Refactor code structure for improved readability and maintainability

workers/inpaint/handler.py

@@ -0,0 +1,137 @@
+"""
+RunPod Serverless worker: SDXL inpainting.
+
+Powers the app's Magic Eraser, Generative Fill AND Outpaint — all three go
+through this one endpoint (RUNPOD_SD_INPAINT_URL). No app change needed; this
+matches the exact contract the app already sends (rpInpaint):
+
+  {"input": {
+     "image": "<base64>",          # required (raw base64 or data: URI)
+     "mask":  "<base64>",          # required — WHITE = regenerate, BLACK = keep
+     "prompt": "<text>",           # optional ("" = Magic Eraser: clean fill)
+     "strength": 0.8,              # optional
+     "guidance_scale": 7,          # optional
+     "num_inference_steps": 35,    # optional
+     "negative_prompt": "...",     # optional
+     "seed": 123                   # optional
+  }}
+Returns: {"image": "<base64 PNG>"}  (or {"error": "..."}).
+
+CUDA 12.1 base + torch cu121 (same as the detect/upscale workers) so it runs on
+RunPod's RTX 3090/4090 (driver 550) hosts.
+"""
+
+import base64
+import io
+import os
+
+_V = "/runpod-volume"
+if os.path.isdir(_V):
+    os.environ.setdefault("HF_HOME", os.path.join(_V, "huggingface"))
+
+import torch  # noqa: E402
+import runpod  # noqa: E402
+from PIL import Image  # noqa: E402
+from diffusers import AutoPipelineForInpainting  # noqa: E402
+
+MODEL = os.environ.get("SD_INPAINT_MODEL", "diffusers/stable-diffusion-xl-1.0-inpainting-0.1")
+MAX_SIZE = int(os.environ.get("SD_INPAINT_MAX_SIZE", "1024"))
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+_pipe = None
+
+
+def _get_pipe():
+    global _pipe
+    if _pipe is None:
+        try:
+            pipe = AutoPipelineForInpainting.from_pretrained(
+                MODEL, torch_dtype=torch.float16, variant="fp16"
+            )
+        except Exception:
+            # Repo may not ship an fp16 variant — fall back to default weights.
+            pipe = AutoPipelineForInpainting.from_pretrained(MODEL, torch_dtype=torch.float16)
+        total_gb = torch.cuda.get_device_properties(0).total_memory / 1e9 if DEVICE == "cuda" else 0
+        if total_gb >= 20:
+            pipe = pipe.to("cuda")
+        else:
+            pipe.enable_model_cpu_offload()
+        pipe.set_progress_bar_config(disable=True)
+        _pipe = pipe
+    return _pipe
+
+
+def _b64_to_image(data, mode="RGB"):
+    if "," in data:
+        data = data.split(",", 1)[1]
+    return Image.open(io.BytesIO(base64.b64decode(data))).convert(mode)
+
+
+def _image_to_b64(img):
+    buf = io.BytesIO()
+    img.save(buf, format="PNG")
+    return base64.b64encode(buf.getvalue()).decode("utf-8")
+
+
+def _fit(img):
+    # SDXL wants multiples of 8; cap the long side at MAX_SIZE.
+    w, h = img.size
+    scale = min(1.0, MAX_SIZE / max(w, h))
+    nw = max(8, (int(w * scale) // 8) * 8)
+    nh = max(8, (int(h * scale) // 8) * 8)
+    return img.resize((nw, nh), Image.LANCZOS)
+
+
+def handler(event):
+    inp = (event or {}).get("input") or {}
+    image_b64 = inp.get("image")
+    mask_b64 = inp.get("mask")
+    if not image_b64:
+        return {"error": "Missing 'image' (base64)."}
+    if not mask_b64:
+        return {"error": "Missing 'mask' (base64)."}
+
+    prompt = (inp.get("prompt") or "").strip()
+    negative = (inp.get("negative_prompt") or "").strip() or None
+
+    try:
+        image = _fit(_b64_to_image(image_b64, "RGB"))
+        mask = _b64_to_image(mask_b64, "L").resize(image.size, Image.NEAREST)
+
+        steps = int(inp.get("num_inference_steps") or 35)
+        guidance = float(inp.get("guidance_scale") or 7.0)
+
+        # Strength: empty prompt = Magic Eraser -> fully replace (1.0). With a
+        # prompt (Generative Fill / Outpaint) respect the caller but keep a floor
+        # so the masked region actually changes.
+        raw = inp.get("strength")
+        if not prompt:
+            strength = 1.0
+        else:
+            strength = max(0.7, min(1.0, float(raw))) if raw is not None else 0.9
+
+        seed = inp.get("seed")
+        generator = (
+            torch.Generator(device=DEVICE).manual_seed(int(seed)) if seed is not None else None
+        )
+
+        eff_prompt = prompt or "clean, seamless, photorealistic background, natural continuation"
+
+        result = _get_pipe()(
+            prompt=eff_prompt,
+            negative_prompt=negative,
+            image=image,
+            mask_image=mask,
+            height=image.height,
+            width=image.width,
+            num_inference_steps=steps,
+            guidance_scale=guidance,
+            strength=strength,
+            generator=generator,
+        ).images[0]
+
+        return {"image": _image_to_b64(result)}
+    except Exception as exc:
+        return {"error": f"{type(exc).__name__}: {exc}"}
+
+
+runpod.serverless.start({"handler": handler})