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Do tripo dinov3 inference in fp32. (#14221)

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comfyanonymous 2026-06-01 18:08:20 -07:00 committed by GitHub
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2 changed files with 5 additions and 5 deletions
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7
comfy/image_encoders/dino3.py
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@ -3,6 +3,7 @@ import torch
import torch.nn as nn import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
import comfy.ops
from comfy.ldm.modules.attention import optimized_attention_for_device from comfy.ldm.modules.attention import optimized_attention_for_device
from comfy.image_encoders.dino2 import LayerScale as DINOv3ViTLayerScale from comfy.image_encoders.dino2 import LayerScale as DINOv3ViTLayerScale
@ -171,11 +172,11 @@ class DINOv3ViTEmbeddings(nn.Module):
patch_embeddings = patch_embeddings.flatten(2).transpose(1, 2) patch_embeddings = patch_embeddings.flatten(2).transpose(1, 2)
if bool_masked_pos is not None: if bool_masked_pos is not None:
mask_token = self.mask_token.to(patch_embeddings.dtype) mask_token = comfy.ops.cast_to_input(self.mask_token, patch_embeddings)
patch_embeddings = torch.where(bool_masked_pos.unsqueeze(-1), mask_token, patch_embeddings) patch_embeddings = torch.where(bool_masked_pos.unsqueeze(-1), mask_token, patch_embeddings)
cls_token = self.cls_token.expand(batch_size, -1, -1).to(patch_embeddings.device) cls_token = comfy.ops.cast_to_input(self.cls_token.expand(batch_size, -1, -1), patch_embeddings)
register_tokens = self.register_tokens.expand(batch_size, -1, -1).to(patch_embeddings.device) register_tokens = comfy.ops.cast_to_input(self.register_tokens.expand(batch_size, -1, -1), patch_embeddings)
embeddings = torch.cat([cls_token, register_tokens, patch_embeddings], dim=1) embeddings = torch.cat([cls_token, register_tokens, patch_embeddings], dim=1)
return embeddings return embeddings

3
comfy_extras/nodes_triposplat.py
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@ -115,12 +115,11 @@ class TripoSplatConditioning(IO.ComfyNode):
# feature1: DINOv3 token sequence (cls + registers + patches), ImageNet-normalized, with a final non-affine layer norm on top # feature1: DINOv3 token sequence (cls + registers + patches), ImageNet-normalized, with a final non-affine layer norm on top
comfy.model_management.load_model_gpu(clip_vision.patcher) comfy.model_management.load_model_gpu(clip_vision.patcher)
device = clip_vision.load_device device = clip_vision.load_device
model_dtype = next(clip_vision.model.parameters()).dtype
img = image.movedim(-1, 1).to(device) # (B,3,H,W) in [0,1] img = image.movedim(-1, 1).to(device) # (B,3,H,W) in [0,1]
mean = torch.tensor(_DINOV3_MEAN, device=device).view(1, 3, 1, 1) mean = torch.tensor(_DINOV3_MEAN, device=device).view(1, 3, 1, 1)
std = torch.tensor(_DINOV3_STD, device=device).view(1, 3, 1, 1) std = torch.tensor(_DINOV3_STD, device=device).view(1, 3, 1, 1)
img = (img - mean) / std img = (img - mean) / std
seq = clip_vision.model(pixel_values=img.to(model_dtype))[0] seq = clip_vision.model(pixel_values=img.float())[0]
feature1 = F.layer_norm(seq.float(), seq.shape[-1:]).to(comfy.model_management.intermediate_device()) feature1 = F.layer_norm(seq.float(), seq.shape[-1:]).to(comfy.model_management.intermediate_device())
# Second conditioning: the Flux2 VAE latent of the image, carried as a standard reference_latents entry # Second conditioning: the Flux2 VAE latent of the image, carried as a standard reference_latents entry