Update app.py

app.py

@@ -3,8 +3,7 @@ import numpy as np
 import random
 import spaces
 import torch
-from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler
-from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
+from diffusers import DiffusionPipeline
 
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -12,7 +11,7 @@ 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 
+MAX_IMAGE_SIZE = 2048
 
 @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)):
@@ -20,15 +19,15 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidan
         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,
+        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",
@@ -113,18 +112,18 @@ 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]
     )
 
     # Adding image input options at the bottom
@@ -137,18 +136,21 @@ with gr.Blocks(css=css) as demo:
     
     additional_image_output = gr.Image(label="Selected Image", show_label=False)
 
-    def select_image(uploaded_image, image_url, use_generated=False):
+    def select_image(uploaded_image, image_url, use_generated):
         if use_generated:
-            return result
+            return result.value  # Return the value of the generated image
         elif uploaded_image is not None:
             return uploaded_image
         elif image_url:
-            return gr.Image.load(image_url)
+            try:
+                return gr.Image.load(image_url)
+            except Exception as e:
+                return f"Failed to load image from URL: {e}"
         return None
 
     # Updated click and change triggers
     use_generated_image.click(lambda: select_image(None, None, True), inputs=[], outputs=[additional_image_output])
-    uploaded_image.change(select_image, inputs=[uploaded_image, image_url], outputs=[additional_image_output])
-    image_url.submit(select_image, inputs=[uploaded_image, image_url], outputs=[additional_image_output])
+    uploaded_image.change(select_image, inputs=[uploaded_image, image_url, gr.State(False)], outputs=[additional_image_output])
+    image_url.submit(select_image, inputs=[uploaded_image, image_url, gr.State(False)], outputs=[additional_image_output])
 
 demo.launch()