Fix Helios Context Parallelism (#13223)

* fix Helios Context Parallelism

* refacotr

* make style and quality

Author: SHYuanBest

src/diffusers/models/transformers/transformer_helios.py

@@ -556,14 +556,21 @@ class HeliosTransformer3DModel(
     _keys_to_ignore_on_load_unexpected = ["norm_added_q"]
     _repeated_blocks = ["HeliosTransformerBlock"]
     _cp_plan = {
-        "blocks.0": {
+        # Input split at attn level and ffn level.
+        "blocks.*.attn1": {
             "hidden_states": ContextParallelInput(split_dim=1, expected_dims=3, split_output=False),
-        },
-        "blocks.*": {
-            "temb": ContextParallelInput(split_dim=1, expected_dims=4, split_output=False),
             "rotary_emb": ContextParallelInput(split_dim=1, expected_dims=3, split_output=False),
         },
-        "blocks.39": ContextParallelOutput(gather_dim=1, expected_dims=3),
+        "blocks.*.attn2": {
+            "hidden_states": ContextParallelInput(split_dim=1, expected_dims=3, split_output=False),
+        },
+        "blocks.*.ffn": {
+            "hidden_states": ContextParallelInput(split_dim=1, expected_dims=3, split_output=False),
+        },
+        # Output gather at attn level and ffn level.
+        **{f"blocks.{i}.attn1": ContextParallelOutput(gather_dim=1, expected_dims=3) for i in range(40)},
+        **{f"blocks.{i}.attn2": ContextParallelOutput(gather_dim=1, expected_dims=3) for i in range(40)},
+        **{f"blocks.{i}.ffn": ContextParallelOutput(gather_dim=1, expected_dims=3) for i in range(40)},
     }
 
     @register_to_config

src/diffusers/pipelines/helios/pipeline_helios_pyramid.py

@@ -449,7 +449,14 @@ def sample_block_noise(
         width,
         patch_size: tuple[int, ...] = (1, 2, 2),
         device: torch.device | None = None,
+        generator: torch.Generator | None = None,
     ):
+        # NOTE: A generator must be provided to ensure correct and reproducible results.
+        # Creating a default generator here is a fallback only — without a fixed seed,
+        # the output will be non-deterministic and may produce incorrect results in CP context.
+        if generator is None:
+            generator = torch.Generator(device=device)
+
         gamma = self.scheduler.config.gamma
         _, ph, pw = patch_size
         block_size = ph * pw
@@ -458,13 +465,17 @@ def sample_block_noise(
             torch.eye(block_size, device=device) * (1 + gamma)
             - torch.ones(block_size, block_size, device=device) * gamma
         )
-        cov += torch.eye(block_size, device=device) * 1e-6
-        dist = torch.distributions.MultivariateNormal(torch.zeros(block_size, device=device), covariance_matrix=cov)
+        cov += torch.eye(block_size, device=device) * 1e-8
+        cov = cov.float()  # Upcast to fp32 for numerical stability — cholesky is unreliable in fp16/bf16.
+
+        L = torch.linalg.cholesky(cov)
         block_number = batch_size * channel * num_frames * (height // ph) * (width // pw)
+        z = torch.randn(block_number, block_size, device=device, generator=generator)
+        noise = z @ L.T
 
-        noise = dist.sample((block_number,))  # [block number, block_size]
         noise = noise.view(batch_size, channel, num_frames, height // ph, width // pw, ph, pw)
         noise = noise.permute(0, 1, 2, 3, 5, 4, 6).reshape(batch_size, channel, num_frames, height, width)
+
         return noise
 
     @property
@@ -918,7 +929,14 @@ def __call__(
 
                         batch_size, channel, num_frames, pyramid_height, pyramid_width = latents.shape
                         noise = self.sample_block_noise(
-                            batch_size, channel, num_frames, pyramid_height, pyramid_width, patch_size, device
+                            batch_size,
+                            channel,
+                            num_frames,
+                            pyramid_height,
+                            pyramid_width,
+                            patch_size,
+                            device,
+                            generator,
                         )
                         noise = noise.to(device=device, dtype=transformer_dtype)
                         latents = alpha * latents + beta * noise  # To fix the block artifact