not run #51

werruww · 2024-11-21T03:17:08Z

/content/ComfyUI/models/text_encoders/t5xxl_fp16.safetensors
/content/ComfyUI/models/unet/flux1-dev-bnb-nf4-v2-unet.safetensors
/content/ComfyUI/models/unet/flux1-schnell-bnb-nf4-unet.safetensors
/content/ComfyUI/models/vae/sdxl_vae.safetensors

Error(s) in loading state_dict for Flux:
size mismatch for img_in.weight: copying a param with shape torch.Size([98304, 1]) from checkpoint, the shape in current model is torch.Size([3072, 64]).
size mismatch for time_in.in_layer.weight: copying a param with shape torch.Size([393216, 1]) from checkpoint, the shape in current model is torch.Size([3072, 256]).
size mismatch for time_in.out_layer.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for vector_in.in_layer.weight: copying a param with shape torch.Size([1179648, 1]) from checkpoint, the shape in current model is torch.Size([3072, 768]).
size mismatch for vector_in.out_layer.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for txt_in.weight: copying a param with shape torch.Size([6291456, 1]) from checkpoint, the shape in current model is torch.Size([3072, 4096]).
size mismatch for double_blocks.0.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.0.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.0.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.0.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.0.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.0.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.0.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.0.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.0.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.0.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.1.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.1.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.1.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.1.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.1.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.1.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.1.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.1.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.1.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.1.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.2.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.2.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.2.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.2.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.2.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.2.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.2.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.2.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.2.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.2.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.3.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.3.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.3.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.3.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.3.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.3.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.3.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.3.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.3.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.3.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.4.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.4.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.4.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.4.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.4.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.4.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.4.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.4.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.4.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.4.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.5.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.5.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.5.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.5.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.5.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.5.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.5.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.5.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.5.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.5.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.6.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.6.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.6.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.6.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.6.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.6.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.6.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.6.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.6.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.6.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.7.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.7.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.7.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.7.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.7.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.7.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.7.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.7.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.7.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.7.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.8.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.8.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.8.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.8.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.8.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.8.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.8.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.8.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.8.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.8.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.9.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.9.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.9.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.9.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.9.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.9.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.9.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.9.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.9.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.9.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.10.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.10.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.10.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.10.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.10.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.10.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.10.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.10.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.10.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.10.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.11.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.11.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.11.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.11.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.11.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.11.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.11.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.11.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.11.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.11.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.12.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.12.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.12.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.12.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.12.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.12.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.12.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.12.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.12.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.12.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.13.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.13.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.13.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.13.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.13.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.13.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.13.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.13.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.13.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.13.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.14.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.14.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.14.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.14.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.14.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.14.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.14.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.14.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.14.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.14.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.15.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.15.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.15.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.15.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.15.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.15.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.15.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.15.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.15.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.15.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.16.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.16.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.16.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.16.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.16.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.16.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.16.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.16.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.16.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.16.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.17.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.17.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.17.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.17.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.17.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.17.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.17.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.17.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.17.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.17.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.18.img_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.18.img_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.18.img_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.18.img_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.18.img_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for double_blocks.18.txt_mod.lin.weight: copying a param with shape torch.Size([28311552, 1]) from checkpoint, the shape in current model is torch.Size([18432, 3072]).
size mismatch for double_blocks.18.txt_attn.qkv.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for double_blocks.18.txt_attn.proj.weight: copying a param with shape torch.Size([4718592, 1]) from checkpoint, the shape in current model is torch.Size([3072, 3072]).
size mismatch for double_blocks.18.txt_mlp.0.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([12288, 3072]).
size mismatch for double_blocks.18.txt_mlp.2.weight: copying a param with shape torch.Size([18874368, 1]) from checkpoint, the shape in current model is torch.Size([3072, 12288]).
size mismatch for single_blocks.0.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.0.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.0.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.1.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.1.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.1.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.2.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.2.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.2.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.3.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.3.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.3.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.4.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.4.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.4.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.5.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.5.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.5.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.6.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.6.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.6.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.7.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.7.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.7.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.8.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.8.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.8.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.9.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.9.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.9.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.10.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.10.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.10.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.11.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.11.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.11.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.12.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.12.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.12.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.13.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.13.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.13.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.14.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.14.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.14.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.15.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.15.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.15.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.16.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.16.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.16.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.17.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.17.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.17.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.18.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.18.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.18.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.19.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.19.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.19.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.20.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.20.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.20.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.21.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.21.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.21.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.22.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.22.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.22.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.23.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.23.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.23.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.24.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.24.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.24.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.25.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.25.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.25.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.26.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.26.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.26.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.27.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.27.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.27.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.28.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.28.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.28.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.29.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.29.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.29.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.30.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.30.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.30.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.31.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.31.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.31.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.32.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.32.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.32.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.33.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.33.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.33.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.34.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.34.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.34.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.35.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.35.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.35.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.36.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.36.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.36.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for single_blocks.37.linear1.weight: copying a param with shape torch.Size([33030144, 1]) from checkpoint, the shape in current model is torch.Size([21504, 3072]).
size mismatch for single_blocks.37.linear2.weight: copying a param with shape torch.Size([23592960, 1]) from checkpoint, the shape in current model is torch.Size([3072, 15360]).
size mismatch for single_blocks.37.modulation.lin.weight: copying a param with shape torch.Size([14155776, 1]) from checkpoint, the shape in current model is torch.Size([9216, 3072]).
size mismatch for final_layer.linear.weight: copying a param with shape torch.Size([98304, 1]) from checkpoint, the shape in current model is torch.Size([64, 3072]).
size mismatch for final_layer.adaLN_modulation.1.weight: copying a param with shape torch.Size([9437184, 1]) from checkpoint, the shape in current model is torch.Size([6144, 3072]).

