Update app.py

app.py

@@ -1,9 +1,8 @@
-# app.py (CPU-optimized v3)
-# Neurale netwerken als tekenaars — ControlNet (scribble) demo voor CPU
-# Wijzigingen:
-# - Verwijderd: Image(..., tool="sketch") → niet ondersteund in jouw Gradio-versie
-# - Gebruikt gr.Sketchpad (retourneert RGBA-numpy), met robuuste conversie
-# - CPU optimalisaties: 512px, DPMSolver, 12 steps, attention_slicing, single-queue
+# app.py (v4, CPU-optimized + brede Gradio-compat)
+# Neurale netwerken als tekenaars — Stable Diffusion + ControlNet (scribble)
+# - Werkt op CPU (langzamer dan GPU, maar demowaardig)
+# - Gebruikt gr.Sketchpad (compatibel met oudere Gradio-versies)
+# - Geen queue-parameters (demo.queue() zonder args)
 
 import os, random, numpy as np
 from typing import Optional
@@ -16,12 +15,14 @@ from diffusers import (
     DPMSolverMultistepScheduler,
 )
 
+# Modellen (kun je via Space Secrets/Opsional env-vars overriden)
 MODEL_BASE = os.environ.get("SD_BASE_MODEL", "runwayml/stable-diffusion-v1-5")
 MODEL_CN   = os.environ.get("CN_SCRIBBLE_MODEL", "lllyasviel/sd-controlnet-scribble")
 
-DEVICE = "cpu"   # geforceerd CPU
+DEVICE = "cpu"              # geforceerd CPU
 DTYPE  = torch.float32
-TARGET_SIZE = 512  # lager = sneller op CPU
+TARGET_SIZE = 512           # lager = sneller op CPU
+DEFAULT_STEPS = 12          # CPU-vriendelijk
 
 pipe: Optional[StableDiffusionControlNetPipeline] = None
 
@@ -33,9 +34,8 @@ def _lazy_load_pipeline():
     pipe = StableDiffusionControlNetPipeline.from_pretrained(
         MODEL_BASE, controlnet=controlnet, torch_dtype=DTYPE, safety_checker=None
     )
-    # Snellere scheduler (minder stappen nodig)
+    # Snellere scheduler → minder stappen nodig
     pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-    # CPU-optimalisaties
     pipe = pipe.to(DEVICE)
     pipe.enable_attention_slicing()
     return pipe
@@ -45,11 +45,9 @@ def to_rgba(img):
     if img is None:
         return None
     if isinstance(img, np.ndarray):
-        # Sketchpad geeft HxWx4 (RGBA) of HxWx3 terug
         if img.dtype != np.uint8:
             img = img.astype(np.uint8)
-        pil = Image.fromarray(img)
-        return pil.convert("RGBA")
+        return Image.fromarray(img).convert("RGBA")
     if isinstance(img, Image.Image):
         return img.convert("RGBA")
     try:
@@ -58,28 +56,29 @@ def to_rgba(img):
         return None
 
 def rgba_to_scribble(img) -> Image.Image:
-    """Zet RGBA schets om naar zwart-op-wit scribble, vierkant 512x512."""
+    """Zet RGBA schets om naar zwart-op-wit, vierkant en resized (TARGET_SIZE)."""
     img = to_rgba(img)
     if img is None:
         return None
     # transparant → wit
-    bg = Image.new("RGBA", img.size, (255,255,255,255))
+    bg = Image.new("RGBA", img.size, (255, 255, 255, 255))
     img = Image.alpha_composite(bg, img)
     # grijs + drempel
     arr = np.array(img.convert("L"))
-    thr = max(25, int(arr.mean()*0.9))
+    thr = max(25, int(arr.mean() * 0.9))
     lines = arr < thr
-    out = np.full((*arr.shape,3), 255, dtype=np.uint8)
-    out[lines] = (0,0,0)
+    out = np.full((*arr.shape, 3), 255, dtype=np.uint8)
+    out[lines] = (0, 0, 0)
     scribble = Image.fromarray(out, "RGB")
     # vierkant + resize
     m = max(scribble.width, scribble.height)
-    sq = Image.new("RGB", (m,m), (255,255,255))
-    sq.paste(scribble, ((m-scribble.width)//2, (m-scribble.height)//2))
+    sq = Image.new("RGB", (m, m), (255, 255, 255))
+    sq.paste(scribble, ((m - scribble.width) // 2, (m - scribble.height) // 2))
     return sq.resize((TARGET_SIZE, TARGET_SIZE), Image.BICUBIC)
 
-def run(drawing, prompt, negative_prompt, guidance_scale=7.0,
-        controlnet_conditioning_scale=1.0, num_inference_steps=12, seed=-1):
+def run(drawing, prompt, negative_prompt,
+        guidance_scale=7.0, controlnet_conditioning_scale=1.0,
+        num_inference_steps=DEFAULT_STEPS, seed=-1):
     if drawing is None:
         raise gr.Error("Teken eerst iets of gebruik de voorbeeld-schets.")
     cn_image = rgba_to_scribble(drawing)
@@ -89,12 +88,14 @@ def run(drawing, prompt, negative_prompt, guidance_scale=7.0,
     if not prompt or not str(prompt).strip():
         prompt = "clean pencil sketch, coherent completion, subtle shading, high quality"
     if seed is None or int(seed) < 0:
-        seed = random.randint(0, 2**31-1)
+        seed = random.randint(0, 2**31 - 1)
     gen = torch.Generator(device=DEVICE).manual_seed(int(seed))
 
