Update app.py

app.py

@@ -1,3 +1,5 @@
+# credits : https://huggingface.co/spaces/black-forest-labs/FLUX.1-dev
+
 import os
 import gradio as gr
 import numpy as np
@@ -19,7 +21,6 @@ pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_d
 torch.cuda.empty_cache()
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 2048
 
 pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
 
@@ -57,27 +58,20 @@ def infer(name, pet, background, style, seed=42, randomize_seed=False, width=102
     else:
         prompt = place + "holding a signal that says " + name + "in a 3D style"
         
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
+    seed = random.randint(0, MAX_SEED)
     generator = torch.Generator().manual_seed(seed)
     
     for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
             prompt=prompt,
-            guidance_scale=guidance_scale,
-            num_inference_steps=num_inference_steps,
-            width=width,
-            height=height,
+            guidance_scale=3,5,
+            num_inference_steps=28,
+            width=1024,
+            height=1024,
             generator=generator,
             output_type="pil",
             good_vae=good_vae,
         ):
             yield img, seed
-        
-examples = [
-    "cat",
-    "dog",
-    "bunny"
-]
 
 css="""
 #col-container {
@@ -127,67 +121,11 @@ with gr.Blocks(css=css) as demo:
             ) 
            
         result = gr.Image(label="Result", show_label=False) 
-        
-        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.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,
-                )
-        
-        gr.Examples(
-            examples = examples,
-            fn = infer,
-            inputs = [prompt],
-            outputs = [result, seed],
-            cache_examples="lazy"
-        )
 
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn = infer,
-        inputs = [prompt, pet, background, style, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        inputs = [prompt, pet, background, style],
         outputs = [result, seed]
     )