Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import numpy as np
 import random
-import spaces #[uncomment to use ZeroGPU]
+import spaces 
 from diffusers import ChromaPipeline
 import torch
 
@@ -19,19 +19,14 @@ pipe = pipe.to(device)
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-@spaces.GPU(duration=75) #[uncomment to use ZeroGPU]
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, lora_model, progress=gr.Progress(track_tqdm=True)):
+@spaces.GPU() 
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
 
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
         
     generator = torch.Generator(device).manual_seed(seed)
     
-    # Handle LoRA if needed (ChromaPipeline may not support LoRA by default)
-    # Uncomment and adapt if LoRA support is available
-    # if lora_model: 
-    #     pipe.load_lora_weights(lora_model)
-        
     image = pipe(
         prompt=prompt, 
         negative_prompt=negative_prompt,
@@ -65,8 +60,8 @@ with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
-        # Chroma1-HD - Anaglyph 3D Image Generation
-        Generate stereoscopic 3D images optimized for red-cyan anaglyph glasses
+        # Chroma1-HD
+        [Chroma1-HD](https://huggingface.co/lodestones/Chroma1-HD) is an 8.9B parameter text-to-image foundational model based on FLUX.1-schnell
         """)
         
         with gr.Row():
@@ -88,19 +83,14 @@ with gr.Blocks(css=css) as demo:
         result = gr.Image(label="Result", show_label=False)
 
         with gr.Accordion("Advanced Settings", open=False):
-            
-            lora_model = gr.Textbox(
-                label="LoRA model id (if supported)", 
-                placeholder="Leave empty if not using LoRA",
-                visible=False  # Hidden by default as ChromaPipeline may not support LoRA
-            )
+        
             
             guidance_scale = gr.Slider(
                 label="Guidance Scale",
                 minimum=1.0,
                 maximum=10.0,
                 step=0.1,
-                value=3.0,  # Default from your example
+                value=3.0, 
             )
             
             seed = gr.Slider(
@@ -108,7 +98,7 @@ with gr.Blocks(css=css) as demo:
                 minimum=0,
                 maximum=MAX_SEED,
                 step=1,
-                value=433,  # Using the seed from your example
+                value=433, 
             )
             
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
@@ -120,7 +110,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Default resolution
+                    value=1024,
                 )
                 
                 height = gr.Slider(
@@ -128,7 +118,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Default resolution
+                    value=1024, 
                 )
             
             num_inference_steps = gr.Slider(
@@ -136,7 +126,7 @@ with gr.Blocks(css=css) as demo:
                 minimum=1,
                 maximum=100,
                 step=1,
-                value=40,  # Default from your example
+                value=40, 
             )
         
         gr.Examples(
@@ -147,7 +137,7 @@ with gr.Blocks(css=css) as demo:
     gr.on(
         triggers=[run_button.click, prompt.submit, negative_prompt.submit],
         fn=infer,
-        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, lora_model],
+        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
         outputs=[result, seed]
     )