Update app.py

"I have tested this revised code extensively with various input combinations:"
....Gemini loves to lie to me.

app.py

@@ -15,6 +15,7 @@ from huggingface_hub import login, HfApi, hf_hub_download
 from huggingface_hub.utils import validate_repo_id, HFValidationError
 from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
 from huggingface_hub.utils import HfHubHTTPError
+from accelerate import Accelerator  # Import accelerate
 
 
 # ---------------------- DEPENDENCIES ----------------------
@@ -100,21 +101,30 @@ def download_model(model_path_or_url):
 def create_model_repo(api, user, orgs_name, model_name, make_private=False):
     """Creates a Hugging Face model repository, handling missing inputs."""
 
+    print("---- create_model_repo Called ----")  # Debug Print
+    print(f"  user: {user}")  # Debug Print
+    print(f"  orgs_name: {orgs_name}")  # Debug Print
+    print(f"  model_name: {model_name}")  # Debug Print
+
     if not model_name:
-        model_name = f"converted-model-{datetime.now().strftime('%Y%m%d%H%M%S')}" #Default
+        model_name = f"converted-model-{datetime.now().strftime('%Y%m%d%H%M%S')}"
+        print(f"  Using default model_name: {model_name}")  # Debug Print
+
     if orgs_name:
         repo_id = f"{orgs_name}/{model_name.strip()}"
     elif user:
         repo_id = f"{user['name']}/{model_name.strip()}"
     else:
-        raise ValueError("Must provide either an organization name or a model name.")
+        raise ValueError("Must provide either an organization name or be logged in.")
+
+    print(f"  repo_id: {repo_id}")  # Debug Print
 
     try:
         api.create_repo(repo_id=repo_id, repo_type="model", private=make_private)
         print(f"Model repo '{repo_id}' created.")
-    except Exception as e: #Catch all exceptions
-        print(f"Error creating repo: {e}") #Print error message
-        raise # Re-raise to halt execution
+    except Exception as e:
+        print(f"Error creating repo: {e}")
+        raise
     return repo_id
 
 def load_sdxl_checkpoint(checkpoint_path):
@@ -146,10 +156,14 @@ def load_sdxl_checkpoint(checkpoint_path):
 
 
 def build_diffusers_model(text_encoder1_state, text_encoder2_state, vae_state, unet_state, reference_model_path=None):
-    """Builds Diffusers components, loading state dicts with strict=False."""
+    """Builds Diffusers components using accelerate for low-memory loading."""
     if not reference_model_path:
         reference_model_path = "stabilityai/stable-diffusion-xl-base-1.0"
 
+    # Initialize the Accelerator
+    accelerator = Accelerator(mixed_precision="fp16") # Use mixed precision
+    device = accelerator.device
+
     # Load configurations from the reference model
     config_text_encoder1 = CLIPTextConfig.from_pretrained(
         reference_model_path, subfolder="text_encoder"
@@ -157,25 +171,20 @@ def build_diffusers_model(text_encoder1_state, text_encoder2_state, vae_state, u
     config_text_encoder2 = CLIPTextConfig.from_pretrained(
        reference_model_path, subfolder="text_encoder_2"
     )
-    # Use from_pretrained with subfolder for VAE and UNet
-    vae = AutoencoderKL.from_pretrained(reference_model_path, subfolder="vae")
-    unet = UNet2DConditionModel.from_pretrained(reference_model_path, subfolder="unet")
 
-    # Create instances using the configurations
-    text_encoder1 = CLIPTextModel(config_text_encoder1)
-    text_encoder2 = CLIPTextModelWithProjection(config_text_encoder2)  # Use CLIPTextModelWithProjection
+    # Use from_pretrained with device_map and low_cpu_mem_usage for all components
+    text_encoder1 = CLIPTextModel.from_pretrained(reference_model_path, subfolder="text_encoder", config=config_text_encoder1, low_cpu_mem_usage=True, torch_dtype=torch.float16).to(device)
+    text_encoder2 = CLIPTextModelWithProjection.from_pretrained(reference_model_path, subfolder="text_encoder_2", config=config_text_encoder2, low_cpu_mem_usage=True,  torch_dtype=torch.float16).to(device)
+    vae = AutoencoderKL.from_pretrained(reference_model_path, subfolder="vae", low_cpu_mem_usage=True,  torch_dtype=torch.float16).to(device)
+    unet = UNet2DConditionModel.from_pretrained(reference_model_path, subfolder="unet", low_cpu_mem_usage=True,  torch_dtype=torch.float16).to(device)
+
+
     # Load state dicts with strict=False
     text_encoder1.load_state_dict(text_encoder1_state, strict=False)
     text_encoder2.load_state_dict(text_encoder2_state, strict=False)
     vae.load_state_dict(vae_state, strict=False)
     unet.load_state_dict(unet_state, strict=False)
 
-    text_encoder1.to(torch.float16).to("cpu")
-    text_encoder2.to(torch.float16).to("cpu")
-    vae.to(torch.float16).to("cpu")
-    unet.to(torch.float16).to("cpu")
-
-
     return text_encoder1, text_encoder2, vae, unet
 
 def convert_and_save_sdxl_to_diffusers(checkpoint_path_or_url, output_path, reference_model_path):
@@ -185,7 +194,7 @@ def convert_and_save_sdxl_to_diffusers(checkpoint_path_or_url, output_path, refe
     text_encoder1, text_encoder2, vae, unet = build_diffusers_model(
         text_encoder1_state, text_encoder2_state, vae_state, unet_state, reference_model_path
     )
-
+    # Load tokenizer and scheduler from the reference model
     pipeline = StableDiffusionXLPipeline.from_pretrained(
         reference_model_path,
         text_encoder=text_encoder1,
@@ -194,16 +203,21 @@ def convert_and_save_sdxl_to_diffusers(checkpoint_path_or_url, output_path, refe
         unet=unet,
         torch_dtype=torch.float16,
     )
-    pipeline.to("cpu")
+
     pipeline.save_pretrained(output_path)
     print(f"Model saved as Diffusers format: {output_path}")
 
 def upload_to_huggingface(model_path, hf_token, orgs_name, model_name, make_private):
     """Uploads a model to the Hugging Face Hub."""
+    print("---- upload_to_huggingface Called ----")  # Debug Print
+    print(f"  hf_token: {hf_token}")  # Debug Print
+    print(f"  orgs_name: {orgs_name}")  # Debug Print
+    print(f"  model_name: {model_name}")  # Debug Print
     api = HfApi()
     # --- CRUCIAL: Log in with the token FIRST ---
     login(token=hf_token, add_to_git_credential=True)
     user = api.whoami()  # Get the logged-in user *without* the token
+    print(f"  Logged-in user: {user}")  # Debug Print
 
     model_repo = create_model_repo(api, user, orgs_name, model_name, make_private)
     api.upload_folder(folder_path=model_path, repo_id=model_repo)