werruww · 2024-11-21T03:27:39Z

Manager

1

beautiful scenery nature glass bottle landscape, , purple galaxy bottle,
text, watermark
KSampler
Flux.forward() missing 1 required positional argument: 'y'

ComfyUI Error Report

Error Details

Node ID: 3
Node Type: KSampler
Exception Type: TypeError
Exception Message: Flux.forward() missing 1 required positional argument: 'y'

Stack Trace

  File "/content/ComfyUI/execution.py", line 323, in execute
    output_data, output_ui, has_subgraph = get_output_data(obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)

  File "/content/ComfyUI/execution.py", line 198, in get_output_data
    return_values = _map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)

  File "/content/ComfyUI/execution.py", line 169, in _map_node_over_list
    process_inputs(input_dict, i)

  File "/content/ComfyUI/execution.py", line 158, in process_inputs
    results.append(getattr(obj, func)(**inputs))

  File "/content/ComfyUI/nodes.py", line 1454, in sample
    return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)

  File "/content/ComfyUI/nodes.py", line 1421, in common_ksampler
    samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,

  File "/content/ComfyUI/comfy/sample.py", line 43, in sample
    samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)

  File "/content/ComfyUI/comfy/samplers.py", line 855, in sample
    return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)

  File "/content/ComfyUI/comfy/samplers.py", line 753, in sample
    return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)

  File "/content/ComfyUI/comfy/samplers.py", line 740, in sample
    output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)

  File "/content/ComfyUI/comfy/samplers.py", line 719, in inner_sample
    samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)

  File "/content/ComfyUI/comfy/samplers.py", line 624, in sample
    samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)

  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)

  File "/content/ComfyUI/comfy/k_diffusion/sampling.py", line 155, in sample_euler
    denoised = model(x, sigma_hat * s_in, **extra_args)

  File "/content/ComfyUI/comfy/samplers.py", line 299, in __call__
    out = self.inner_model(x, sigma, model_options=model_options, seed=seed)

  File "/content/ComfyUI/comfy/samplers.py", line 706, in __call__
    return self.predict_noise(*args, **kwargs)

