Update app.py

app.py

@@ -3,8 +3,8 @@ import numpy as np
 import random
 import spaces
 import torch
-from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
-from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
+from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
 from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
 import os  # Import os module to access environment variables
 
@@ -14,7 +14,6 @@ hf_token = os.environ.get("HF_API_TOKEN")
 dtype = torch.bfloat16
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
- 
 
 taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
 good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype, token=hf_token).to(device)
@@ -43,21 +42,31 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidan
             good_vae=good_vae,
         ):
             yield img, seed
-    
+
 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",
 ]
 
-css="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 520px;
 }
 """
 
-with gr.Blocks(css=css) as demo:
+# Define the dark theme
+dark_theme = gr.themes.Default(
+    primary_hue="blue",
+    neutral_hue="gray",
+    spacing_size="md",
+    font="default",
+    # Set mode to 'dark' to enable dark mode
+    mode="dark"
+)
+
+with gr.Blocks(theme=dark_theme, css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""# FLUX.1 [dev]
@@ -128,18 +137,18 @@ with gr.Blocks(css=css) as demo:
                 )
         
         gr.Examples(
-            examples = examples,
-            fn = infer,
-            inputs = [prompt],
-            outputs = [result, seed],
+            examples=examples,
+            fn=infer,
+            inputs=[prompt],
+            outputs=[result, seed],
             cache_examples="lazy"
         )
 
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
+        fn=infer,
+        inputs=[prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result, seed]
     )
 
-demo.launch()
+demo.launch()