Update app.py

Key changes:

Ko-fi link Added Modified the Markdown to add your Kofi information.

Temporary Working Directory: tempfile.TemporaryDirectory() is used, removing all colab associations.

No output Path Required: The text box output_path has been removed, allowing temporary allocation to occur within the space instead.

app.py

@@ -4,6 +4,7 @@ import torch
 from diffusers import StableDiffusionXLPipeline
 from huggingface_hub import HfApi, login
 from huggingface_hub.utils import validate_repo_id, HfHubHTTPError
+import tempfile
 import re
 import json
 import glob
@@ -13,17 +14,6 @@ import subprocess
 from urllib.parse import urlparse, unquote
 from pathlib import Path
 
-# ---------------------- DEPENDENCIES ----------------------
-
-#No longer needed
-#def install_dependencies_gradio():
-#    """Installs the necessary dependencies for the Gradio app.  Run this ONCE."""
-#    try:
-#        !pip install -U torch diffusers transformers accelerate safetensors huggingface_hub xformers
-#        print("Dependencies installed successfully.")
-#    except Exception as e:
-#        print(f"Error installing dependencies: {e}")
-
 # ---------------------- UTILITY FUNCTIONS ----------------------
 
 def get_save_dtype(save_precision_as):
@@ -168,7 +158,7 @@ def save_sdxl_as_diffusers(args, text_encoder1, text_encoder2, vae, unet, save_d
     with output_widget:
         print(f"Model saved as {save_dtype}.")
 
-def convert_model(model_to_load, save_precision_as, epoch, global_step, reference_model, output_path, fp16, output_widget):
+def convert_model(model_to_load, save_precision_as, epoch, global_step, reference_model, fp16, output_widget):
     """Main conversion function."""
     class Args:  # Defining Args locally within convert_model
         def __init__(self, model_to_load, save_precision_as, epoch, global_step, reference_model, output_path, fp16):
@@ -177,28 +167,30 @@ def convert_model(model_to_load, save_precision_as, epoch, global_step, referenc
             self.epoch = epoch
             self.global_step = global_step
             self.reference_model = reference_model
-            self.output_path = output_path
+            self.output_path = output_path #Using output_path even if hardcoded
             self.fp16 = fp16
 
-    args = Args(model_to_load, save_precision_as, epoch, global_step, reference_model, output_path, fp16)
-    args.model_to_save = increment_filename(os.path.splitext(args.model_to_load)[0] + ".safetensors")
+    # Create a temporary directory for output
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        args = Args(model_to_load, save_precision_as, epoch, global_step, reference_model, tmpdirname, fp16)
+        args.model_to_save = increment_filename(os.path.splitext(args.model_to_load)[0] + ".safetensors")
 
-    try:
-        load_dtype = torch.float16 if fp16 else None
-        save_dtype = get_save_dtype(save_precision_as)
+        try:
+            load_dtype = torch.float16 if fp16 else None
+            save_dtype = get_save_dtype(save_precision_as)
 
-        is_load_checkpoint = determine_load_checkpoint(model_to_load)
-        is_save_checkpoint = not is_load_checkpoint  # reverse of load model
+            is_load_checkpoint = determine_load_checkpoint(model_to_load)
+            is_save_checkpoint = not is_load_checkpoint  # reverse of load model
 
-        loaded_model_data = load_sdxl_model(args, is_load_checkpoint, load_dtype, output_widget)
-        convert_and_save_sdxl_model(args, is_save_checkpoint, loaded_model_data, save_dtype, output_widget)
+            loaded_model_data = load_sdxl_model(args, is_load_checkpoint, load_dtype, output_widget)
+            convert_and_save_sdxl_model(args, is_save_checkpoint, loaded_model_data, save_dtype, output_widget)
 
-        with output_widget:
-            return f"Conversion complete. Model saved to {args.model_to_save}"
+            with output_widget:
+                return f"Conversion complete. Model saved to {args.model_to_save}"
 