  File "/content/ComfyUI/comfy/samplers.py", line 709, in predict_noise
    return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed)

  File "/content/ComfyUI/comfy/samplers.py", line 279, in sampling_function
    out = calc_cond_batch(model, conds, x, timestep, model_options)

  File "/content/ComfyUI/comfy/samplers.py", line 228, in calc_cond_batch
    output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)

  File "/content/ComfyUI/comfy/model_base.py", line 144, in apply_model
    model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()

  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)

  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)

System Information

ComfyUI Version: v0.3.0-2-g772e620
Arguments: main.py
OS: posix
Python Version: 3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0]
Embedded Python: false
PyTorch Version: 2.5.1+cu121

Devices

Name: cuda:0 Tesla T4 : cudaMallocAsync
- Type: cuda
- VRAM Total: 15835660288
- VRAM Free: 1963830460
- Torch VRAM Total: 13723762688
- Torch VRAM Free: 8170684

Logs

2024-11-21T03:24:14.873127 - [START] Security scan2024-11-21T03:24:14.873149 - 
2024-11-21T03:24:15.974098 - [DONE] Security scan2024-11-21T03:24:15.974123 - 
2024-11-21T03:24:16.060792 - ## ComfyUI-Manager: installing dependencies done.2024-11-21T03:24:16.060860 - 
2024-11-21T03:24:16.060903 - ** ComfyUI startup time:2024-11-21T03:24:16.060937 -  2024-11-21T03:24:16.060970 - 2024-11-21 03:24:16.0608802024-11-21T03:24:16.061002 - 
2024-11-21T03:24:16.061036 - ** Platform:2024-11-21T03:24:16.061063 -  2024-11-21T03:24:16.061088 - Linux2024-11-21T03:24:16.061113 - 
2024-11-21T03:24:16.061140 - ** Python version:2024-11-21T03:24:16.061165 -  2024-11-21T03:24:16.061191 - 3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0]2024-11-21T03:24:16.061217 - 
2024-11-21T03:24:16.061243 - ** Python executable:2024-11-21T03:24:16.061268 -  2024-11-21T03:24:16.061292 - /usr/bin/python32024-11-21T03:24:16.061317 - 
2024-11-21T03:24:16.061343 - ** ComfyUI Path:2024-11-21T03:24:16.061368 -  2024-11-21T03:24:16.061392 - /content/ComfyUI2024-11-21T03:24:16.061417 - 
2024-11-21T03:24:16.061483 - ** Log path:2024-11-21T03:24:16.061513 -  2024-11-21T03:24:16.061538 - /content/ComfyUI/comfyui.log2024-11-21T03:24:16.061564 - 
2024-11-21T03:24:16.069734 - 
Prestartup times for custom nodes:2024-11-21T03:24:16.069783 - 
2024-11-21T03:24:16.069832 -    1.2 seconds:2024-11-21T03:24:16.069861 -  2024-11-21T03:24:16.069886 - /content/ComfyUI/custom_nodes/ComfyUI-Manager2024-11-21T03:24:16.069915 - 
2024-11-21T03:24:16.069949 - 
2024-11-21T03:24:18.345879 - Total VRAM 15102 MB, total RAM 12979 MB
2024-11-21T03:24:18.346067 - pytorch version: 2.5.1+cu121
2024-11-21T03:24:18.378783 - Set vram state to: NORMAL_VRAM
2024-11-21T03:24:18.379081 - Device: cuda:0 Tesla T4 : cudaMallocAsync
2024-11-21T03:24:19.490582 - Using pytorch cross attention
2024-11-21T03:24:20.903595 - [Prompt Server] web root: /content/ComfyUI/web
2024-11-21T03:24:21.819203 - ### Loading: ComfyUI-Manager (V2.51.9)2024-11-21T03:24:21.819444 - 
2024-11-21T03:24:21.866120 - ### ComfyUI Revision: 2844 [772e620e] | Released on '2024-11-20'2024-11-21T03:24:21.866179 - 
2024-11-21T03:24:21.873391 - Traceback (most recent call last):
  File "/content/ComfyUI/nodes.py", line 2024, in load_custom_node
    module_spec.loader.exec_module(module)
  File "<frozen importlib._bootstrap_external>", line 879, in exec_module
  File "<frozen importlib._bootstrap_external>", line 1016, in get_code
  File "<frozen importlib._bootstrap_external>", line 1073, in get_data
FileNotFoundError: [Errno 2] No such file or directory: '/content/ComfyUI/custom_nodes/.ipynb_checkpoints/__init__.py'

