Add BEN2 and BiRefNet-HR as selectable Remove Background models

BEN2 becomes the new default local backend (clean cutouts, strong on
hair/fur edges), with BiRefNet-HR available as a high-res/print
alternate and U2Net kept as the lightweight fallback. Both are
MIT-licensed and download weights from HuggingFace on first use
(cached via the existing hf_cache bind mount), unlike U2Net/SAM which
need an explicit download script.

- config: new BG_REMOVAL_MODEL setting (default "ben2")
- tools.py: remove-background-base64 now tries local backends in
  order (request.model override > BG_REMOVAL_MODEL > ben2/u2net),
  falling back to rembg's birefnet-general session as a last resort
- requirements.gpu.txt / Dockerfile.gpu: add ben2 + transformers deps
  needed for the new backends, with a build-time smoke test for ben2
- frontend: model dropdown in the Remove Background dialog, threaded
  through api.js to the new request field

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ro4PwQKvSc3CH19LSN21Ht

frontend/src/js/modules/image/remove_background.js

@@ -49,6 +49,11 @@ class Image_remove_background_class {
             title: 'Remove Background',
             params: [
                 { name: "info", title: "AI will detect the main subject and remove the background.", type: "label" },
+                {
+                    name: "model", title: "Model:", value: "auto", type: "select",
+                    values: ["auto", "ben2", "birefnet-hr", "u2net"],
+                    comment: "auto = best available (BEN2 by default). BiRefNet-HR is slower but sharper on high-res/print work.",
+                },
                 { name: "new_layer", title: "Create as new layer:", value: true },
                 { name: "trim_result", title: "Trim transparent edges:", value: false },
             ],
@@ -74,7 +79,7 @@ class Image_remove_background_class {
             var imageData = canvas.toDataURL('image/png').split(',')[1];
 
             // Call backend API
-            var result = await apiService.removeBackground(imageData);
+            var result = await apiService.removeBackground(imageData, params.model);
 
             // Create image from result
             var resultImage = new Image();

frontend/src/js/services/api.js

@@ -72,11 +72,12 @@ class ApiService {
     }
 
     /**
-     * Remove background from image using AI (rembg)
+     * Remove background from image using AI (BEN2 / BiRefNet-HR / U2Net / rembg)
      * @param {string} imageData - Base64 encoded image data
-     * @returns {Promise<{result: string, width: number, height: number}>} - Base64 encoded result with transparency
+     * @param {string} [model='auto'] - "auto", "ben2", "birefnet-hr", "u2net", or "rembg"
+     * @returns {Promise<{result: string, width: number, height: number, method: string}>} - Base64 encoded result with transparency
      */
-    async removeBackground(imageData) {
+    async removeBackground(imageData, model = 'auto') {
         const response = await fetch(`${this.baseUrl}/tools/remove-background-base64`, {
             method: 'POST',
             headers: {
@@ -84,6 +85,7 @@ class ApiService {
             },
             body: JSON.stringify({
                 image: imageData,
+                model: model,
             }),
         });