nodes/kijai-ComfyUI-WanVideoWrapper
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small fixes, add comfy pbar for model loading

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kijai
2025-07-06 23:55:37 +03:00
parent a35142b006
commit d903ea7d3d
4 changed files with 31 additions and 15 deletions
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uni3c/controlnet.py
+8 -4
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@@ -189,6 +189,8 @@ class WanControlNet(ModelMixin):
self.in_channels = controlnet_cfg["in_channels"]
self.dim = controlnet_cfg["dim"]
self.num_heads = controlnet_cfg["num_heads"]
self.quantized = controlnet_cfg["quantized"]
self.base_dtype = controlnet_cfg["base_dtype"]
if controlnet_cfg["conv_out_dim"] != controlnet_cfg["dim"]:
self.proj_in = nn.Linear(controlnet_cfg["conv_out_dim"], controlnet_cfg["dim"])
@@ -230,11 +232,13 @@ class WanControlNet(ModelMixin):
self.controlnet_mask_embedding = MaskCamEmbed(controlnet_cfg)
def forward(self, render_latent, render_mask, camera_embedding, temb, device):
def forward(self, render_latent, render_mask, camera_embedding, temb, device):
controlnet_rotary_emb = self.controlnet_rope(render_latent)
controlnet_inputs = self.controlnet_patch_embedding(render_latent.to(torch.float32)).to(render_latent.dtype)
controlnet_inputs = controlnet_inputs.to(render_latent.dtype)
controlnet_inputs = self.controlnet_patch_embedding(render_latent.to(torch.float32))
if not self.quantized:
controlnet_inputs = controlnet_inputs.to(render_latent.dtype)
else:
controlnet_inputs = controlnet_inputs.to(self.base_dtype)
controlnet_inputs = controlnet_inputs.flatten(2).transpose(1, 2)
uni3c/nodes.py
+4 -2
View File
@@ -69,7 +69,9 @@ class WanVideoUni3C_ControlnetLoader:
"num_layers": 20,
"add_channels": 7,
"mid_channels": 256,
"attention_mode": attention_mode
"attention_mode": attention_mode,
"quantized": True if quantization != "disabled" else False,
"base_dtype": base_dtype
}
from .controlnet import WanControlNet
@@ -94,7 +96,7 @@ class WanVideoUni3C_ControlnetLoader:
dtype = torch.float8_e5m2
else:
dtype = base_dtype
params_to_keep = {"norm", "head", "time_in", "vector_in", "controlnet_patch_embedding", "time_", "img_emb", "modulation", "text_embedding", "adapter"}
params_to_keep = {"norm", "head", "time_in", "vector_in", "controlnet_patch_embedding", "time_", "img_emb", "modulation", "text_embedding", "adapter", "proj_in"}
log.info("Using accelerate to load and assign controlnet model weights to device...")
param_count = sum(1 for _ in controlnet.named_parameters())