pipeline.py

from transformers import T5Tokenizer, T5ForConditionalGeneration
from diffusers import StableDiffusionPipeline
import torch

# Load models
t5_model = T5ForConditionalGeneration.from_pretrained('t5_model')
t5_tokenizer = T5Tokenizer.from_pretrained('t5_tokenizer')
ArtifyAI_model = StableDiffusionPipeline.from_pretrained('.', torch_dtype=torch.float16)
ArtifyAI_model = ArtifyAI_model.to('cuda')

# Combined pipeline
def t5_to_image_pipeline(input_text):
    # T5 model processing
    t5_inputs = t5_tokenizer.encode(input_text, return_tensors='pt', truncation=True)
    summary_ids = t5_model.generate(t5_inputs, max_length=50, num_beams=5, early_stopping=True)
    generated_text = t5_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    
    # Generate image from text using Stable Diffusion
    image = ArtifyAI_model(generated_text).images[0]
    return image