ComfyUI v0.18.1

There was an issue where the resample split was too early and dropped one
of the rolling convolutions a frame early. This is most noticable as a
lighting/color change between pixel frames 5->6 (latent 2->3), or as a
lighting change between the first and last frame in an FLF wan flow.

comfy/ldm/wan/vae.py

@@ -376,11 +376,16 @@ class Decoder3d(nn.Module):
             return
 
         layer = self.upsamples[layer_idx]
-        if isinstance(layer, Resample) and layer.mode == 'upsample3d' and x.shape[2] > 1:
-            for frame_idx in range(x.shape[2]):
+        if feat_cache is not None:
+            x = layer(x, feat_cache, feat_idx)
+        else:
+            x = layer(x)
+
+        if isinstance(layer, Resample) and layer.mode == 'upsample3d' and x.shape[2] > 2:
+            for frame_idx in range(0, x.shape[2], 2):
                 self.run_up(
-                    layer_idx,
-                    [x[:, :, frame_idx:frame_idx + 1, :, :]],
+                    layer_idx + 1,
+                    [x[:, :, frame_idx:frame_idx + 2, :, :]],
                     feat_cache,
                     feat_idx.copy(),
                     out_chunks,
@@ -388,11 +393,6 @@ class Decoder3d(nn.Module):
             del x
             return
 
-        if feat_cache is not None:
-            x = layer(x, feat_cache, feat_idx)
-        else:
-            x = layer(x)
-
         next_x_ref = [x]
         del x
         self.run_up(layer_idx + 1, next_x_ref, feat_cache, feat_idx, out_chunks)

comfy/sample.py

@@ -8,12 +8,12 @@ import comfy.nested_tensor
 
 def prepare_noise_inner(latent_image, generator, noise_inds=None):
     if noise_inds is None:
-        return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
+        return torch.randn(latent_image.size(), dtype=torch.float32, layout=latent_image.layout, generator=generator, device="cpu").to(dtype=latent_image.dtype)
 
     unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
     noises = []
     for i in range(unique_inds[-1]+1):
-        noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
+        noise = torch.randn([1] + list(latent_image.size())[1:], dtype=torch.float32, layout=latent_image.layout, generator=generator, device="cpu").to(dtype=latent_image.dtype)
         if i in unique_inds:
             noises.append(noise)
     noises = [noises[i] for i in inverse]

comfy/samplers.py

@@ -985,8 +985,8 @@ class CFGGuider:
         self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options)
         device = self.model_patcher.load_device
 
-        noise = noise.to(device)
-        latent_image = latent_image.to(device)
+        noise = noise.to(device=device, dtype=torch.float32)
+        latent_image = latent_image.to(device=device, dtype=torch.float32)
         sigmas = sigmas.to(device)
         cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype())
 
@@ -1028,6 +1028,7 @@ class CFGGuider:
                 denoise_mask, _ = comfy.utils.pack_latents(denoise_masks)
             else:
                 denoise_mask = denoise_masks[0]
+            denoise_mask = denoise_mask.float()
 
         self.conds = {}
         for k in self.original_conds:

comfy_extras/nodes_canny.py

@@ -3,6 +3,7 @@ from typing_extensions import override
 
 import comfy.model_management
 from comfy_api.latest import ComfyExtension, io
+import torch
 
 
 class Canny(io.ComfyNode):
@@ -29,8 +30,8 @@ class Canny(io.ComfyNode):
 
     @classmethod
     def execute(cls, image, low_threshold, high_threshold) -> io.NodeOutput:
-        output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold)
-        img_out = output[1].to(comfy.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1)
+        output = canny(image.to(device=comfy.model_management.get_torch_device(), dtype=torch.float32).movedim(-1, 1), low_threshold, high_threshold)
+        img_out = output[1].to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()).repeat(1, 3, 1, 1).movedim(1, -1)
         return io.NodeOutput(img_out)
 
 

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.18.0"
+__version__ = "0.18.1"

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.18.0"
+version = "0.18.1"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"