Update app.py

app.py

@@ -4,6 +4,7 @@ import numpy as np
 import random
 import torch
 from diffusers import StableDiffusion3Pipeline, SD3Transformer2DModel, FlowMatchEulerDiscreteScheduler
+import re
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 dtype = torch.float16
@@ -14,7 +15,7 @@ pipe = StableDiffusion3Pipeline.from_pretrained(repo, torch_dtype=torch.float16)
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1344
 
-@spaces.GPU
+@spaces.GPU(enable_queue=True)
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
 
     if randomize_seed:
@@ -31,6 +32,14 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
         height = height,
         generator = generator
     ).images[0] 
+
+    # Sanitize the prompt to make it filesystem-safe
+    sanitized_prompt = re.sub(r'[<>:"/\\|?*]', '_', prompt)
+    sanitized_prompt = sanitized_prompt[:50]  # Truncate to avoid excessively long filenames
+
+    # Save the image as PNG with the sanitized prompt in the name
+    save_path = f"generated_image_{sanitized_prompt}_{seed}.png"
+    image.save(save_path, format="PNG")
     
     return image, seed
 
@@ -93,7 +102,6 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme", js=js_func) as generino:
         prompt = gr.Text(
                   label="Prompt",
                   show_label=False,
-                  max_lines=3,
                   placeholder="Enter your prompt",
                   container=False,
                   elem_id="prompt",
@@ -168,4 +176,4 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme", js=js_func) as generino:
         outputs = [result, seed]
     )
 
-generino.launch(show_api=False)
+generino.queue(max_size=10).launch(show_api=False, share=False)