| 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('ArtifyAI_model', 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 | |