent prompts for text-to-image models, given any text.
+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.
+
+Compared to other prompt generation models using GPT2, this one runs with 50% faster forwardpropagation and 40% less disk space & RAM.
+
+
+### PyTorch
+
+```bash
+pip install --upgrade transformers
+```
+
+```python
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+# load the pretrained tokenizer
+tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
+tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+tokenizer.max_len = 512
+
+# load the fine-tuned model
+model = GPT2LMHeadModel.from_pretrained('FredZhang7/distilgpt2-stable-diffusion')
+
+# generate text using fine-tuned model
+from transformers import pipeline
+nlp = pipeline('text-generation', model=model, tokenizer=tokenizer)
+ins = "a beautiful city"
+
+# generate 10 samples
+outs = nlp(ins, max_length=80, num_return_sequences=10)
+
+# print the 10 samples
+for i in range(len(outs)):
+    outs[i] = str(outs[i]['generated_text']).replace('  ', '')
+print('\033[96m' + ins + '\033[0m')
+print('\033[93m' + '\n\n'.join(outs) + '\033[0m')
+```
+
+Example Output:
+![Example Output](./prompt-examples.png)