fixes update and time

app.py

@@ -3,7 +3,7 @@ import numpy as np
 import random
 import torch
 from diffusers import (
-    DiffusionPipeline, FluxPipeline, PixArtSigmaPipeline,
+    DiffusionPipeline, StableDiffusion3Pipeline, FluxPipeline, PixArtSigmaPipeline,
     AuraFlowPipeline, Kandinsky3Pipeline, HunyuanDiTPipeline,
     LuminaText2ImgPipeline
 )
@@ -26,7 +26,7 @@ TORCH_DTYPE = torch.bfloat16 if torch.cuda.is_available() else torch.float32
 MODEL_CONFIGS = {
     "Stable Diffusion 3.5": {
         "repo_id": "stabilityai/stable-diffusion-3.5-large",
-        "pipeline_class": DiffusionPipeline
+        "pipeline_class": StableDiffusion3Pipeline
     },
     "FLUX": {
         "repo_id": "black-forest-labs/FLUX.1-dev",
@@ -162,7 +162,7 @@ def generate_image(
                 seed = random.randint(0, MAX_SEED)
             
             generator = torch.Generator(DEVICE).manual_seed(seed)
-            
+            print(f"Generating image with {model_name}...")
             progress(0.3, desc=f"Generating image with {model_name}...")
             
             # Generate image
@@ -290,20 +290,22 @@ with gr.Blocks(css=css) as demo:
         return f"Current memory usage: System RAM: {memory_gb:.2f} GB"
 
     # Handle generation for each model
-    @spaces.GPU(duration=180)
+    @spaces.GPU(duration=600)
     def generate_all(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress()):
         outputs = []
         for model_name in MODEL_CONFIGS.keys():
+            print(f"IMAGE GENERATING {model_name} ")
             try:
                 image, used_seed = generate_image(
                     model_name, prompt, negative_prompt, seed,
                     randomize_seed, width, height, guidance_scale,
                     num_inference_steps, progress
                 )
+                print(f"IMAGE GENERATED {model_name} {update_memory_usage()}")
                 outputs.extend([image, used_seed])
                 
                 # Update memory usage after each model
-                memory_indicator.update(update_memory_usage())
+                #memory_indicator.update(update_memory_usage())
                 
             except Exception as e:
                 outputs.extend([None, None])