distilgpt2-magicprompt-SD

Generate/augment your prompt, stable diffusion style.

This model is a fine-tuned version of distilgpt2 on the Gustavosta/Stable-Diffusion-Prompts dataset. It achieves the following results on the evaluation set:

• Loss: 1.3089
• eval_steps_per_second = 17.201
• perplexity = 3.7022

example

Results in (DALL-E, but you get the idea):

this distilgpt2 version is probably small/fast enough to be used locally on CPU!

basic usage

install transformers as needed:

pip install -U transformers

load and query through a pipeline object:

from transformers import pipeline

model_tag = "pszemraj/distilgpt2-magicprompt-SD"
generator = pipeline(
    "text-generation",
    model=model_tag,
)

prompt = "The Answer to Why"
result = generator(
    prompt,
    max_new_tokens=24,
)  # generate, adjust/add kwargs as needed
print(result[0]["generated_text"])

Training and evaluation data

refer to the Gustavosta/Stable-Diffusion-Prompts dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.001
• train_batch_size: 16
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 8
• total_train_batch_size: 256
• total_eval_batch_size: 4
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 10.0

Training results

Framework versions

• Transformers 4.25.0.dev0
• Pytorch 1.13.0+cu117
• Datasets 2.6.1
• Tokenizers 0.13.1