Update app.py

app.py

@@ -10,10 +10,6 @@ import numpy as np
 import time
 import zipfile
 import os
-import requests
-from urllib.parse import urlparse
-import tempfile
-import shutil
 
 # Description for the app
 DESCRIPTION = """## Qwen Image Hpc/."""
@@ -48,45 +44,6 @@ aspect_ratios = {
     "3:4": (1140, 1472)
 }
 
-def load_lora_opt(pipe, lora_input):
-    lora_input = lora_input.strip()
-    if not lora_input:
-        return
-
-    # If it's just an ID like "author/model"
-    if "/" in lora_input and not lora_input.startswith("http"):
-        pipe.load_lora_weights(lora_input, adapter_name="default")
-        return
-
-    if lora_input.startswith("http"):
-        url = lora_input
-
-        # Repo page (no blob/resolve)
-        if "huggingface.co" in url and "/blob/" not in url and "/resolve/" not in url:
-            repo_id = urlparse(url).path.strip("/")
-            pipe.load_lora_weights(repo_id, adapter_name="default")
-            return
-
-        # Blob link → convert to resolve link
-        if "/blob/" in url:
-            url = url.replace("/blob/", "/resolve/")
-
-        # Download direct file
-        tmp_dir = tempfile.mkdtemp()
-        local_path = os.path.join(tmp_dir, os.path.basename(urlparse(url).path))
-
-        try:
-            print(f"Downloading LoRA from {url}...")
-            resp = requests.get(url, stream=True)
-            resp.raise_for_status()
-            with open(local_path, "wb") as f:
-                for chunk in resp.iter_content(chunk_size=8192):
-                    f.write(chunk)
-            print(f"Saved LoRA to {local_path}")
-            pipe.load_lora_weights(local_path, adapter_name="default")
-        finally:
-            shutil.rmtree(tmp_dir, ignore_errors=True)
-
 # Generation function for Qwen/Qwen-Image
 @spaces.GPU(duration=120)
 def generate_qwen(
@@ -100,8 +57,6 @@ def generate_qwen(
     num_inference_steps: int = 50,
     num_images: int = 1,
     zip_images: bool = False,
-    lora_input: str = "",
-    lora_scale: float = 1.0,
     progress=gr.Progress(track_tqdm=True),
 ):
     if randomize_seed:
@@ -109,21 +64,10 @@ def generate_qwen(
     generator = torch.Generator(device).manual_seed(seed)
     
     start_time = time.time()
-
-    current_adapters = pipe_qwen.get_list_adapters()
-    for adapter in current_adapters:
-        pipe_qwen.delete_adapters(adapter)
-    pipe_qwen.disable_lora()
-
-    use_lora = False
-    if lora_input and lora_input.strip() != "":
-        load_lora_opt(pipe_qwen, lora_input)
-        pipe_qwen.set_adapters(["default"], adapter_weights=[lora_scale])
-        use_lora = True
     
     images = pipe_qwen(
         prompt=prompt,
-        negative_prompt=negative_prompt if negative_prompt else "",
+        negative_prompt=negative_prompt if negative_prompt else None,
         height=height,
         width=width,
         guidance_scale=guidance_scale,
@@ -144,12 +88,6 @@ def generate_qwen(
             for i, img_path in enumerate(image_paths):
                 zipf.write(img_path, arcname=f"Img_{i}.png")
         zip_path = zip_name
-
-    # Clean up adapters
-    current_adapters = pipe_qwen.get_list_adapters()
-    for adapter in current_adapters:
-        pipe_qwen.delete_adapters(adapter)
-    pipe_qwen.disable_lora()
     
     return image_paths, seed, f"{duration:.2f}", zip_path
 
@@ -167,8 +105,6 @@ def generate(
     num_inference_steps: int,
     num_images: int,
     zip_images: bool,
-    lora_input: str,
-    lora_scale: float,
     progress=gr.Progress(track_tqdm=True),
 ):
     final_negative_prompt = negative_prompt if use_negative_prompt else ""
@@ -183,8 +119,6 @@ def generate(
         num_inference_steps=num_inference_steps,
         num_images=num_images,
         zip_images=zip_images,
-        lora_input=lora_input,
-        lora_scale=lora_scale,
         progress=progress,
     )
 
@@ -212,7 +146,7 @@ footer {
 '''
 
 # Gradio interface
-with gr.Blocks(css=css, theme="bethecloud/storj_theme", delete_cache=(240, 240)) as demo:
+with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
     gr.Markdown(DESCRIPTION)
     with gr.Row():
         prompt = gr.Text(
@@ -231,7 +165,6 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme", delete_cache=(240, 240))
             choices=list(aspect_ratios.keys()),
             value="1:1",
         )
-        lora = gr.Textbox(label="qwen image lora (optional)", placeholder="flymy-ai/qwen-image-anime-irl-lora")
     with gr.Accordion("Additional Options", open=False):
         use_negative_prompt = gr.Checkbox(
             label="Use negative prompt",
@@ -290,14 +223,6 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme", delete_cache=(240, 240))
             value=1,
         )
         zip_images = gr.Checkbox(label="Zip generated images", value=False)
-        with gr.Row(): 
-            lora_scale = gr.Slider(
-                label="LoRA Scale",
-                minimum=0,
-                maximum=2,
-                step=0.01,
-                value=1,
-            )
         
         gr.Markdown("### Output Information")
         seed_display = gr.Textbox(label="Seed used", interactive=False)
@@ -338,8 +263,6 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme", delete_cache=(240, 240))
             num_inference_steps,
             num_images,
             zip_images,
-            lora,
-            lora_scale,
         ],
         outputs=[result, seed_display, generation_time, zip_file],
         api_name="run",
@@ -355,4 +278,4 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme", delete_cache=(240, 240))
     )
 
 if __name__ == "__main__":
-    demo.queue(max_size=50).launch(share=False, mcp_server=True, ssr_mode=False, debug=True, show_error=True)
+    demo.queue(max_size=50).launch(share=False, mcp_server=True, ssr_mode=False, show_error=True)