2024-11-21T03:24:21.875403 - Cannot import /content/ComfyUI/custom_nodes/.ipynb_checkpoints module for custom nodes: [Errno 2] No such file or directory: '/content/ComfyUI/custom_nodes/.ipynb_checkpoints/__init__.py'
2024-11-21T03:24:21.946553 - 
Import times for custom nodes:
2024-11-21T03:24:21.946750 -    0.0 seconds: /content/ComfyUI/custom_nodes/websocket_image_save.py
2024-11-21T03:24:21.946857 -    0.0 seconds: /content/ComfyUI/custom_nodes/ComfyUI_bnb_nf4_fp4_Loaders
2024-11-21T03:24:21.946942 -    0.0 seconds (IMPORT FAILED): /content/ComfyUI/custom_nodes/.ipynb_checkpoints
2024-11-21T03:24:21.947036 -    0.0 seconds: /content/ComfyUI/custom_nodes/ComfyUI-GGUF
2024-11-21T03:24:21.947118 -    0.0 seconds: /content/ComfyUI/custom_nodes/ComfyUI_UNet_bitsandbytes_NF4
2024-11-21T03:24:21.947377 -    0.1 seconds: /content/ComfyUI/custom_nodes/ComfyUI-Manager
2024-11-21T03:24:21.947561 - 
2024-11-21T03:24:21.955252 - Starting server

