tutorials/image/z-image/z-image-turbo

[giscus[bot]]

tutorials/image/z-image/z-image-turbo

Z-Image is a 6B parameter efficient image generation foundation model with single-stream diffusion transformer, offering sub-second inference latency.

https://docs.comfy.org/tutorials/image/z-image/z-image-turbo

[Demuzx]

Is it joke?

CLIPLoader
Error(s) in loading state_dict for Llama2:
size mismatch for model.embed_tokens.weight: copying a param with shape torch.Size([151936, 2560]) from checkpoint, the shape in current model is torch.Size([128256, 4096]).
size mismatch for model.layers.0.self_attn.q_proj.weight: copying a param with shape torch.Size([4096, 2560]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.0.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 2560]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).

[MiBaDK]

That's the errors I get as well

[RebootWebsites]

took a while to reproduce this...
Guys once you load the models into the correct folders. you select the models correctly in the sampler (unet_name, vae, clip ) . You click run once - will fail.

Now, click on the manager (top right), you will see the red restart button. ON TOP OF THAT, click on "Update All Custom Nodes" , once update done - click restart.

Now will work perfect.

[Philnex]

Had the same issue, updating all did it for me.

[Yonghao-lucky]

Is it joke?

CLIPLoader Error(s) in loading state_dict for Llama2: size mismatch for model.embed_tokens.weight: copying a param with shape torch.Size([151936, 2560]) from checkpoint, the shape in current model is torch.Size([128256, 4096]). size mismatch for model.layers.0.self_attn.q_proj.weight: copying a param with shape torch.Size([4096, 2560]) from checkpoint, the shape in current model is torch.Size([4096, 4096]). size mismatch for model.layers.0.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 2560]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).

How to solve the problem of deploying on Linux servers?

[RebootWebsites]

same solution

[navdesigner]

