Update app.py

app.py

@@ -4,37 +4,67 @@ import random
 import spaces
 import torch
 from diffusers import DiffusionPipeline
+from transformers import CLIPTokenizer
 
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-pipe = DiffusionPipeline.from_pretrained("UnfilteredAI/NSFW-Flux-v1", torch_dtype=dtype).to(device)
+# Initialize CLIP tokenizer for prompt length checking
+tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
+
+pipe = DiffusionPipeline.from_pretrained(
+    "UnfilteredAI/NSFW-Flux-v1",
+    torch_dtype=dtype
+).to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
+MAX_TOKENS = 77  # CLIP's maximum token length
+
+def truncate_prompt(prompt):
+    """Truncate the prompt to fit within CLIP's token limit"""
+    tokens = tokenizer.encode(prompt, truncation=True, max_length=MAX_TOKENS)
+    return tokenizer.decode(tokens)
 
 @spaces.GPU()
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
+def infer(
+    prompt,
+    seed=42,
+    randomize_seed=False,
+    width=1024,
+    height=1024,
+    num_inference_steps=4,
+    progress=gr.Progress(track_tqdm=True)
+):
+    # Truncate prompt if necessary
+    truncated_prompt = truncate_prompt(prompt)
+    
     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,
+    
+    try:
+        image = pipe(
+            prompt=truncated_prompt,
+            width=width,
+            height=height,
+            num_inference_steps=num_inference_steps,
+            generator=generator,
             guidance_scale=0.0
-    ).images[0] 
-    return image, seed
- 
+        ).images[0]
+        
+        return image, seed
+    except Exception as e:
+        raise gr.Error(f"Error generating image: {str(e)}")
+
 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",
 ]
 
-css="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 520px;
@@ -42,13 +72,18 @@ css="""
 """
 
 with gr.Blocks(css=css) as demo:
-    
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""NSFW-Flux-v1 is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. Finetuned by UnfilteredAI, this model is designed to produce a wide range of images, including explicit and NSFW (Not Safe For Work) images from textual inputs
+        gr.Markdown("""
+            NSFW-Flux-v1 is a 12 billion parameter rectified flow transformer 
+            capable of generating images from text descriptions. 
+            Finetuned by UnfilteredAI, this model is designed to produce 
+            a wide range of images, including explicit and NSFW 
+            (Not Safe For Work) images from textual inputs.
+            
+            Note: Long prompts will be automatically truncated to fit the model's requirements.
         """)
         
         with gr.Row():
-            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -56,13 +91,11 @@ 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,
@@ -70,11 +103,9 @@ 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,
@@ -82,7 +113,6 @@ with gr.Blocks(css=css) as demo:
                     step=32,
                     value=1024,
                 )
-                
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
@@ -92,8 +122,6 @@ with gr.Blocks(css=css) as demo:
                 )
             
             with gr.Row():
-                
-  
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
@@ -103,18 +131,25 @@ 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, num_inference_steps],
-        outputs = [result, seed]
-    )
+        
+        gr.on(
+            triggers=[run_button.click, prompt.submit],
+            fn=infer,
+            inputs=[
+                prompt,
+                seed,
+                randomize_seed,
+                width,
+                height,
+                num_inference_steps
+            ],
+            outputs=[result, seed]
+        )
 
 demo.launch()