2024-11-21T03:24:21.956199 - To see the GUI go to: http://127.0.0.1:8188
2024-11-21T03:24:21.969591 - [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/model-list.json2024-11-21T03:24:21.969644 - 
2024-11-21T03:24:21.985075 - [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/github-stats.json2024-11-21T03:24:21.985128 - 
2024-11-21T03:24:21.991483 - [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/alter-list.json2024-11-21T03:24:21.991545 - 
2024-11-21T03:24:22.019756 - [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json2024-11-21T03:24:22.019818 - 
2024-11-21T03:24:22.047778 - [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/extension-node-map.json2024-11-21T03:24:22.047838 - 
2024-11-21T03:24:33.775671 - FETCH DATA from: /content/ComfyUI/custom_nodes/ComfyUI-Manager/extension-node-map.json2024-11-21T03:24:33.775726 - 2024-11-21T03:24:33.780741 -  [DONE]2024-11-21T03:24:33.780798 - 
2024-11-21T03:24:51.039285 - got prompt
2024-11-21T03:24:51.065394 - Using pytorch attention in VAE
2024-11-21T03:24:51.067105 - Using pytorch attention in VAE
2024-11-21T03:24:51.712838 - Requested to load MochiTEModel_
2024-11-21T03:24:51.713067 - Loading 1 new model
2024-11-21T03:24:51.723686 - loaded completely 0.0 9083.38671875 True
2024-11-21T03:25:40.860442 - model weight dtype torch.bfloat16, manual cast: torch.float16
2024-11-21T03:25:40.861178 - model_type FLUX
2024-11-21T03:25:41.113390 - Requested to load Flux
2024-11-21T03:25:41.113613 - Loading 1 new model
2024-11-21T03:26:16.528571 - loaded completely 0.0 6388.649485588074 True
2024-11-21T03:26:16.609879 - 
  0% 0/20 [00:00<?, ?it/s]2024-11-21T03:26:16.643446 - 
  0% 0/20 [00:00<?, ?it/s]2024-11-21T03:26:16.643943 - 
2024-11-21T03:26:16.687871 - !!! Exception during processing !!! Flux.forward() missing 1 required positional argument: 'y'
2024-11-21T03:26:16.695222 - Traceback (most recent call last):
  File "/content/ComfyUI/execution.py", line 323, in execute
    output_data, output_ui, has_subgraph = get_output_data(obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)
  File "/content/ComfyUI/execution.py", line 198, in get_output_data
    return_values = _map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)
  File "/content/ComfyUI/execution.py", line 169, in _map_node_over_list
    process_inputs(input_dict, i)
  File "/content/ComfyUI/execution.py", line 158, in process_inputs
    results.append(getattr(obj, func)(**inputs))
  File "/content/ComfyUI/nodes.py", line 1454, in sample
    return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
  File "/content/ComfyUI/nodes.py", line 1421, in common_ksampler
    samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
  File "/content/ComfyUI/comfy/sample.py", line 43, in sample
    samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
  File "/content/ComfyUI/comfy/samplers.py", line 855, in sample
    return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
  File "/content/ComfyUI/comfy/samplers.py", line 753, in sample
    return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
  File "/content/ComfyUI/comfy/samplers.py", line 740, in sample
    output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
  File "/content/ComfyUI/comfy/samplers.py", line 719, in inner_sample
    samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
  File "/content/ComfyUI/comfy/samplers.py", line 624, in sample
    samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
  File "/content/ComfyUI/comfy/k_diffusion/sampling.py", line 155, in sample_euler
    denoised = model(x, sigma_hat * s_in, **extra_args)
  File "/content/ComfyUI/comfy/samplers.py", line 299, in __call__
    out = self.inner_model(x, sigma, model_options=model_options, seed=seed)
  File "/content/ComfyUI/comfy/samplers.py", line 706, in __call__
    return self.predict_noise(*args, **kwargs)
  File "/content/ComfyUI/comfy/samplers.py", line 709, in predict_noise
    return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed)
  File "/content/ComfyUI/comfy/samplers.py", line 279, in sampling_function
    out = calc_cond_batch(model, conds, x, timestep, model_options)
  File "/content/ComfyUI/comfy/samplers.py", line 228, in calc_cond_batch
    output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
  File "/content/ComfyUI/comfy/model_base.py", line 144, in apply_model
    model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
TypeError: Flux.forward() missing 1 required positional argument: 'y'

2024-11-21T03:26:16.696322 - Prompt executed in 85.65 seconds

Attached Workflow

Please make sure that workflow does not contain any sensitive information such as API keys or passwords.

