loras: match VAE input dtype in Z-Image trainer (bf16 pipeline VAE)

QLoRA training reached VAE encode then failed: the ZImagePipeline loads the VAE in
bf16, but the image tensor was fed as float32 -> conv2d "Input type (float) and bias
type (BFloat16) should be the same". Feed the VAE its own weight dtype.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RdMufYvtTbtGDWsiZVoXce

codai/__init__.py

@@ -16,7 +16,7 @@
 
 # Canonical product version for CoderAI — single source of truth. Both the API
 # metadata and the admin web UI read from here.
-__version__ = "0.1.20"
+__version__ = "0.1.21"
 
 # Configure the CUDA caching allocator BEFORE torch is imported anywhere.
 # expandable_segments lets the allocator return freed pages to the driver even

codai/api/loras.py

@@ -1509,6 +1509,13 @@ def _train_dit(req, base_path, images, instance_prompt,
     # ── VAE encode reference images → scaled latents ───────────────────────────
     _set_progress(status="preparing", message="encoding reference images (VAE)")
     vae = _to_dev(vae); vae.requires_grad_(False); vae.eval()
+    # The pipeline loads the VAE in the pipeline dtype (bf16); the input image tensor
+    # must match the VAE's weight dtype or conv2d raises "Input type (float) and bias
+    # type (BFloat16) should be the same".
+    try:
+        _vdt = next(vae.parameters()).dtype
+    except Exception:
+        _vdt = compute_dtype
     sf = float(getattr(vae.config, "scaling_factor", 1.0) or 1.0)
     shf = float(getattr(vae.config, "shift_factor", 0.0) or 0.0)
     spatial = (int(resolution) // 16) * 16
@@ -1521,7 +1528,7 @@ def _train_dit(req, base_path, images, instance_prompt,
     latents_list = []
     with torch.no_grad():
         for img in images:
-            px = tfm(img.convert("RGB")).unsqueeze(0).to(device, dtype=torch.float32)
+            px = tfm(img.convert("RGB")).unsqueeze(0).to(device, dtype=_vdt)
             lat = vae.encode(px).latent_dist.sample()
             lat = (lat - shf) * sf
             latents_list.append(lat.to(compute_dtype).cpu())