feat: live loss curve preview during training

- Send updated loss curve to ComfyUI frontend every 50 steps via
  pbar_train.update_absolute() with a JPEG preview tuple — same
  mechanism as KSampler's denoising previews.
- Fix x-axis step labels for resumed runs (previously always started
  at 0; now correctly shows start_step + offset).
- Split _draw_loss_curve (returns PIL Image) from _pil_to_tensor
  (converts for ComfyUI IMAGE output).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

nodes/selva_lora_trainer.py

@@ -78,16 +78,17 @@ def _load_npz(path: Path) -> dict:
 # Loss curve rendering
 # ---------------------------------------------------------------------------
 
-def _draw_loss_curve(losses: list[float], log_interval: int) -> torch.Tensor:
-    """Render a loss curve as a [1, H, W, 3] float32 IMAGE tensor for ComfyUI."""
+def _draw_loss_curve(losses: list[float], log_interval: int,
+                     start_step: int = 0) -> Image.Image:
+    """Render a loss curve as a PIL Image."""
     W, H = 800, 380
-    pl, pr, pt, pb = 70, 20, 25, 45   # plot margins
+    pl, pr, pt, pb = 70, 20, 25, 45
 
     img  = Image.new("RGB", (W, H), (255, 255, 255))
     draw = ImageDraw.Draw(img)
 
-    pw = W - pl - pr   # plot area width
-    ph = H - pt - pb   # plot area height
+    pw = W - pl - pr
+    ph = H - pt - pb
 
     if len(losses) >= 2:
         lo, hi = min(losses), max(losses)
@@ -111,11 +112,12 @@ def _draw_loss_curve(losses: list[float], log_interval: int) -> torch.Tensor:
             pts.append((x, y))
         draw.line(pts, fill=(66, 133, 244), width=2)
 
-        # x-axis step labels
-        total_steps = n * log_interval
+        # x-axis step labels — account for start_step so resumed runs are correct
+        first_step = start_step + log_interval
+        last_step  = start_step + n * log_interval
         for i in range(5):
             x    = pl + int(i * pw / 4)
-            step = int(i * total_steps / 4)
+            step = int(first_step + i * (last_step - first_step) / 4)
             draw.text((x - 12, H - pb + 5), str(step), fill=(120, 120, 120))
 
     # Axes
@@ -123,8 +125,13 @@ def _draw_loss_curve(losses: list[float], log_interval: int) -> torch.Tensor:
     draw.line([(pl, H - pb), (W - pr, H - pb)], fill=(40, 40, 40), width=1)
     draw.text((pl + 4, 5), "Training Loss", fill=(40, 40, 40))
 
+    return img
+
+
+def _pil_to_tensor(img: Image.Image) -> torch.Tensor:
+    """Convert a PIL Image to a [1, H, W, 3] float32 IMAGE tensor for ComfyUI."""
     arr = np.array(img).astype(np.float32) / 255.0
-    return torch.from_numpy(arr).unsqueeze(0)  # [1, H, W, 3]
+    return torch.from_numpy(arr).unsqueeze(0)
 
 
 # ---------------------------------------------------------------------------
@@ -382,6 +389,12 @@ class SelvaLoraTrainer:
                       f"loss={avg:.4f}  lr={lr_now:.2e}", flush=True)
                 running_loss = 0.0
 
+                # Live preview: send updated loss curve to ComfyUI frontend
+                preview_img = _draw_loss_curve(loss_history, log_interval, start_step)
+                pbar_train.update_absolute(
+                    step - start_step, remaining, ("JPEG", preview_img, 85)
+                )
+
             if step % save_every == 0 or step == steps:
                 ckpt_path = output_dir / f"adapter_step{step:05d}.pt"
                 torch.save({
@@ -406,6 +419,6 @@ class SelvaLoraTrainer:
         generator.to(next(model["generator"].parameters()).device)
         patched = {**model, "generator": generator}
 
-        loss_curve = _draw_loss_curve(loss_history, log_interval)
+        loss_curve = _pil_to_tensor(_draw_loss_curve(loss_history, log_interval, start_step))
 
         return (patched, str(final_path), loss_curve)