Update app.py

app.py

@@ -3,6 +3,7 @@ import numpy as np
 import random
 import spaces
 import torch
+import os
 import time
 from diffusers import DiffusionPipeline
 
@@ -13,7 +14,7 @@ except ImportError:
     raise ImportError("The 'sentencepiece' library is required but not installed. Please add it to your environment.")
 
 # Set the device and dtype
-dtype = torch.float16  # Change to float16 for better compatibility and performance
+dtype = torch.float16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 # Load the diffusion pipeline without requiring an API token
@@ -23,141 +24,82 @@ MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
 @spaces.GPU()
-def infer(prompt, negative_prompt=None, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, guidance_scale=7.5, progress=gr.Progress(track_tqdm=True)):
+def infer(prompt, negative_prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, guidance_scale=7.5, progress=gr.Progress(track_tqdm=True)):
     start_time = time.time()
-    
+
     if width > MAX_IMAGE_SIZE or height > MAX_IMAGE_SIZE:
         raise ValueError("Image size exceeds the maximum allowed dimensions.")
-    
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator(device=device).manual_seed(seed)
-    
+
     try:
-        # Include negative prompts in the diffusion pipeline call
         image = pipe(
-            prompt=prompt, 
-            negative_prompt=negative_prompt,  # Using the negative prompt here
+            prompt=prompt,
+            negative_prompt=negative_prompt,
             width=width,
             height=height,
-            num_inference_steps=num_inference_steps, 
+            num_inference_steps=num_inference_steps,
             generator=generator,
             guidance_scale=guidance_scale
         ).images[0]
     except Exception as e:
         print(f"Error generating image: {e}")
         return None, seed, f"Error: {str(e)}"
-    
-    if time.time() - start_time > 60:  # 60 seconds timeout
+
+    if time.time() - start_time > 60:
         return None, seed, "Image generation took too long and was cancelled."
 
     return image, seed, None
 
 examples = [
-    "a tiny astronaut hatching from an egg on the moon",
-    "a cat holding a sign that says hello world",
-    "an anime illustration of a wiener schnitzel",
+    ["a tiny astronaut hatching from an egg on the moon", "blurry, low quality"],
+    ["a cat holding a sign that says hello world", "dog, text, writing"],
+    ["an anime illustration of a wiener schnitzel", "realistic, photograph"],
 ]
 
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-    padding: 20px;
-    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
-    border-radius: 10px;
-    background-color: #f8f9fa;
-}
-#run-button {
-    background-color: #007bff;
-    color: white;
-    border: none;
-    padding: 10px 20px;
-    font-size: 16px;
-    border-radius: 5px;
-}
-#run-button:hover {
-    background-color: #0056b3;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown("""
-        # Custom Image Creator
-        A 12B param rectified flow transformer from [FLUX.1](https://blackforestlabs.ai/) for 4-step generation.
-        """, elem_id="title")
-        
-        prompt = gr.Textbox(
-            label="Prompt",
-            show_label=False,
-            max_lines=1,
-            placeholder="Enter your prompt...",
-        )
-        negative_prompt = gr.Textbox(
-            label="Negative Prompt",
-            show_label=False,
-            max_lines=1,
-            placeholder="Enter negative prompts (what to avoid)...",
-        )
-        run_button = gr.Button("Run", elem_id="run-button")
-        
-        result = gr.Image(label="Result", show_label=False, interactive=True)
-        
-        with gr.Accordion("Advanced Settings", open=False):
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-                tooltip="Seed value for reproducibility. Randomize for unique results."
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # Custom Image Creator
+    12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation
+    [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1)]
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            prompt = gr.Textbox(
+                label="Prompt",
+                placeholder="Enter your prompt",
+                lines=3
             )
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+                placeholder="Enter things to avoid in the image",
+                lines=2
+            )
+            run_button = gr.Button("Generate Image")
+        with gr.Column(scale=3):
+            result = gr.Image(label="Generated Image")
+
+    with gr.Accordion("Advanced Settings", open=False):
+        with gr.Row():
+            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                    tooltip="Adjust the width of the generated image."
-                )
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                    tooltip="Adjust the height of the generated image."
-                )
-            
-            with gr.Row():
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=4,
-                    tooltip="Controls the quality and coherence of the output."
-                )
-                guidance_scale = gr.Slider(
-                    label="Guidance Scale",
-                    minimum=0.0,
-                    maximum=20.0,
-                    step=0.5,
-                    value=7.5,
-                    tooltip="Higher values result in outputs closer to the prompt."
-                )
-        
-        gr.Examples(
-            examples=examples,
-            fn=infer,
-            inputs=[prompt],
-            outputs=[result, seed],
-            cache_examples="lazy"
-        )
+        with gr.Row():
+            width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
+            height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
+        with gr.Row():
+            num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=50, step=1, value=4)
+            guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=20.0, step=0.5, value=7.5)
+
+    gr.Examples(
+        examples=examples,
+        fn=infer,
+        inputs=[prompt, negative_prompt],
+        outputs=[result, seed],
+        cache_examples=True
+    )
 
     run_button.click(
         fn=infer,
@@ -169,5 +111,5 @@ with gr.Blocks(css=css) as demo:
     ## Save Your Image
     Right-click on the image and select 'Save As' to download the generated image.
     """)
-    
+
 demo.launch()