feat: multi-view image input UI and full-stack plumbing

api/routers/generation.py

@@ -2,7 +2,7 @@
 import threading
 import traceback
 import uuid
-from typing import Dict
+from typing import Dict, List
 from fastapi import APIRouter, File, Form, UploadFile, HTTPException, BackgroundTasks
 from services.generators.base import smooth_progress
 
@@ -18,7 +18,8 @@
 @router.post("/from-image")
 async def generate_from_image(
     background_tasks: BackgroundTasks,
-    image: UploadFile = File(...),
+    image: List[UploadFile] = File(...),
+    view_labels: str = Form(""),
     model_id: str = Form("sf3d"),
     collection: str = Form("Default"),
     vertex_count: int = Form(10000),
@@ -30,8 +31,9 @@ async def generate_from_image(
     seed: int = Form(-1),
     num_inference_steps: int = Form(30),
 ):
-    if not image.content_type or not image.content_type.startswith("image/"):
-        raise HTTPException(400, "File must be an image")
+    for img in image:
+        if not img.content_type or not img.content_type.startswith("image/"):
+            raise HTTPException(400, "All files must be images")
 
     if remesh not in ("quad", "triangle", "none"):
         raise HTTPException(400, "remesh must be 'quad', 'triangle', or 'none'")
@@ -56,8 +58,12 @@ async def generate_from_image(
 
     generator_registry.switch_model(model_id)
 
-    job_id      = str(uuid.uuid4())
-    image_bytes = await image.read()
+    job_id = str(uuid.uuid4())
+    image_bytes_list = [await img.read() for img in image]
+    # Pass single bytes for backward compat, list for multi-view
+    image_data = image_bytes_list[0] if len(image_bytes_list) == 1 else image_bytes_list
+    # Parse view labels (e.g. "front,back" → ["front", "back"])
+    parsed_view_labels = [v.strip() for v in view_labels.split(",") if v.strip()] if view_labels else []
     params      = {
         "vertex_count":       vertex_count,
         "remesh":             remesh,
@@ -67,12 +73,13 @@ async def generate_from_image(
         "guidance_scale":       guidance_scale,
         "seed":                 seed,
         "num_inference_steps":  num_inference_steps,
+        "view_labels":          parsed_view_labels,
     }
 
     job = JobStatus(job_id=job_id, status="pending", progress=0)
     _jobs[job_id] = job
 
-    background_tasks.add_task(_run_generation, job_id, image_bytes, params, collection)
+    background_tasks.add_task(_run_generation, job_id, image_data, params, collection)
 
     return {"job_id": job_id}
 
@@ -86,7 +93,7 @@ async def job_status(job_id: str):
     return job
 
 
-async def _run_generation(job_id: str, image_bytes: bytes, params: dict, collection: str = "Default") -> None:
+async def _run_generation(job_id: str, image_bytes, params: dict, collection: str = "Default") -> None:
     job = _jobs[job_id]
     job.status = "running"
 

api/services/generators/base.py

@@ -4,7 +4,7 @@
 from abc import ABC, abstractmethod
 import threading
 from pathlib import Path
-from typing import Callable, Optional
+from typing import Callable, List, Optional, Union
 
 
 def smooth_progress(
@@ -84,12 +84,13 @@ def is_loaded(self) -> bool:
     @abstractmethod
     def generate(
         self,
-        image_bytes: bytes,
+        image_bytes: Union[bytes, List[bytes]],
         params: dict,
         progress_cb: Optional[Callable[[int, str], None]] = None,
     ) -> Path:
         """
-        Starts 3D generation from an image.
+        Starts 3D generation from one or more images.
+        Pass a single bytes for single-view, or List[bytes] for multi-view.
         Returns the path to the generated .glb file.
         progress_cb(percent: int, step_label: str)
         """

api/services/generators/hunyuan3d.py

@@ -19,7 +19,7 @@
 import uuid
 import zipfile
 from pathlib import Path
-from typing import Callable, Optional
+from typing import Callable, List, Optional, Union
 
 from PIL import Image
 
@@ -84,7 +84,7 @@ def unload(self) -> None:
 
     def generate(
         self,
-        image_bytes: bytes,
+        image_bytes: Union[bytes, List[bytes]],
         params: dict,
         progress_cb: Optional[Callable[[int, str], None]] = None,
     ) -> Path:
@@ -93,9 +93,23 @@ def generate(
         num_steps  = int(params.get("num_inference_steps", 50))
         vert_count = int(params.get("vertex_count", 0))
 
