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]).

[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

[smartbulldog-pro]

I had the same error, update Comfyui to the latest version 0.4.0 and the error will disappear.

[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]).

Sorry guys. I just need to update comfyui to last version. Now it's working.

[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

[smartbulldog-pro]

I had the same error, update Comfyui to the latest version 0.4.0 and the error will disappear.

[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.

[Xu-Perry]

my gpu is 5070TI, when i run this work follow, it crashed on loading model. don't know to fixed it. any help?

[Xu-Perry]

seems no any error log, just crashed. ```E:\ComfyUI_windows_portable_nvidia_cu128\ComfyUI_windows_portable>.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build
Checkpoint files will always be loaded safely.
Total VRAM 16303 MB, total RAM 32602 MB
pytorch version: 2.9.1+cu128
Set vram state to: NORMAL_VRAM
Device: cuda:0 NVIDIA GeForce RTX 5070 Ti : cudaMallocAsync
Using async weight offloading with 2 streams
Enabled pinned memory 14671.0
working around nvidia conv3d memory bug.
Using pytorch attention
Python version: 3.12.10 (tags/v3.12.10:0cc8128, Apr 8 2025, 12:21:36) [MSC v.1943 64 bit (AMD64)]
ComfyUI version: 0.4.0
ComfyUI frontend version: 1.34.8
[Prompt Server] web root: E:\ComfyUI_windows_portable_nvidia_cu128\ComfyUI_windows_portable\python_embeded\Lib\site-packages\comfyui_frontend_package\static
Total VRAM 16303 MB, total RAM 32602 MB
pytorch version: 2.9.1+cu128
Set vram state to: NORMAL_VRAM
Device: cuda:0 NVIDIA GeForce RTX 5070 Ti : cudaMallocAsync
Using async weight offloading with 2 streams
Enabled pinned memory 14671.0

Import times for custom nodes:
0.0 seconds: E:\ComfyUI_windows_portable_nvidia_cu128\ComfyUI_windows_portable\ComfyUI\custom_nodes\websocket_image_save.py

Context impl SQLiteImpl.
Will assume non-transactional DDL.
No target revision found.
Starting server

To see the GUI go to: http://127.0.0.1:8188
got prompt
Using pytorch attention in VAE
Using pytorch attention in VAE
VAE load device: cuda:0, offload device: cpu, dtype: torch.bfloat16
Requested to load ZImageTEModel_
loaded completely; 95367431640625005117571072.00 MB usable, 7672.25 MB loaded, full load: True
CLIP/text encoder model load device: cuda:0, offload device: cpu, current: cuda:0, dtype: torch.float16

E:\ComfyUI_windows_portable_nvidia_cu128\ComfyUI_windows_portable>echo If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest.
If you see this and ComfyUI did not start try updating your Nvidia Drivers to the latest.

E:\ComfyUI_windows_portable_nvidia_cu128\ComfyUI_windows_portable>pause```

[Xu-Perry]

i got the reason，my PC didn‘t set Virtual memory，should set it

[CrysisPoRusski]

Hi, can you tell me how to solve this problem?
I'm using the Z Image Turbo model.

[ERROR: clip input is invalid: None
If the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.]

[Demuzx]

Hi, can you tell me how to solve this problem? I'm using the Z Image Turbo model.

[ERROR: clip input is invalid: None If the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.]

I've updated comfyui to last version.

[CrysisPoRusski]

Unfortunately, it didn't work.

[Demuzx]

Try to check hash of files you downloaded.

[cmillsap]

Is it possible with these workflows to make the example images available somewhere? I have several set up but can't find the example images to test them with.