From e643977d617a18ea008b22ae6366dbf4648d28db Mon Sep 17 00:00:00 2001 From: Eugene Fairley Date: Sat, 28 Jun 2025 22:53:55 -0700 Subject: [PATCH] save --- comfy/model_base.py | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index 98610e227..63177576e 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -1188,7 +1188,7 @@ class WAN21(BaseModel): image = image.to(device) image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") for i in range(0, image.shape[1], 16): - image[:, i: i + 16] = self.process_latent_in(image[:, i: i + 36]) + image[:, i: i + 16] = self.process_latent_in(image[:, i: i + 16]) image = utils.resize_to_batch_size(image, noise.shape[0]) print(f"image shape: {image.shape}") @@ -1199,25 +1199,25 @@ class WAN21(BaseModel): image = image[:, :(extra_channels - 4)] mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)).to(device) if "concat_mask" in kwargs or "denoise_mask" in kwargs else None - if mask is None: - mask = torch.zeros_like(noise)[:, :4] - else: - if mask.shape[1] != 4: - mask = torch.mean(mask, dim=1, keepdim=True) - mask = 1.0 - mask - mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") - if mask.shape[-3] < noise.shape[-3]: - mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0) - if mask.shape[1] == 1: - mask = mask.repeat(1, 4, 1, 1, 1) + # if mask is None: + # mask = torch.zeros_like(noise)[:, :4] + # else: + # if mask.shape[1] != 4: + # mask = torch.mean(mask, dim=1, keepdim=True) + # mask = 1.0 - mask + # mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + # if mask.shape[-3] < noise.shape[-3]: + # mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0) + # if mask.shape[1] == 1: + # mask = mask.repeat(1, 4, 1, 1, 1) - print(f"Mask shape: {mask.shape}, noise shape: {noise.shape}") - mask = utils.resize_to_batch_size(mask, noise.shape[0]) + # print(f"Mask shape: {mask.shape}, noise shape: {noise.shape}") + # mask = utils.resize_to_batch_size(mask, noise.shape[0]) print(f"image shape: {image.shape}, mask shape: {mask.shape}") res = torch.cat((mask, image), dim=1) tracks = kwargs.get("tracks", None) if tracks is not None: - res = patch_motion(tracks, res, 220.0, (4, 16), 2)[None] + res = patch_motion(tracks.to(device), res, 220.0, (4, 16), 2)[None] return res