{"last_node_id":15,"last_link_id":14,"nodes":[{"id":5,"type":"EmptyLatentImage","pos":[473,609],"size":[315,106],"flags":{},"order":0,"mode":0,"inputs":[],"outputs":[{"name":"LATENT","type":"LATENT","links":[2],"slot_index":0}],"properties":{"Node name for S&R":"EmptyLatentImage"},"widgets_values":[512,512,1]},{"id":7,"type":"CLIPTextEncode","pos":[413,389],"size":[425.27801513671875,180.6060791015625],"flags":{},"order":5,"mode":0,"inputs":[{"name":"clip","type":"CLIP","link":13}],"outputs":[{"name":"CONDITIONING","type":"CONDITIONING","links":[6],"slot_index":0}],"properties":{"Node name for S&R":"CLIPTextEncode"},"widgets_values":["text, watermark"]},{"id":13,"type":"VAELoader","pos":[38,499],"size":[315,58],"flags":{},"order":1,"mode":0,"inputs":[],"outputs":[{"name":"VAE","type":"VAE","links":[11],"slot_index":0}],"properties":{"Node name for S&R":"VAELoader"},"widgets_values":["sdxl_vae.safetensors"]},{"id":14,"type":"CLIPLoader","pos":[-14,665],"size":[315,82],"flags":{},"order":2,"mode":0,"inputs":[],"outputs":[{"name":"CLIP","type":"CLIP","links":[12,13],"slot_index":0}],"properties":{"Node name for S&R":"CLIPLoader"},"widgets_values":["t5xxl_fp16.safetensors","stable_diffusion"]},{"id":15,"type":"UNETLoaderNF4","pos":[17,281],"size":[315,58],"flags":{},"order":3,"mode":0,"inputs":[],"outputs":[{"name":"MODEL","type":"MODEL","links":[14],"slot_index":0}],"properties":{"Node name for S&R":"UNETLoaderNF4"},"widgets_values":["flux1-dev-bnb-nf4-v2-unet.safetensors"]},{"id":6,"type":"CLIPTextEncode","pos":[200,79],"size":[422.84503173828125,164.31304931640625],"flags":{},"order":4,"mode":0,"inputs":[{"name":"clip","type":"CLIP","link":12}],"outputs":[{"name":"CONDITIONING","type":"CONDITIONING","links":[4],"slot_index":0}],"properties":{"Node name for S&R":"CLIPTextEncode"},"widgets_values":["beautiful scenery nature glass bottle landscape, , purple galaxy bottle,"]},{"id":3,"type":"KSampler","pos":[591,120],"size":[315,262],"flags":{},"order":6,"mode":0,"inputs":[{"name":"model","type":"MODEL","link":14},{"name":"positive","type":"CONDITIONING","link":4},{"name":"negative","type":"CONDITIONING","link":6},{"name":"latent_image","type":"LATENT","link":2}],"outputs":[{"name":"LATENT","type":"LATENT","links":[7],"slot_index":0}],"properties":{"Node name for S&R":"KSampler"},"widgets_values":[159612653147126,"randomize",1,8,"euler","normal",1]},{"id":8,"type":"VAEDecode","pos":[932,124],"size":[210,46],"flags":{},"order":7,"mode":0,"inputs":[{"name":"samples","type":"LATENT","link":7},{"name":"vae","type":"VAE","link":11}],"outputs":[{"name":"IMAGE","type":"IMAGE","links":[9],"slot_index":0}],"properties":{"Node name for S&R":"VAEDecode"},"widgets_values":[]},{"id":9,"type":"SaveImage","pos":[1005,257],"size":[210,58],"flags":{},"order":8,"mode":0,"inputs":[{"name":"images","type":"IMAGE","link":9}],"outputs":[],"properties":{},"widgets_values":["ComfyUI"]}],"links":[[2,5,0,3,3,"LATENT"],[4,6,0,3,1,"CONDITIONING"],[6,7,0,3,2,"CONDITIONING"],[7,3,0,8,0,"LATENT"],[9,8,0,9,0,"IMAGE"],[11,13,0,8,1,"VAE"],[12,14,0,6,0,"CLIP"],[13,14,0,7,0,"CLIP"],[14,15,0,3,0,"MODEL"]],"groups":[],"config":{},"extra":{"ds":{"scale":0.7513148009015777,"offset":[-28.24399999999999,50.12000000000015]}},"version":0.4}

Additional Context

(Please add any additional context or steps to reproduce the error here)

werruww · 2024-11-21T03:54:51Z

mat1 and mat2 shapes cannot be multiplied (154x768 and 4096x3072)

werruww · 2024-11-21T03:55:18Z

Manager

1

beautiful scenery nature glass bottle landscape, , purple galaxy bottle,
text, watermark
KSampler
mat1 and mat2 shapes cannot be multiplied (154x768 and 4096x3072)

ComfyUI Error Report

Error Details

Node ID: 3
Node Type: KSampler
Exception Type: RuntimeError
Exception Message: mat1 and mat2 shapes cannot be multiplied (154x768 and 4096x3072)

Stack Trace

  File "/content/ComfyUI/execution.py", line 323, in execute
    output_data, output_ui, has_subgraph = get_output_data(obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)

  File "/content/ComfyUI/execution.py", line 198, in get_output_data
    return_values = _map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)

  File "/content/ComfyUI/execution.py", line 169, in _map_node_over_list
    process_inputs(input_dict, i)

