Real per-step progress bars for local GPU inference

Backend:
- local_diffusion.py: add _make_step_cb() that writes step/total_steps/
  progress into _states on every diffusers callback_on_step_end; wired into
  txt2img, inpaint, img2img with TypeError fallback for older diffusers
- ai_tools.py: GET /api/generate/progress SSE endpoint — streams _states
  as JSON array every 200ms so clients get live denoising step counts

Frontend:
- progress_overlay.js: add connectProgressSSE(pipeType, baseUrl) /
  disconnectProgressSSE() — opens EventSource, maps step/total_steps
  to bar percentage (0→85% during denoising, 85→100 for decode/place)
- text_to_image.js: connect SSE before POST, disconnect on done/error
- selection_actions.js: connect SSE for AI edit / asymmetry operations

Result: for local GPU, progress bar shows "Step 12 / 30" with exact fill;
for remote providers and upscale (no step callbacks), shimmer animates.

https://claude.ai/code/session_01WVDg7amsy1TTtxvpku7bcM

backend/app/routers/ai_tools.py

@@ -4,10 +4,12 @@ All endpoints are under /api prefix.
 """
 
 from fastapi import APIRouter, HTTPException
+from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 from typing import Optional
 import base64
 import asyncio
+import json
 
 from app.services.local_inpaint import (
     lama_inpaint, opencv_inpaint, lama_available, gpu_available, rembg_available,
@@ -191,6 +193,37 @@ async def inpaint_remote(req: InpaintRemoteRequest):
         raise HTTPException(status_code=500, detail=str(e))
 
 
+@router.get("/generate/progress")
+async def generation_progress_stream():
+    """
+    SSE stream of local GPU pipeline inference progress.
+    Events are JSON arrays of pipeline state objects, emitted every 200 ms.
+    Each object: {pipeline, state, step, total_steps, progress, message, model_id, …}
+    Clients open this with EventSource before firing a generation POST,
+    then close it when the POST resolves.
+    """
+    from app.services.local_diffusion import get_all_model_states
+
+    async def event_gen():
+        try:
+            while True:
+                states = get_all_model_states()
+                yield f"data: {json.dumps(states)}\n\n"
+                await asyncio.sleep(0.2)
+        except asyncio.CancelledError:
+            pass
+
+    return StreamingResponse(
+        event_gen(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "X-Accel-Buffering": "no",
+            "Connection": "keep-alive",
+        },
+    )
+
+
 @router.post("/generate/txt2img")
 async def txt2img(req: Txt2ImgRequest):
     """Text-to-image via configured remote provider."""