Enable VAE tiling for Qwen-Image-2.1 (#5)

* init

Signed-off-by: Isotr0py <mozf@inferact.ai>

* fix

Signed-off-by: Isotr0py <mozf@inferact.ai>

* test(diffusion): add Qwen-Image-2.1 unit tests

Signed-off-by: Isotr0py <mozf@inferact.ai>

* fix(diffusion): Qwen-Image-2.1 VAE spatial ratio, tiling e2e verification

- AutoencoderKLQwenImage21: derive spatial_compression_ratio from the
  architecture (2 ** len(temperal_downsample) = 16x) instead of trusting
  the checkpoint's scale_factor_spatial=8, which corrupted tiled
  decode/encode geometry (output cropped to half size) and tiling gating.
- pipeline: _read_vae_scale_factor prefers the architecture-derived value
  for the same reason.
- models/qwen_image_21/__init__: lazy exports to break the circular import
  distributed VAE -> package __init__ -> pipeline -> distributed VAE.
- Add VAE unit tests: ratio derivation, tiled vs untiled decode/encode
  consistency, distributed tile_split/exec/merge == tiled_decode.
- e2e matrix (GB200, seed 42, 20 steps) all green; see e2e_out/qwen21_vae/.
- examples: qwen_image_21_edit.py gains --vae-use-tiling.
- docs: mark VAE patch-parallel verified for Qwen-Image-2.1; recipe notes
  the default-tile banding limitation at 2048px+.

* clean tests

Signed-off-by: Isotr0py <mozf@inferact.ai>

* fix cudnn determination

Signed-off-by: Isotr0py <mozf@inferact.ai>

---------

Signed-off-by: Isotr0py <mozf@inferact.ai>

vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage21.py

@@ -15,6 +15,7 @@ from vllm_omni.diffusion.distributed.autoencoders.distributed_vae_executor impor
 )
 from vllm_omni.diffusion.models.qwen_image_21.autoencoder_kl_qwenimage21 import (
     AutoencoderKLQwenImage21,
+    _cudnn_deterministic,
     _unpatchify,
 )
 
@@ -89,11 +90,12 @@ class DistributedAutoencoderKLQwenImage21(AutoencoderKLQwenImage21, DistributedV
         """Decode a single latent tile into RGB space."""
         self.clear_cache()
         time = []
-        for k in range(len(task.tensor)):
-            self._conv_idx = [0]
-            tile = self.post_quant_conv(task.tensor[k])
-            decoded = self.decoder(tile, feat_cache=self._feat_map, feat_idx=self._conv_idx, first_chunk=(k == 0))
-            time.append(decoded)
+        with _cudnn_deterministic():
+            for k in range(len(task.tensor)):
+                self._conv_idx = [0]
+                tile = self.post_quant_conv(task.tensor[k])
+                decoded = self.decoder(tile, feat_cache=self._feat_map, feat_idx=self._conv_idx, first_chunk=(k == 0))
+                time.append(decoded)
         result = torch.cat(time, dim=2)
         return result