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--- |
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library_name: transformers |
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tags: |
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- flux |
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- stable-diffusion |
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- prompt-enhancer |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- google/flan-t5-small |
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--- |
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# Model Details |
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The model has been finetuned to optimize the conversion of short prompts into detailed prompts, enabling stable diffusion or flux-based image generation. This refinement enables users to craft more specific and nuanced requests, resulting in higher-quality and more coherent images. |
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### Model Description |
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- **Developed by:** [Imran Ali] |
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- **Model type:** [T5 (Text-to-Text Transfer Transformer)] |
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- **Language(s) (NLP):** [English] |
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- **License:** [apache-2.0] |
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- **Finetuned from model:** [t5-small] |
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- **Demo:** [Demo Space](https://huggingface.co/spaces/imranali291/flux-prompt-enhancer) |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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from transformers import T5Tokenizer, T5ForConditionalGeneration |
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# Load the tokenizer and model |
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tokenizer = T5Tokenizer.from_pretrained("imranali291/flux-prompt-enhancer") |
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model = T5ForConditionalGeneration.from_pretrained("imranali291/flux-prompt-enhancer") |
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# Example input |
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input_text = "Futuristic cityscape at twilight descent." |
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# Tokenize input |
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids |
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# Generate output |
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output = model.generate(input_ids, max_length=128, eos_token_id=tokenizer.eos_token_id, do_sample=True, top_p=0.9, temperature=0.7, repetition_penalty=2.5) |
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print(tokenizer.decode(output[0], skip_special_tokens=True)) |
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``` |