-        # Step 1 — background removal
-        self._report(progress_cb, 5, "Removing background…")
-        image = self._preprocess(image_bytes)
+        # Step 1 — background removal (single or multi-view)
+        view_labels = params.get("view_labels", [])
+        is_multiview = isinstance(image_bytes, list) and len(image_bytes) > 1
+        if is_multiview:
+            self._report(progress_cb, 5, f"Removing backgrounds ({len(image_bytes)} images)…")
+            processed_images = [self._preprocess(ib) for ib in image_bytes]
+            if view_labels and len(view_labels) == len(processed_images):
+                image = {label: img for label, img in zip(view_labels, processed_images)}
+            else:
+                fallback_keys = ["front", "left", "back", "right"]
+                image = {fallback_keys[i]: img for i, img in enumerate(processed_images[:4])}
+        elif isinstance(image_bytes, list):
+            self._report(progress_cb, 5, "Removing background…")
+            image = self._preprocess(image_bytes[0])
+        else:
+            self._report(progress_cb, 5, "Removing background…")
+            image = self._preprocess(image_bytes)
 
         # Step 2 — shape generation (long, no internal callbacks)
         self._report(progress_cb, 12, "Generating 3D shape…")

api/services/generators/hunyuan3d_mini.py

@@ -17,7 +17,7 @@
 import uuid
 import zipfile
 from pathlib import Path
-from typing import Callable, Optional
+from typing import Callable, List, Optional, Union
 
 from PIL import Image
 
@@ -83,7 +83,7 @@ def unload(self) -> None:
 
     def generate(
         self,
-        image_bytes: bytes,
+        image_bytes: Union[bytes, List[bytes]],
         params: dict,
         progress_cb: Optional[Callable[[int, str], None]] = None,
     ) -> Path:
@@ -96,9 +96,23 @@ def generate(
         guidance_scale   = float(params.get("guidance_scale", 5.5))
         seed             = int(params.get("seed", -1))
 
-        # Step 1 — background removal
-        self._report(progress_cb, 5, "Removing background…")
-        image = self._preprocess(image_bytes)
+        # Step 1 — background removal (single or multi-view)
+        view_labels = params.get("view_labels", [])
+        is_multiview = isinstance(image_bytes, list) and len(image_bytes) > 1
+        if is_multiview:
+            self._report(progress_cb, 5, f"Removing backgrounds ({len(image_bytes)} images)…")
+            processed_images = [self._preprocess(ib) for ib in image_bytes]
+            if view_labels and len(view_labels) == len(processed_images):
+                image = {label: img for label, img in zip(view_labels, processed_images)}
+            else:
+                fallback_keys = ["front", "left", "back", "right"]
+                image = {fallback_keys[i]: img for i, img in enumerate(processed_images[:4])}
+        elif isinstance(image_bytes, list):
+            self._report(progress_cb, 5, "Removing background…")
+            image = self._preprocess(image_bytes[0])
+        else:
+            self._report(progress_cb, 5, "Removing background…")
+            image = self._preprocess(image_bytes)
 
         # Step 2 — shape generation
         # If texture is enabled, reserve 5-70% for shape and 70-95% for texture

api/services/generators/sf3d.py

@@ -9,7 +9,7 @@
 import uuid
 import zipfile
 from pathlib import Path
-from typing import Callable, Optional
+from typing import Callable, List, Optional, Union
 
 from PIL import Image
 
@@ -69,12 +69,16 @@ def load(self) -> None:
 
     def generate(
         self,
-        image_bytes: bytes,
+        image_bytes: Union[bytes, List[bytes]],
         params: dict,
         progress_cb: Optional[Callable[[int, str], None]] = None,
     ) -> Path:
         import torch
 
+        # SF3D only supports single-image input; use first image if list provided
+        if isinstance(image_bytes, list):
+            image_bytes = image_bytes[0]
+
         vertex_count = int(params.get("vertex_count", 10000))
         remesh       = str(params.get("remesh", "quad"))
 

src/areas/generate/GeneratePage.tsx

@@ -7,9 +7,10 @@ import WorkspacePanel from './components/WorkspacePanel'
 import Viewer3D from './components/Viewer3D'
 
 export default function GeneratePage(): JSX.Element {
-  const selectedImagePath = useAppStore((s) => s.selectedImagePath)
+  const viewImages = useAppStore((s) => s.viewImages)
   const { currentJob, startGeneration } = useGeneration()
   const isGenerating = currentJob?.status === 'uploading' || currentJob?.status === 'generating'
+  const hasFrontImage = !!viewImages.front
 
   return (
     <>
@@ -23,8 +24,8 @@ export default function GeneratePage(): JSX.Element {
         {/* Sticky bottom: Generate button */}
         <div className="p-4 border-t border-zinc-800">
           <button
-            onClick={() => selectedImagePath && startGeneration(selectedImagePath)}
-            disabled={!selectedImagePath || isGenerating}
+            onClick={() => hasFrontImage && startGeneration()}
+            disabled={!hasFrontImage || isGenerating}
             className="w-full py-2.5 rounded-lg text-sm font-semibold bg-accent hover:bg-accent-dark disabled:opacity-40 disabled:cursor-not-allowed text-white transition-colors"
           >
             {isGenerating ? 'Generating…' : 'Generate 3D Model'}