Update app.py

app.py

@@ -1,27 +1,27 @@
 import gradio as gr
 import numpy as np
 import random
-import spaces
 import torch
-from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
-from PIL import Image
-import io
+from diffusers import DiffusionPipeline
 import os
 
+# Constants
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
+DEFAULT_IMAGE_SIZE = 1024
+
+# Model setup
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# Set your Hugging Face API token
 huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
 
-# Load the diffusion pipeline with the Hugging Face API token
-pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, token=huggingface_token).to(device)
+pipe = DiffusionPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev",
+    torch_dtype=dtype,
+    token=huggingface_token
+).to(device)
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 2048
-
-@spaces.GPU(duration=200)
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+def generate_image(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().manual_seed(seed)
@@ -35,30 +35,15 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidan
     ).images[0]
     return image, seed
 
-def download_image(image, file_format):
-    img_byte_arr = io.BytesIO()
-    image.save(img_byte_arr, format=file_format)
-    img_byte_arr = img_byte_arr.getvalue()
-    return img_byte_arr
-
-examples = [
-    "a galaxy swirling with vibrant blue and purple hues",
-    "a futuristic cityscape under a dark sky",
-    "a serene forest with a magical glowing tree",
-    "a futuristic cityscape with sleek skyscrapers and flying cars",
-    "a portrait of a smiling woman with a colorful floral crown",
-    "a fantastical creature with the body of a dragon and the wings of a butterfly",
-]
-
 css = """
 body {
     background-color: #f4faff;
     color: #005662;
     font-family: 'Poppins', sans-serif;
 }
-#col-container {
+.container {
     margin: 0 auto;
-    max-width: 100%;
+    max-width: 900px;
     padding: 20px;
 }
 .gr-button {
@@ -70,135 +55,62 @@ body {
 .gr-button:hover {
     background-color: #0277bd;
 }
-.gr-examples-card {
-    border: 1px solid #eeeeee;
+.gr-box {
     border-radius: 12px;
-    padding: 16px;
-    margin-bottom: 12px;
-}
-.gr-examples-card:hover {
-    background-color: #f4faf2;
-    border-color: #0277bd;
-    color: #005662;
-}
-.gr-progress-bar, .gr-progress-bar-fill {
-    background-color: #0288d1 !important;
-}
-.gr-slider, .gr-slider-track {
-    background-color: #0288d1 !important;
-}
-.gr-slider-thumb {
-    background-color: #005662 !important;
-}
-.gr-text-input, .gr-image {
-    width: 100%;
-    box-sizing: border-box;
-    margin-bottom: 10px;
+    border: 1px solid #eeeeee;
 }
 """
 
 with gr.Blocks(css=css, theme=gr.themes.Soft(primary_hue="blue", secondary_hue="gray")) as demo:
+    gr.Markdown("""
+    # FLUX.1 [dev] | A Text-To-Image Rectified Flow 12B Transformer
     
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 [dev] | A Text-To-Image Rectified Flow 12B Transformer
-        
-<a href="https://huggingface.co/black-forest-labs/FLUX.1-dev" style="text-decoration:none;">
-<div class="gr-examples-card">
-    <h3>View Model Details</h3>
-    <p>Explore more about this model on Hugging Face.</p>
-</div>
-</a>
-        """)
-
-        with gr.Row():
+    Enter a text prompt below to generate an image. Click 'Generate' to create your image.
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=4):
             prompt = gr.Text(
                 label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
+                placeholder="Enter your prompt here",
+                lines=2
             )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
+        with gr.Column(scale=1):
+            generate_button = gr.Button("Generate", variant="primary")
+    
+    result = gr.Image(label="Generated Image", type="pil")
+    
+    with gr.Accordion("Advanced Settings", open=False):
+        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.Accordion("Advanced Settings", open=True):
-            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,
-                )
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                )
-            
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance Scale",
-                    minimum=1,
-                    maximum=15,
-                    step=0.1,
-                    value=3.5,
-                )
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=28,
-                )
+        with gr.Row():
+            width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=DEFAULT_IMAGE_SIZE)
+            height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=DEFAULT_IMAGE_SIZE)
         
-        download_format = gr.Radio(
-            label="Download Format",
-            choices=["PNG", "JPEG", "SVG", "WEBP"],
-            value="PNG",
-            type="value",
-        )
-
-        download_button = gr.Button("Download Image")
-
-        download_button.click(
-            fn=download_image,
-            inputs=[result, download_format],
-            outputs=gr.File(label="Download"),
-        )
-
-        gr.Examples(
-            examples=examples,
-            fn=infer,
-            inputs=[prompt],
-            outputs=[result, seed],
-            cache_examples="lazy"
-        )
-
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
+        with gr.Row():
+            guidance_scale = gr.Slider(label="Guidance Scale", minimum=1, maximum=15, step=0.1, value=3.5)
+            num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=50, step=1, value=28)
+    
+    gr.Examples(
+        examples=[
+            "a galaxy swirling with vibrant blue and purple hues",
+            "a futuristic cityscape under a dark sky",
+            "a serene forest with a magical glowing tree",
+            "a portrait of a smiling woman with a colorful floral crown",
+            "a fantastical creature with the body of a dragon and the wings of a butterfly",
+        ],
+        inputs=prompt,
+        outputs=[result, seed],
+        fn=generate_image,
+        cache_examples=True,
+    )
+    
+    generate_button.click(
+        fn=generate_image,
         inputs=[prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
         outputs=[result, seed]
     )
 
-    demo.load(
-        fn=lambda: None,
-        inputs=None,
-        outputs=None
-    )
 
-demo.launch(share=True)
+demo.launch(share=True)