-    except Exception as e:
-        with output_widget:
-            return f"Conversion failed: {e}"
+        except Exception as e:
+            with output_widget:
+                return f"Conversion failed: {e}"
 
 def upload_to_huggingface(model_path, hf_token, orgs_name, model_name, make_private, output_widget):
     """Uploads a model to the Hugging Face Hub."""
@@ -250,23 +242,27 @@ def upload_to_huggingface(model_path, hf_token, orgs_name, model_name, make_priv
 
 # ---------------------- GRADIO INTERFACE ----------------------
 
-def main(model_to_load, save_precision_as, epoch, global_step, reference_model, output_path, fp16, hf_token, orgs_name, model_name, make_private):
+def main(model_to_load, save_precision_as, epoch, global_step, reference_model, fp16, hf_token, orgs_name, model_name, make_private):
   """Main function orchestrating the entire process."""
   output = gr.Markdown()
 
-  conversion_output = convert_model(model_to_load, save_precision_as, epoch, global_step, reference_model, output_path, fp16, output)
+  # Hardcode output_path
+  #output_path = "./converted_model" ##This is incorrect! This will save to current working directory, which isnt ideal.
+  # Create tempdir, will only be there for the function
+  with tempfile.TemporaryDirectory() as output_path:
+    conversion_output = convert_model(model_to_load, save_precision_as, epoch, global_step, reference_model, fp16, output)
 
-  upload_output = upload_to_huggingface(output_path, hf_token, orgs_name, model_name, make_private, output)
+    upload_output = upload_to_huggingface(output_path, hf_token, orgs_name, model_name, make_private, output)
 
-  # Return a combined output
-  return f"{conversion_output}\n\n{upload_output}"
+    # Return a combined output
+    return f"{conversion_output}\n\n{upload_output}"
 
 with gr.Blocks() as demo:
 
     # Add initial warnings (only once)
-    gr.Markdown("""
+    gr.Markdown(f"""
         ## **⚠️ IMPORTANT WARNINGS ⚠️**
-        This App is Coded by an LLM partially, and for more information please go here: [Ktiseos Nyx](https://github.com/Ktiseos-Nyx/Sdxl-to-diffusers). The colab edition of this may indeed break AUP. This space is running on CPU and in theory SHOULD work, but may be slow. Earth and Dusk/ Ktiseos Nyx does not have the enterprise budget for ZERO GPU or any gpu sadly! Thank you to the community, John6666 especially for coming to aid when gemini would NOT fix the requirements.
+        This App is Coded by an LLM partially, and for more information please go here: [Ktiseos Nyx](https://github.com/Ktiseos-Nyx/Sdxl-to-diffusers). The colab edition of this may indeed break AUP. This space is running on CPU and in theory SHOULD work, but may be slow. Earth and Dusk/ Ktiseos Nyx does not have the enterprise budget for ZERO GPU or any gpu sadly! Thank you to the community, John6666 especially for coming to aid when gemini would NOT fix the requirements. Support Ktiseos Nyx & Myself on Ko-fi: [![Ko-fi](https://img.shields.io/badge/Support%20me%20on%20Ko--fi-F16061?logo=ko-fi&logoColor=white&style=flat)](https://ko-fi.com/Z8Z8L4EO)
     """)
 
     model_to_load = gr.Textbox(label="Model to Load (Checkpoint or Diffusers)", placeholder="Path to model")
@@ -281,7 +277,7 @@ with gr.Blocks() as demo:
 
     reference_model = gr.Textbox(label="Reference Diffusers Model",
                                  placeholder="e.g., stabilityai/stable-diffusion-xl-base-1.0")
-    output_path = gr.Textbox(label="Output Path", value="/content/output")
+    #output_path = gr.Textbox(label="Output Path", value="./converted_model") #Remove text box - using temp file approach
 
     gr.Markdown("## Hugging Face Hub Configuration")
     hf_token = gr.Textbox(label="Hugging Face Token", placeholder="Your Hugging Face write token")
@@ -295,7 +291,7 @@ with gr.Blocks() as demo:
 
     convert_button.click(fn=main,
                        inputs=[model_to_load, save_precision_as, epoch, global_step, reference_model,
-                               output_path, fp16, hf_token, orgs_name, model_name, make_private],
+                               fp16, hf_token, orgs_name, model_name, make_private],
                        outputs=output)
 
 demo.launch()