Update app.py

app.py

@@ -357,16 +357,14 @@ def run_canonizer(
     steps,
     progress=gr.Progress(track_tqdm=True),
 ):
-    # Safety cleanup
     gc.collect()
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
 
-    # No images → error
     if not images:
         raise gr.Error("⚠️ Please upload an image first.")
 
-    # Load first image only
+    # Load first image
     first = images[0]
     try:
         path = first[0] if isinstance(first, (tuple, list)) else first
@@ -384,17 +382,16 @@ def run_canonizer(
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator(device=device).manual_seed(int(seed))
 
-    # Resize to frame
-    fw, fh = BASE_FRAMES[FRAME_LABELS.index(frame_label)].size
-    base_img = base_img.resize((fw, fh), Image.LANCZOS)
+    # STEP 1 — Downscale to 512px for SD‑Turbo
+    base_small = base_img.resize((512, 512), Image.LANCZOS)
 
     # Prompt
     prompt = generate_prompt(name, sin, personality, vibe, color, prop)
 
-    # SD‑Turbo inference (super stable)
+    # STEP 2 — SD‑Turbo inference (super stable)
     try:
-        result = pipe(
-            image=base_img,
+        result_small = pipe(
+            image=base_small,
             prompt=prompt,
             negative_prompt=DEFAULT_NEGATIVE_PROMPT,
             num_inference_steps=int(steps),
@@ -404,13 +401,17 @@ def run_canonizer(
         ).images[0]
     except Exception as e:
         print("INFERENCE ERROR:", e)
-        raise gr.Error("⚠️ The AI failed to generate the saint image. Try a smaller image or lower steps.")
+        raise gr.Error("⚠️ The AI failed to generate the saint image. Try lower steps.")
+
+    # STEP 3 — Upscale back to frame size (800×1920)
+    fw, fh = BASE_FRAMES[FRAME_LABELS.index(frame_label)].size
+    result_big = result_small.resize((fw, fh), Image.LANCZOS)
 
-    # Frame + ribbon
-    framed = apply_frame(result, frame_label)
+    # STEP 4 — Frame + ribbon
+    framed = apply_frame(result_big, frame_label)
     final_img = draw_name_on_ribbon(framed, name or "Unnamed Saint")
 
-    # Oracle
+    # STEP 5 — Oracle
     saint_text = generate_local_oracle(name, sin, personality, vibe, color, prop)
     saint_html = saint_text.replace("\n", "<br>")
 
@@ -422,12 +423,13 @@ def run_canonizer(
     </div>
     """
 
-    # Download
+    # STEP 6 — Download
     download_path = build_download_image(final_img, saint_text, name or "saint")
 
     return final_img, oracle_html, seed, download_path
 
 
+
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 #  CSS
 # ═══════════════════════════════════════════════════════════════════════