moved back to models

pipeline_loader.py

@@ -1,42 +0,0 @@
-from text_diffusion_pipeline import TextConditionalDDPMPipeline
-from latent_diffusion_pipeline import UnconditionalDDPMPipeline
-import os
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-
-
-def get_pipeline(model_path):
-    # If model_path is a local directory, use the original logic
-    if os.path.isdir(model_path):
-        #Diffusion models
-        if os.path.exists(os.path.join(model_path, "unet")):
-            if os.path.exists(os.path.join(model_path, "text_encoder")):
-                #If it has a text encoder and a unet, it's text conditional diffusion
-                pipe = TextConditionalDDPMPipeline.from_pretrained(model_path)
-            else:
-                #If it has no text encoder, use the unconditional diffusion model
-                pipe = UnconditionalDDPMPipeline.from_pretrained(model_path)
-    else:
-        # Assume it's a Hugging Face Hub model ID
-        # Try to load config to determine if it's text-conditional
-        try:
-            config, _ = DiffusionPipeline.load_config(model_path)
-            has_text_encoder = "text_encoder" in config
-        except Exception:
-            print(f"Warning: Could not load config: {e}")
-            has_text_encoder = False
-        if has_text_encoder:
-            # Use the local pipeline file for custom_pipeline
-            pipe = DiffusionPipeline.from_pretrained(
-                model_path,
-                custom_pipeline="models.text_diffusion_pipeline.TextConditionalDDPMPipeline",
-                trust_remote_code=True,
-            )
-        else:
-            # Fallback: try unconditional 
-            pipe = DiffusionPipeline.from_pretrained(
-                model_path,
-                custom_pipeline="models.latent_diffusion_pipeline.UnconditionalDDPMPipeline",
-                trust_remote_code=True,
-            )
-
-    return pipe