Trying to speed up

app.py

@@ -1,17 +1,10 @@
 # app.py
 import gradio as gr
 import spaces
-from diffqrcoder_wrapper import generate_qr_art
+from diffqrcoder_wrapper import generate_qr_art, load_pipeline
+import torch
 
-DEFAULT_PROMPT = (
-    "whimsical biomimetic blueprint, iridescent inks swirling through "
-    "mechanical petals, soft gears woven with luminescent filigree"
-)
-
-DEFAULT_NEG = "easynegative"
-
-
-@spaces.GPU  # ZeroGPU: attach GPU only for this call
+@spaces.GPU
 def infer(
     url_or_text: str,
     prompt: str,
@@ -21,100 +14,22 @@ def infer(
     perceptual_guidance_scale: float,
     srmpgd_iters: int,
 ):
-    # Map 0 → None for SR-MPGD iteration (as in original script)
+    # 🔹 Make sure pipeline is loaded *once* and then just moved to GPU
+    pipe = load_pipeline()
+
+    # move to GPU *here*, with an explicit non-blocking call
+    pipe.to("cuda")
+
     srmpgd_num_iteration = None if srmpgd_iters == 0 else srmpgd_iters
 
     img = generate_qr_art(
+        pipe,
         url_or_text=url_or_text,
         prompt=prompt,
-        neg_prompt=DEFAULT_NEG,
         num_inference_steps=num_inference_steps,
-        qrcode_module_size=20,
-        qrcode_padding=78,
         controlnet_conditioning_scale=controlnet_scale,
         scanning_robust_guidance_scale=scanning_robust_guidance_scale,
         perceptual_guidance_scale=perceptual_guidance_scale,
         srmpgd_num_iteration=srmpgd_num_iteration,
-        srmpgd_lr=0.1,
-        seed=1,
     )
     return img
-
-
-with gr.Blocks() as demo:
-    gr.Markdown(
-        r"""
-# DiffQRCoder – ZeroGPU demo
-
-Generate aesthetic, scanning-robust QR codes using the **DiffQRCoder** pipeline
-([WACV 2025](https://openaccess.thecvf.com/content/WACV2025/html/Liao_DiffQRCoder_Diffusion-Based_Aesthetic_QR_Code_Generation_with_Scanning_Robustness_Guided_WACV_2025_paper.html)) 🚀
-        """
-    )
-
-    with gr.Row():
-        url = gr.Textbox(
-            label="QR contents (URL or text)",
-            value="https://example.com",
-        )
-
-    prompt = gr.Textbox(
-        label="Style prompt",
-        value=DEFAULT_PROMPT,
-        lines=3,
-    )
-
-    with gr.Accordion("Advanced parameters", open=False):
-        steps = gr.Slider(
-            minimum=10,
-            maximum=60,
-            value=40,
-            step=1,
-            label="Diffusion steps (num_inference_steps)",
-        )
-        control_scale = gr.Slider(
-            minimum=0.5,
-            maximum=2.0,
-            value=1.35,
-            step=0.05,
-            label="ControlNet conditioning scale",
-        )
-        srg_scale = gr.Slider(
-            minimum=0,
-            maximum=800,
-            value=500,
-            step=10,
-            label="Scanning-robust guidance scale (srg)",
-        )
-        pg_scale = gr.Slider(
-            minimum=0,
-            maximum=10,
-            value=2,
-            step=0.5,
-            label="Perceptual guidance scale (pg)",
-        )
-        srmpgd_iters = gr.Slider(
-            minimum=0,
-            maximum=64,
-            value=0,
-            step=1,
-            label="SR-MPGD iterations (0 = disabled)",
-        )
-
-    btn = gr.Button("Generate QR Art ✨", variant="primary")
-    out = gr.Image(label="Output QR art", type="pil")
-
-    btn.click(
-        fn=infer,
-        inputs=[
-            url,
-            prompt,
-            steps,
-            control_scale,
-            srg_scale,
-            pg_scale,
-            srmpgd_iters,
-        ],
-        outputs=[out],
-    )
-
-demo.launch()

diffqrcoder_wrapper.py

@@ -83,6 +83,7 @@ def load_pipeline():
 
 
 def generate_qr_art(
+    pipe: DiffQRCoderPipeline, 
     url_or_text: str,
     prompt: str,
     neg_prompt: str = "easynegative",
@@ -96,7 +97,6 @@ def generate_qr_art(
     srmpgd_lr: float = 0.1,
     seed: int = 1,
 ) -> Image.Image:
-    pipe = load_pipeline()
 
     generator = torch.Generator(device=DEVICE).manual_seed(seed)