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---
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
|