Create model_pipelines.py

model_pipelines.py

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+import torch
+from diffusers import StableDiffusionPipeline
+
+def load_pipelines(device="cuda"):
+    model_ids = {
+        "sd_v1_5": "runwayml/stable-diffusion-v1-5",
+        "openjourney_v4": "prompthero/openjourney-v4",
+        "ldm_256": "CompVis/ldm-text2im-large-256"
+    }
+    pipes = {}
+    for name, mid in model_ids.items():
+        pipe = StableDiffusionPipeline.from_pretrained(mid, torch_dtype=torch.float16)
+        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