metadata

library_name: transformers
tags:
  - art
datasets:
  - gokaygokay/prompt_description_stable_diffusion_3k
language:
  - en
pipeline_tag: text2text-generation

Model Card

Fine tuned EleutherAI/pythia-410m using gokaygokay/prompt_description_stable_diffusion_3k dataset.

Direct Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "gokaygokay/phytia410m_desctoprompt"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Your description
test_description = """
View to a rustic terrace filled with pots with autumn flowers and a vine full of red leaves and bunches of grapes. 
in the foreground a wooden table with a copious breakfast, coffee, bowls, vases and plates with fruits, nuts, chestnuts, hazelnuts, breads and buns.
"""

prompt_template = """### Description:
{description}

### Prompt:
"""

text = prompt_template.format(description=test_description)

def inference(text, model, tokenizer, max_input_tokens=1000, max_output_tokens=200):
  # Tokenize
    input_ids = tokenizer.encode(
          text,
          return_tensors="pt",
          truncation=True,
          max_length=max_input_tokens
    )

    # Generate
    device = model.device
    generated_tokens_with_prompt = model.generate(
    input_ids=input_ids.to(device),
    max_length=max_output_tokens,
    )

    # Decode
    generated_text_with_prompt = tokenizer.batch_decode(generated_tokens_with_prompt, skip_special_tokens=True)

    # Strip the prompt
    generated_text_answer = generated_text_with_prompt[0][len(text):]

    return generated_text_answer


print("Description input (test):", test_description)

print("Finetuned model's prompt: ")
print(inference(test_description, model, tokenizer))