64 lines
2.1 KiB
Markdown
64 lines
2.1 KiB
Markdown
---
|
|
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:
|
|
 |