---
license: bigscience-bloom-rail-1.0
tags:
- generated_from_trainer
- stable-diffusion
- diffusion
model-index:
- name: bloom-560m-finetuned-sd-prompts
  results: []
  
datasets:
 - Gustavosta/Stable-Diffusion-Prompts
 
widget:
- text: "<s>Prompt: young, curly haired, redhead Natalie Portman as a"
- text: "<s>Prompt: a powerful energy woman, by alexander fedosav"

inference:
  parameters:
    eos_token_id: 2
    max_length: 128
    
  
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bloom-560m-finetuned-sd-prompts

This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the [Gustavosta/Stable-Diffusion-Prompts](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8742


## Example of usage

```py
import torch
from transformers import BloomTokenizerFast, BloomForCausalLM

device = 'cuda' if torch.cuda.is_available() else 'cpu'
ckpt = 'mrm8488/bloom-560m-finetuned-sd-prompts' 

tokenizer = BloomTokenizerFast.from_pretrained(ckpt)
model = BloomForCausalLM.from_pretrained(ckpt).to(device)

def generate_prompt(text):
    inputs = tokenizer(text, return_tensors='pt')
    input_ids = inputs.input_ids.to(device)
    attention_mask = inputs.attention_mask.to(device)
    output = model.generate(input_ids, attention_mask=attention_mask, repetition_penalty=1.05, max_length=2048, eos_token_id=tokenizer.eos_token_id)

    return tokenizer.decode(output[0], skip_special_tokens=False)
    
text = "<s>Prompt: pikachu dinning in the eiffel tower"

generate_prompt(text)

# Output: <s>Prompt: pikachu dinning in the eiffel tower, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha</s>
```

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6743        | 0.17  | 100  | 2.0891          |
| 1.8919        | 0.33  | 200  | 1.7191          |
| 1.5907        | 0.5   | 300  | 1.4454          |
| 1.3865        | 0.67  | 400  | 1.3247          |
| 1.2487        | 0.83  | 500  | 1.2150          |
| 1.1565        | 1.0   | 600  | 1.1031          |
| 0.896         | 1.17  | 700  | 1.0612          |
| 0.8389        | 1.33  | 800  | 0.9994          |
| 0.8071        | 1.5   | 900  | 0.9530          |
| 0.7628        | 1.67  | 1000 | 0.9206          |
| 0.7423        | 1.83  | 1100 | 0.8883          |
| 0.7155        | 2.0   | 1200 | 0.8742          |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1