Update app.py

app.py

@@ -15,15 +15,8 @@ from transformers import AutoProcessor, AutoModelForMaskGeneration, pipeline
 from dataclasses import dataclass
 from typing import Any, List, Dict, Optional, Union, Tuple
 
-# --- Constants and Setup ---
-# Ensure all required modules are available
-check_min_version("0.29.0.dev0")
-
-# Set a seed for reproducibility. The original script uses a fixed seed.
-generator = torch.Generator(device="cuda").manual_seed(42)
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-
 # --- Helper Dataclasses (Identical to diptych_prompting_inference.py) ---
 @dataclass
 class BoundingBox:
@@ -220,15 +213,22 @@ def run_diptych_prompting(
     input_image: Image.Image,
     subject_name: str,
     target_prompt: str,
-    attn_enforce: float,
-    ctrl_scale: float,
-    width: int,
-    height: int,
-    pixel_offset: int,
-    num_steps: int,
-    guidance: float,
+    attn_enforce: float = 1.3,
+    ctrl_scale: float = 0.95,
+    width: int = 768,
+    height: int = 768,
+    pixel_offset: int = 8,
+    num_steps: int = 30,
+    guidance: float = 3.5,
+    seed: int = 42,
+    randomize_seed: bool = False,
     progress=gr.Progress(track_tqdm=True)
 ):
+    if randomize_seed:
+        actual_seed = random.randint(0, 9223372036854775807)
+    else:
+        actual_seed = seed
+        
     if input_image is None: raise gr.Error("Please upload a reference image.")
     if not subject_name: raise gr.Error("Please provide the subject's name (e.g., 'a red car').")
     if not target_prompt: raise gr.Error("Please provide a target prompt.")
@@ -261,6 +261,7 @@ def run_diptych_prompting(
 
     # 4. Run Inference (using parameters identical to the original script)
     progress(0.4, desc="Running diffusion process...")
+    generator = torch.Generator(device="cuda").manual_seed(actual_seed)
     result = pipe(
         prompt=diptych_text_prompt,
         height=diptych_size[1],
@@ -313,6 +314,8 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
                 width = gr.Slider(minimum=512, maximum=1024, value=768, step=64, label="Image Width")
                 height = gr.Slider(minimum=512, maximum=1024, value=768, step=64, label="Image Height")
                 pixel_offset = gr.Slider(minimum=0, maximum=32, value=8, step=1, label="Padding (Pixel Offset)")
+                seed = gr.Slider(minimum=0, maximum=9223372036854775807, value=42, step=1, label="Seed")
+                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
         with gr.Column(scale=1):
             output_image = gr.Image(type="pil", label="Generated Image")
 
@@ -325,12 +328,12 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         inputs=[input_image, subject_name, target_prompt],
         outputs=output_image,
         fn=run_diptych_prompting,
-        cache_examples=False,
+        cache_examples="lazy",
     )
 
     run_button.click(
         fn=run_diptych_prompting,
-        inputs=[input_image, subject_name, target_prompt, attn_enforce, ctrl_scale, width, height, pixel_offset, num_steps, guidance],
+        inputs=[input_image, subject_name, target_prompt, attn_enforce, ctrl_scale, width, height, pixel_offset, num_steps, guidance, seed, randomize_seed],
         outputs=output_image
     )