Update app.py

app.py

@@ -3,8 +3,8 @@ 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 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
 
 dtype = torch.bfloat16
@@ -18,10 +18,20 @@ torch.cuda.empty_cache()
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
+# Bind the live preview helper function to the pipe
 pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
 
 @spaces.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)):
+    # --- Fix: Truncate prompt to allowed token length ---
+    # Use the pipeline's tokenizer to tokenize the prompt.
+    tokenizer = pipe.tokenizer
+    max_length = tokenizer.model_max_length  # Typically 77 for CLIP
+    tokens = tokenizer.tokenize(prompt)
+    if len(tokens) > max_length:
+        prompt = tokenizer.decode(tokenizer.encode(prompt, truncation=True, max_length=max_length), skip_special_tokens=True)
+    # -------------------------------------------------------
+    
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator().manual_seed(seed)
@@ -36,7 +46,7 @@ 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",
@@ -44,7 +54,7 @@ examples = [
     "an anime illustration of a wiener schnitzel",
 ]
 
-css="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 520px;
@@ -60,7 +70,6 @@ with gr.Blocks(css=css) as demo:
         """)
         
         with gr.Row():
-            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -68,13 +77,11 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
-            
             run_button = gr.Button("Run", scale=0)
         
         result = gr.Image(label="Result", show_label=False)
         
         with gr.Accordion("Advanced Settings", open=False):
-            
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -82,11 +89,9 @@ with gr.Blocks(css=css) as demo:
                 step=1,
                 value=0,
             )
-            
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
             
             with gr.Row():
-                
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
@@ -94,7 +99,6 @@ with gr.Blocks(css=css) as demo:
                     step=32,
                     value=1024,
                 )
-                
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
@@ -104,7 +108,6 @@ with gr.Blocks(css=css) as demo:
                 )
             
             with gr.Row():
-
                 guidance_scale = gr.Slider(
                     label="Guidance Scale",
                     minimum=1,
@@ -112,7 +115,6 @@ with gr.Blocks(css=css) as demo:
                     step=0.1,
                     value=3.5,
                 )
-  
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
@@ -122,18 +124,18 @@ 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]
     )
 
-demo.launch()
+demo.launch()