add save to outputs

app.py

@@ -5,6 +5,8 @@ import torch
 from diffusers import FluxTransformer2DModel, FluxPipeline
 from transformers import T5EncoderModel, CLIPTextModel
 from optimum.quanto import QuantizedDiffusersModel, QuantizedTransformersModel
+from datetime import datetime
+from PIL import Image
 import json
 import devicetorch
 import os
@@ -25,6 +27,21 @@ nav {
   display: inline;
 }
 """
+#save all generated images into an output folder with unique name
+def save_images(images):  
+    output_folder = "output" 
+    os.makedirs(output_folder, exist_ok=True)
+    saved_paths = []
+    
+    for i, img in enumerate(images):
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        filename = f"flux_{timestamp}_{i}.png"
+        filepath = os.path.join(output_folder, filename)
+        img.save(filepath)
+        saved_paths.append(filepath)
+    
+    return saved_paths
+    
 def infer(prompt, checkpoint="black-forest-labs/FLUX.1-schnell", seed=42, guidance_scale=0.0, num_images_per_prompt=1, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
     global pipe
     global selected
@@ -79,7 +96,9 @@ def infer(prompt, checkpoint="black-forest-labs/FLUX.1-schnell", seed=42, guidan
     print(f"Inference finished!")
     devicetorch.empty_cache(torch)
     print(f"emptied cache")
-    return images, seed
+    saved_paths = save_images(images) #save the images into the output folder
+    return images, seed, saved_paths    
+    
 def update_slider(checkpoint, num_inference_steps):
     if checkpoint == "sayakpaul/FLUX.1-merged":
         return 8