Update app.py

app.py

@@ -1,68 +1,59 @@
+import random
+
 import gradio as gr
 import numpy as np
-import random
 import spaces
 import torch
 from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler
 from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
+from gradio_imagefeed import ImageFeed
+
 
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
-
 pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
+LICENSE=f"""# Better UI for FLUX.1 [dev] [[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)]"""
+CSS = "#col-container { margin: 0 auto; max-width: 900px; }"
+EXAMPLES = ["a tiny elephant hatching from a turtle egg in the palm of a human hand, highly detailed textures, close-up"]
+
 
-@spaces.GPU(duration=190)
-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)):
+@spaces.GPU(duration=120)
+def infer(prompt, seed=99999, randomize_seed=True, width=896, height=1152, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
+    
     generator = torch.Generator().manual_seed(seed)
+    
     image = pipe(
         prompt = prompt, 
         width = width,
         height = height,
         num_inference_steps = num_inference_steps, 
         generator = generator,
-        guidance_scale=guidance_scale
-    ).images[0] 
-    return image, 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",
-]
+        guidance_scale=guidance_scale).images[0]
+    
+    yield image, seed
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
 
-with gr.Blocks(css=css) as demo:
-    
+with gr.Blocks(css=CSS) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 [dev]
-12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
-[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
-        """)
+        gr.Markdown(LICENSE)
         
         with gr.Row():
-            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
+                max_lines=5,
+                placeholder="Prompt",
+                container=False)
             
             run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
+
+        result = ImageFeed(label="Result", show_label=False)
+        # result = gr.Image(label="Result", show_label=False)
         
         with gr.Accordion("Advanced Settings", open=False):
             
@@ -71,52 +62,45 @@ with gr.Blocks(css=css) as demo:
                 minimum=0,
                 maximum=MAX_SEED,
                 step=1,
-                value=0,
-            )
+                value=random.randint(0, MAX_SEED))
             
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            randomize_seed = gr.Checkbox(label="Randomize", value=True)
             
             with gr.Row():
-                
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,
-                )
+                    value=896)
                 
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,
-                )
+                    value=1152)
             
             with gr.Row():
-
                 guidance_scale = gr.Slider(
                     label="Guidance Scale",
                     minimum=1,
                     maximum=15,
                     step=0.1,
-                    value=3.5,
-                )
+                    value=3)
   
                 num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
+                    label="Inference Steps",
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=28,
-                )
+                    value=28)
         
         gr.Examples(
-            examples = examples,
-            fn = infer,
-            inputs = [prompt],
-            outputs = [result, seed],
+            examples=EXAMPLES,
+            fn=infer,
+            inputs=[prompt],
+            outputs=[result, seed],
             cache_examples="lazy"
         )