From 96d0cfe0d7c061f38c5e070e49878fa69c7ede66 Mon Sep 17 00:00:00 2001
From: Yousef Rafat <81116377+yousef-rafat@users.noreply.github.com>
Date: Fri, 8 May 2026 20:02:09 +0300
Subject: [PATCH] .

---
 comfy/sample.py | 44 --------------------------------------------
 1 file changed, 44 deletions(-)

diff --git a/comfy/sample.py b/comfy/sample.py
index 878c4e984..653829582 100644
--- a/comfy/sample.py
+++ b/comfy/sample.py
@@ -7,50 +7,6 @@ import logging
 import comfy.nested_tensor
 
 def prepare_noise_inner(latent_image, generator, noise_inds=None):
-    coord_counts = getattr(latent_image, "trellis_coord_counts", None)
-    if coord_counts is not None:
-        if coord_counts.ndim != 1:
-            raise ValueError(f"Trellis2 coord_counts must be 1D, got shape {tuple(coord_counts.shape)}")
-        if coord_counts.shape[0] != latent_image.size(0):
-            raise ValueError(
-                f"Trellis2 coord_counts length {coord_counts.shape[0]} does not match latent batch {latent_image.size(0)}"
-            )
-        if (coord_counts < 0).any() or (coord_counts > latent_image.size(2)).any():
-            raise ValueError(
-                f"Trellis2 coord_counts must be within [0, {latent_image.size(2)}], got {coord_counts.tolist()}"
-            )
-        noise = torch.zeros(latent_image.size(), dtype=torch.float32, layout=latent_image.layout, device="cpu")
-        if noise_inds is None:
-            noise_inds = np.arange(latent_image.size(0), dtype=np.int64)
-        else:
-            noise_inds = np.asarray(noise_inds, dtype=np.int64)
-            if noise_inds.shape[0] != latent_image.size(0):
-                raise ValueError(
-                    f"Trellis2 noise_inds length {noise_inds.shape[0]} does not match latent batch {latent_image.size(0)}"
-                )
-
-        base_seed = int(generator.initial_seed())
-        unique_inds = np.unique(noise_inds)
-        sample_noises = {}
-        for noise_index in unique_inds.tolist():
-            rows = np.flatnonzero(noise_inds == noise_index)
-            max_count = max(int(coord_counts[row].item()) for row in rows.tolist())
-            local_generator = torch.Generator(device="cpu")
-            local_generator.manual_seed(base_seed + int(noise_index))
-            sample_noises[int(noise_index)] = torch.randn(
-                [1, latent_image.size(1), max_count, latent_image.size(3)],
-                dtype=torch.float32,
-                layout=latent_image.layout,
-                generator=local_generator,
-                device="cpu",
-            )
-
-        for batch_index, noise_index in enumerate(noise_inds.tolist()):
-            count = int(coord_counts[batch_index].item())
-            sample_noise = sample_noises[int(noise_index)]
-            noise[batch_index:batch_index + 1, :, :count, :] = sample_noise[:, :, :count, :]
-        return noise.to(dtype=latent_image.dtype)
-
     if noise_inds is None:
         return torch.randn(latent_image.size(), dtype=torch.float32, layout=latent_image.layout, generator=generator, device="cpu").to(dtype=latent_image.dtype)