  File "/content/ComfyUI/execution.py", line 158, in process_inputs
    results.append(getattr(obj, func)(**inputs))

  File "/content/ComfyUI/nodes.py", line 1454, in sample
    return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)

  File "/content/ComfyUI/nodes.py", line 1421, in common_ksampler
    samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,

  File "/content/ComfyUI/comfy/sample.py", line 43, in sample
    samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)

  File "/content/ComfyUI/comfy/samplers.py", line 855, in sample
    return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)

  File "/content/ComfyUI/comfy/samplers.py", line 753, in sample
    return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)

  File "/content/ComfyUI/comfy/samplers.py", line 740, in sample
    output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)

  File "/content/ComfyUI/comfy/samplers.py", line 719, in inner_sample
    samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)

  File "/content/ComfyUI/comfy/samplers.py", line 624, in sample
    samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)

  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)

  File "/content/ComfyUI/comfy/k_diffusion/sampling.py", line 155, in sample_euler
    denoised = model(x, sigma_hat * s_in, **extra_args)

  File "/content/ComfyUI/comfy/samplers.py", line 299, in __call__
    out = self.inner_model(x, sigma, model_options=model_options, seed=seed)

  File "/content/ComfyUI/comfy/samplers.py", line 706, in __call__
    return self.predict_noise(*args, **kwargs)

  File "/content/ComfyUI/comfy/samplers.py", line 709, in predict_noise
    return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed)

  File "/content/ComfyUI/comfy/samplers.py", line 279, in sampling_function
    out = calc_cond_batch(model, conds, x, timestep, model_options)

  File "/content/ComfyUI/comfy/samplers.py", line 228, in calc_cond_batch
    output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)

  File "/content/ComfyUI/comfy/model_base.py", line 144, in apply_model
    model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()

  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)

  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)

  File "/content/ComfyUI/comfy/ldm/flux/model.py", line 181, in forward
    out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options)

  File "/content/ComfyUI/comfy/ldm/flux/model.py", line 114, in forward_orig
    txt = self.txt_in(txt)

  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)

  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)

  File "/content/ComfyUI/custom_nodes/ComfyUI_bnb_nf4_fp4_Loaders/__init__.py", line 161, in forward
    return functional_linear_4bits(x, self.weight, self.bias)

  File "/content/ComfyUI/custom_nodes/ComfyUI_bnb_nf4_fp4_Loaders/__init__.py", line 15, in functional_linear_4bits
    out = bnb.matmul_4bit(x, weight.t(), bias=bias, quant_state=weight.quant_state)

  File "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py", line 579, in matmul_4bit
    return MatMul4Bit.apply(A, B, out, bias, quant_state)

  File "/usr/local/lib/python3.10/dist-packages/torch/autograd/function.py", line 575, in apply
    return super().apply(*args, **kwargs)  # type: ignore[misc]

  File "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py", line 509, in forward
    output = torch.nn.functional.linear(A, F.dequantize_4bit(B, quant_state).to(A.dtype).t(), bias)

System Information

ComfyUI Version: v0.3.0-2-g772e620
Arguments: main.py
OS: posix
Python Version: 3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0]
Embedded Python: false
PyTorch Version: 2.5.1+cu121

Devices

Name: cuda:0 Tesla T4 : cudaMallocAsync
- Type: cuda
- VRAM Total: 15835660288
- VRAM Free: 9282298268
- Torch VRAM Total: 6408896512
- Torch VRAM Free: 13869468

