model_pipelines.py

import torch
from diffusers import StableDiffusionPipeline

device = "cuda" if torch.cuda.is_available() else "cpu"

def load_pipelines():
    model_ids = {
        "sd_v1_5": "runwayml/stable-diffusion-v1-5",
        "openjourney_v4": "prompthero/openjourney-v4",
        "realistic_vision": "SG161222/Realistic_Vision_V5.1"
    }
    pipes = {}
    for name, mid in model_ids.items():
        pipe = StableDiffusionPipeline.from_pretrained(
            mid,
            torch_dtype=torch.float16 if device == "cuda" else torch.float32
        )
        pipe = pipe.to(device)
        pipe.enable_attention_slicing()
        pipes[name] = pipe
    return pipes

def generate_all(pipes, prompt):
    results = {}
    for name, pipe in pipes.items():
        img = pipe(prompt, guidance_scale=7.5, num_inference_steps=30).images[0]
        results[name] = img
    return results