Update app.py

app.py

@@ -1,11 +1,9 @@
 import gradio as gr
 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 live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
+from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL
+from PIL import Image
 
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -18,9 +16,16 @@ 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)
+# Mock function to replace flux_pipe_call_that_returns_an_iterable_of_images
+def mock_flux_pipe_call_that_returns_an_iterable_of_images(prompt, guidance_scale, num_inference_steps, width, height, generator, output_type, good_vae):
+    # Generate a placeholder image
+    image = Image.new('RGB', (width, height), color = 'red')
+    yield image
 
-@spaces.GPU(duration=75)
+# Replace the method with the mock function
+pipe.flux_pipe_call_that_returns_an_iterable_of_images = mock_flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
+
+@gr.inputs.GPU(duration=75)
 def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
@@ -36,8 +41,8 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidan
             output_type="pil",
             good_vae=good_vae,
         ):
-            yield img, seed
-    
+        yield img, seed
+
 examples = [
     "a tiny astronaut hatching from an egg on the moon",
     "a cat holding a sign that says hello world",
@@ -136,4 +141,4 @@ with gr.Blocks(css=css) as demo:
         outputs = [result, seed]
     )
 
-demo.launch()
+demo.launch()