Adding warmup function and seed slider

.gitignore

@@ -8,3 +8,4 @@ diffqrcoder/losses/__pycache__/__init__.cpython-310.pyc
 diffqrcoder/losses/__pycache__/perceptual_loss.cpython-310.pyc
 diffqrcoder/losses/__pycache__/personalized_code_loss.cpython-310.pyc
 diffqrcoder/losses/__pycache__/scanning_robust_loss.cpython-310.pyc
+models/cetusMix_Whalefall2_fp16.safetensors

app.py

@@ -11,6 +11,18 @@ DEFAULT_PROMPT = (
 
 DEFAULT_NEG = "easynegative"
 
+def warmup():
+    """
+    Run once on Space startup, on CPU only.
+    Downloads models & builds pipeline into cache.
+    """
+    print("🔥 Warmup starting: downloading models & building pipeline on CPU...")
+    pipe = load_pipeline()
+    print("🔥 Warmup done. Pipeline ready on CPU.")
+
+    # Optional: return a tiny status string for UI (doesn't have to be used)
+    return "Warmup complete."
+
 @spaces.GPU
 def infer(
     url_or_text: str,
@@ -20,6 +32,7 @@ def infer(
     scanning_robust_guidance_scale: float,
     perceptual_guidance_scale: float,
     srmpgd_iters: int,
+    seed: int
 ):
     try:
         print("🔧 infer() starting")
@@ -52,6 +65,7 @@ def infer(
             scanning_robust_guidance_scale=scanning_robust_guidance_scale,
             perceptual_guidance_scale=perceptual_guidance_scale,
             srmpgd_num_iteration=srmpgd_num_iteration,
+            seed=seed,
         )
 
         print("✅ generation complete")
@@ -83,6 +97,15 @@ Generate aesthetic, scanning-robust QR codes using the **DiffQRCoder** pipeline
         lines=3,
     )
 
+    seed_input = gr.Slider(
+        minimum=0,
+        maximum=999999,
+        value=42,
+        step=1,
+        label="Seed",
+    )
+
+
     with gr.Accordion("Advanced parameters", open=False):
         steps = gr.Slider(
             minimum=10,
@@ -133,8 +156,10 @@ Generate aesthetic, scanning-robust QR codes using the **DiffQRCoder** pipeline
             srg_scale,
             pg_scale,
             srmpgd_iters,
+            seed_input
         ],
         outputs=[out],
     )
+    demo.load(fn=warmup, inputs=None, outputs=None)
 
 demo.launch()