Update app.py

app.py

@@ -12,8 +12,8 @@ def convert_and_upload(token, source_repo, target_repo, precision):
     if not token:
         yield "❌ Error: Please provide a valid Hugging Face Write Token."
         return
-    if not target_repo.strip():
-        yield "❌ Error: Please specify a Target Repository."
+    if not target_repo.strip() or "your-username" in target_repo:
+        yield "❌ Error: Please specify a valid Target Repository (e.g., your-username/repo-name)."
         return
 
     # Map precision string to PyTorch dtype
@@ -48,7 +48,7 @@ def convert_and_upload(token, source_repo, target_repo, precision):
         yield f"⏳ Processing {file}..."
 
         try:
-            # Download file locally, bypassing symlink cache to save space
+            # Download file locally, bypassing symlink cache to save disk space
             local_path = hf_hub_download(
                 repo_id=source_repo,
                 filename=file,
@@ -72,7 +72,7 @@ def convert_and_upload(token, source_repo, target_repo, precision):
                 converted_path = os.path.join(TEMP_DIR, "converted.safetensors")
                 save_file(tensors, converted_path)
 
-                # Wipe tensors from RAM
+                # Wipe tensors from RAM to prevent OOM
                 del tensors
                 gc.collect()
 
@@ -110,20 +110,31 @@ def convert_and_upload(token, source_repo, target_repo, precision):
     yield f"✅ All files processed and successfully uploaded to {target_repo}!"
 
 # Dynamic UI Update for Target Repo Name
-def update_target_repo(source, precision):
+def update_target_repo(username, source, precision):
+    user_prefix = username.strip() if username.strip() else "your-username"
     model_name = "Z-Image-Turbo" if "Turbo" in source else "Z-Image-Base"
-    return f"rootlocalghost/{model_name}-{precision}"
+    return f"{user_prefix}/{model_name}-{precision}"
 
 # Build the Gradio UI
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("# 🚀 Z-Image Quantizer & Uploader")
     gr.Markdown(
-        "Select your source model and desired precision. The tool will sequentially download, quantize the "
-        "**text_encoder** and **transformer** files, and upload everything to your target repository while keeping memory usage under 16GB."
+        "Convert the **Z-Image** or **Z-Image-Turbo** models to lower precisions (FP8, FP16, BF16) and push them directly to your own Hugging Face account.\n\n"
+        "**How it works:** This tool sequentially downloads, quantizes the **text_encoder** and **transformer** files, and uploads everything. "
+        "It is designed to run safely on free Spaces (16GB RAM) by processing files one at a time."
     )
 
     with gr.Row():
         with gr.Column(scale=2):
+            hf_token = gr.Textbox(
+                label="Hugging Face Token (Write Access Required)", 
+                type="password",
+                placeholder="hf_..."
+            )
+            hf_username = gr.Textbox(
+                label="Your Hugging Face Username", 
+                placeholder="e.g., rootlocalghost"
+            )
             source_repo = gr.Dropdown(
                 choices=["Tongyi-MAI/Z-Image", "Tongyi-MAI/Z-Image-Turbo"], 
                 value="Tongyi-MAI/Z-Image-Turbo", 
@@ -135,13 +146,9 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
                 label="Quantization Precision"
             )
             target_repo = gr.Textbox(
-                label="Target Repository", 
-                value="rootlocalghost/Z-Image-Turbo-FP8"
-            )
-            hf_token = gr.Textbox(
-                label="Hugging Face Token (Write Access)", 
-                type="password",
-                placeholder="hf_..."
+                label="Target Repository (Auto-generated)", 
+                value="your-username/Z-Image-Turbo-FP8",
+                interactive=True
             )
             start_btn = gr.Button("Start Quantization & Upload", variant="primary")
         
@@ -154,8 +161,13 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             )
 
     # Automatically update the target repo name when inputs change
-    source_repo.change(fn=update_target_repo, inputs=[source_repo, precision], outputs=[target_repo])
-    precision.change(fn=update_target_repo, inputs=[source_repo, precision], outputs=[target_repo])
+    inputs_to_watch = [hf_username, source_repo, precision]
+    for inp in inputs_to_watch:
+        inp.change(
+            fn=update_target_repo, 
+            inputs=inputs_to_watch, 
+            outputs=[target_repo]
+        )
 
     start_btn.click(
         fn=convert_and_upload,