getting these errors
UNETLoader
Error(s) in loading state_dict for NextDiT:
size mismatch for x_embedder.weight: copying a param with shape torch.Size([3840, 64]) from checkpoint, the shape in current model is torch.Size([2304, 64]).
size mismatch for x_embedder.bias: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for noise_refiner.0.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for noise_refiner.0.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for noise_refiner.0.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for noise_refiner.0.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for noise_refiner.0.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for noise_refiner.0.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for noise_refiner.0.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for noise_refiner.0.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for noise_refiner.0.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for noise_refiner.0.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for noise_refiner.0.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for noise_refiner.1.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for noise_refiner.1.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for noise_refiner.1.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for noise_refiner.1.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for noise_refiner.1.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for noise_refiner.1.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for noise_refiner.1.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for noise_refiner.1.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for noise_refiner.1.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for noise_refiner.1.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for noise_refiner.1.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for context_refiner.0.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for context_refiner.0.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for context_refiner.0.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for context_refiner.0.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for context_refiner.0.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for context_refiner.0.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for context_refiner.0.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for context_refiner.0.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for context_refiner.0.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for context_refiner.0.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for context_refiner.0.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for context_refiner.1.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for context_refiner.1.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for context_refiner.1.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for context_refiner.1.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for context_refiner.1.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for context_refiner.1.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for context_refiner.1.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for context_refiner.1.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for context_refiner.1.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for context_refiner.1.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for context_refiner.1.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for t_embedder.mlp.2.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 1024]).
size mismatch for t_embedder.mlp.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for cap_embedder.1.weight: copying a param with shape torch.Size([3840, 2560]) from checkpoint, the shape in current model is torch.Size([2304, 2560]).
size mismatch for cap_embedder.1.bias: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.0.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.0.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.0.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.0.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.0.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.0.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.0.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.0.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.0.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.0.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.0.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.1.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.1.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.1.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.1.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.1.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.1.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.1.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.1.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.1.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.1.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.1.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.2.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.2.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.2.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.2.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.2.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.2.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.2.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.2.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.2.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.2.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.2.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.3.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.3.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.3.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.3.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.3.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.3.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.3.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.3.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.3.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.3.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.3.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.4.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.4.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.4.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.4.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.4.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.4.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.4.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.4.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.4.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.4.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.4.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.5.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.5.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.5.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.5.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.5.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.5.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.5.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.5.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.5.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.5.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.5.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.6.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.6.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.6.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.6.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.6.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.6.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.6.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.6.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.6.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.6.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.6.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.7.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.7.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.7.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.7.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.7.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.7.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.7.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.7.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.7.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.7.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.7.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.8.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.8.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.8.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.8.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.8.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.8.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.8.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.8.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.8.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.8.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.8.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.9.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.9.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.9.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.9.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.9.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.9.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.9.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.9.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.9.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.9.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.9.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.10.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.10.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.10.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.10.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.10.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.10.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.10.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.10.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.10.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.10.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.10.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.11.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.11.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.11.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.11.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.11.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.11.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.11.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.11.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.11.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.11.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.11.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.12.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.12.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.12.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.12.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.12.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.12.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.12.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.12.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.12.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.12.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.12.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.13.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.13.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.13.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.13.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.13.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.13.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.13.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.13.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.13.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.13.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.13.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.14.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.14.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.14.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.14.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.14.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.14.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.14.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.14.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.14.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.14.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.14.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.15.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.15.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.15.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.15.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.15.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.15.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.15.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.15.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.15.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.15.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.15.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.16.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.16.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.16.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.16.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.16.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.16.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.16.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.16.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.16.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.16.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.16.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.17.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.17.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.17.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.17.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.17.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.17.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.17.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.17.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.17.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.17.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.17.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.18.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.18.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.18.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.18.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.18.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.18.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.18.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.18.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.18.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.18.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.18.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.19.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.19.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.19.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.19.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.19.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.19.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.19.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.19.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.19.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.19.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.19.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.20.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.20.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.20.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.20.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.20.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.20.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.20.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.20.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.20.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.20.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.20.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.21.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.21.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.21.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.21.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.21.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.21.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.21.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.21.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.21.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.21.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.21.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.22.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.22.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.22.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.22.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.22.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.22.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.22.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.22.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.22.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.22.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.22.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.23.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.23.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.23.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.23.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.23.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.23.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.23.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.23.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.23.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.23.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.23.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.24.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.24.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.24.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.24.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.24.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.24.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.24.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.24.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.24.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.24.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.24.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.25.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.25.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.25.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.25.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.25.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.25.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.25.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.25.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.25.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.25.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.25.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.26.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.26.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.26.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.26.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.26.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.26.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.26.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.26.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.26.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.26.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.26.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.27.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.27.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.27.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.27.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.27.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.27.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.27.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.27.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.27.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.27.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.27.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.28.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.28.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.28.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.28.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.28.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.28.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.28.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.28.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.28.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.28.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.28.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.29.attention.qkv.weight: copying a param with shape torch.Size([11520, 3840]) from checkpoint, the shape in current model is torch.Size([3840, 2304]).
size mismatch for layers.29.attention.out.weight: copying a param with shape torch.Size([3840, 3840]) from checkpoint, the shape in current model is torch.Size([2304, 2304]).
size mismatch for layers.29.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.29.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
size mismatch for layers.29.feed_forward.w1.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.29.feed_forward.w2.weight: copying a param with shape torch.Size([3840, 10240]) from checkpoint, the shape in current model is torch.Size([2304, 9216]).
size mismatch for layers.29.feed_forward.w3.weight: copying a param with shape torch.Size([10240, 3840]) from checkpoint, the shape in current model is torch.Size([9216, 2304]).
size mismatch for layers.29.attention_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.29.ffn_norm1.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.29.attention_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for layers.29.ffn_norm2.weight: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).
size mismatch for final_layer.linear.weight: copying a param with shape torch.Size([64, 3840]) from checkpoint, the shape in current model is torch.Size([64, 2304]).
size mismatch for final_layer.adaLN_modulation.1.weight: copying a param with shape torch.Size([3840, 256]) from checkpoint, the shape in current model is torch.Size([2304, 1024]).
size mismatch for final_layer.adaLN_modulation.1.bias: copying a param with shape torch.Size([3840]) from checkpoint, the shape in current model is torch.Size([2304]).

[RebootWebsites]

update ComfyUI

[navdesigner]

it worked after the update, thank you

[IsItDanOrAi]

Ive tried every version of the Turbo model, BF16/FP8/GGUF - same issue.

Unet: ZImage Turbo BF16
CLIP: ZImage_Qwen_3_4b
VAE: ae
ModelPatchLoader: Z-Image-Turbo-Fun-Controlnet-Union

ComfyUI Error Report
Error Details

• Node ID: 64
• Node Type: ModelPatchLoader
• Exception Type: UnboundLocalError
• Exception Message: cannot access local variable 'model' where it is not associated with a value

[comfyui-wiki]

The ControlNet support isn't in the latest stable version, so please make sure you have updated ComfyUI to the latest commit. If you are using the desktop version, you need to wait for the next stable release.

[IsItDanOrAi]

Appreciate the reply.