     p = _lazy_load_pipeline()
-    full_prompt = ("black pencil sketch, clean lines, coherent shape completion, subtle shading, "
-                   "consistent with the input scribble, " + prompt)
+    full_prompt = (
+        "black pencil sketch, clean lines, coherent shape completion, subtle shading, "
+        "consistent with the input scribble, " + prompt
+    )
     result = p(
         prompt=full_prompt,
         negative_prompt=negative_prompt or "text, watermark, extra limbs, low quality, distorted face, nsfw",
@@ -106,14 +107,18 @@ def run(drawing, prompt, negative_prompt, guidance_scale=7.0,
     )
     image = result.images[0]
     pad = 12
-    combo = Image.new("RGB", (cn_image.width*2+pad, cn_image.height), (245,245,245))
-    combo.paste(cn_image, (0,0)); combo.paste(image, (cn_image.width+pad, 0))
+    combo = Image.new("RGB", (cn_image.width * 2 + pad, cn_image.height), (245, 245, 245))
+    combo.paste(cn_image, (0, 0))
+    combo.paste(image, (cn_image.width + pad, 0))
     return image, cn_image, combo, f"Seed: {seed}, steps: {num_inference_steps}"
 
 def make_example_scribble() -> Image.Image:
+    """Kleine voorbeeldschets (appelvorm) voor snelle test."""
     w, h = 360, 360
-    img = Image.new("RGBA", (w,h), (0,0,0,0)); d = ImageDraw.Draw(img)
-    d.line([(100,300),(90,230),(110,170),(160,140),(200,140),(260,170),(280,230),(260,300),(180,320),(100,300)],
+    img = Image.new("RGBA", (w, h), (0, 0, 0, 0))
+    d = ImageDraw.Draw(img)
+    d.line([(100,300),(90,230),(110,170),(160,140),(200,140),
+            (260,170),(280,230),(260,300),(180,320),(100,300)],
            fill=(0,0,0,255), width=12)
     d.line([(200,140),(210,110)], fill=(0,0,0,255), width=10)
     d.line([(210,110),(250,120),(265,150)], fill=(0,0,0,255), width=9)
@@ -121,8 +126,9 @@ def make_example_scribble() -> Image.Image:
 
 CSS = ".gradio-container{max-width:1100px} #combo img{border-radius:10px}"
 
-with gr.Blocks(title="🖌️ Neurale netwerken als tekenaars (CPU v3)", css=CSS, theme=gr.themes.Soft()) as demo:
+with gr.Blocks(title="🖌️ Neurale netwerken als tekenaars (CPU v4)", css=CSS) as demo:
     gr.Markdown("## 🖌️ Neurale netwerken als tekenaars (CPU)\nTeken links of klik **Voorbeeld-schets**; AI vult aan met Stable Diffusion + ControlNet (scribble).")
+
     with gr.Row():
         with gr.Column():
             canvas = gr.Sketchpad(label="Schets (teken in zwart op transparant/wit)", height=420)
@@ -131,20 +137,21 @@ with gr.Blocks(title="🖌️ Neurale netwerken als tekenaars (CPU v3)", css=CSS
             with gr.Accordion("Geavanceerd", open=False):
                 guidance_scale = gr.Slider(5.0, 10.0, value=7.0, step=0.5, label="Guidance scale")
                 cn_strength   = gr.Slider(0.6, 1.6, value=1.0, step=0.05, label="ControlNet conditioning scale")
-                steps         = gr.Slider(6, 25, value=12, step=1, label="Aantal diffusion-steps")
+                steps         = gr.Slider(6, 25, value=DEFAULT_STEPS, step=1, label="Aantal diffusion-steps")
                 seed          = gr.Number(value=-1, precision=0, label="Seed (-1 = random)")
             with gr.Row():
                 run_btn = gr.Button("Vervolledig ✨", variant="primary")
                 example_btn = gr.Button("Voorbeeld-schets", variant="secondary")
+
         with gr.Column():
-            out_img   = gr.Image(label="Vervolledigde tekening", interactive=False)
-            cn_prev   = gr.Image(label="Scribble (input voor AI)", interactive=False)
-            combo     = gr.Image(label="Vergelijking (links: input, rechts: AI)", elem_id="combo")
-            meta      = gr.Markdown()
+            out_img = gr.Image(label="Vervolledigde tekening", interactive=False)
+            cn_prev = gr.Image(label="Scribble (input voor AI)", interactive=False)
+            combo   = gr.Image(label="Vergelijking (links: input, rechts: AI)", elem_id="combo")
+            meta    = gr.Markdown()
 
     def fill_and_run(p, n, gs, cs, st, sd):
         img = make_example_scribble()
-        a,b,c,d = run(img, p, n, gs, cs, st, sd)
+        a, b, c, d = run(img, p, n, gs, cs, st, sd)
         return img, a, b, c, d
 
     run_btn.click(run, inputs=[canvas, prompt, negative_prompt, guidance_scale, cn_strength, steps, seed],
@@ -152,7 +159,7 @@ with gr.Blocks(title="🖌️ Neurale netwerken als tekenaars (CPU v3)", css=CSS
     example_btn.click(fill_and_run, inputs=[prompt, negative_prompt, guidance_scale, cn_strength, steps, seed],
                       outputs=[canvas, out_img, cn_prev, combo, meta])
 
-# Belangrijk op CPU: geen parallelle jobs
-demo.queue(concurrency_count=1, max_size=8)
+demo.queue()  # zonder parameters: compatibel met oudere Gradio-versies
+
 if __name__ == "__main__":
     demo.launch()