Logs

2024-11-21T03:51:59.390636 - [START] Security scan2024-11-21T03:51:59.390657 - 
2024-11-21T03:52:02.323244 - [DONE] Security scan2024-11-21T03:52:02.323265 - 
2024-11-21T03:52:02.432289 - ## ComfyUI-Manager: installing dependencies done.2024-11-21T03:52:02.432345 - 
2024-11-21T03:52:02.432387 - ** ComfyUI startup time:2024-11-21T03:52:02.432421 -  2024-11-21T03:52:02.432455 - 2024-11-21 03:52:02.4323642024-11-21T03:52:02.432503 - 
2024-11-21T03:52:02.432538 - ** Platform:2024-11-21T03:52:02.432565 -  2024-11-21T03:52:02.432590 - Linux2024-11-21T03:52:02.432614 - 
2024-11-21T03:52:02.432641 - ** Python version:2024-11-21T03:52:02.432666 -  2024-11-21T03:52:02.432691 - 3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0]2024-11-21T03:52:02.432717 - 
2024-11-21T03:52:02.432743 - ** Python executable:2024-11-21T03:52:02.432767 -  2024-11-21T03:52:02.432792 - /usr/bin/python32024-11-21T03:52:02.432816 - 
2024-11-21T03:52:02.432841 - ** ComfyUI Path:2024-11-21T03:52:02.432865 -  2024-11-21T03:52:02.432889 - /content/ComfyUI2024-11-21T03:52:02.432914 - 
2024-11-21T03:52:02.432961 - ** Log path:2024-11-21T03:52:02.432989 -  2024-11-21T03:52:02.433015 - /content/ComfyUI/comfyui.log2024-11-21T03:52:02.433040 - 
2024-11-21T03:52:02.440885 - 
Prestartup times for custom nodes:2024-11-21T03:52:02.440935 - 
2024-11-21T03:52:02.440987 -    3.1 seconds:2024-11-21T03:52:02.441019 -  2024-11-21T03:52:02.441048 - /content/ComfyUI/custom_nodes/ComfyUI-Manager2024-11-21T03:52:02.441079 - 
2024-11-21T03:52:02.441108 - 
2024-11-21T03:52:06.744450 - Total VRAM 15102 MB, total RAM 12979 MB
2024-11-21T03:52:06.744652 - pytorch version: 2.5.1+cu121
2024-11-21T03:52:06.846532 - Set vram state to: NORMAL_VRAM
2024-11-21T03:52:06.846788 - Device: cuda:0 Tesla T4 : cudaMallocAsync
2024-11-21T03:52:08.159671 - Using pytorch cross attention
2024-11-21T03:52:10.073818 - [Prompt Server] web root: /content/ComfyUI/web
2024-11-21T03:52:11.631712 - ### Loading: ComfyUI-Manager (V2.51.9)2024-11-21T03:52:11.631780 - 
2024-11-21T03:52:11.698831 - ### ComfyUI Revision: 2844 [772e620e] | Released on '2024-11-20'2024-11-21T03:52:11.698889 - 
2024-11-21T03:52:11.706935 - Traceback (most recent call last):
  File "/content/ComfyUI/nodes.py", line 2024, in load_custom_node
    module_spec.loader.exec_module(module)
  File "<frozen importlib._bootstrap_external>", line 879, in exec_module
  File "<frozen importlib._bootstrap_external>", line 1016, in get_code
  File "<frozen importlib._bootstrap_external>", line 1073, in get_data
FileNotFoundError: [Errno 2] No such file or directory: '/content/ComfyUI/custom_nodes/.ipynb_checkpoints/__init__.py'

2024-11-21T03:52:11.707109 - Cannot import /content/ComfyUI/custom_nodes/.ipynb_checkpoints module for custom nodes: [Errno 2] No such file or directory: '/content/ComfyUI/custom_nodes/.ipynb_checkpoints/__init__.py'
2024-11-21T03:52:12.085819 - [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/alter-list.json2024-11-21T03:52:12.085886 - 
2024-11-21T03:52:12.113613 - 
Import times for custom nodes:
2024-11-21T03:52:12.113778 -    0.0 seconds (IMPORT FAILED): /content/ComfyUI/custom_nodes/.ipynb_checkpoints
2024-11-21T03:52:12.113874 -    0.0 seconds: /content/ComfyUI/custom_nodes/websocket_image_save.py
2024-11-21T03:52:12.113946 -    0.0 seconds: /content/ComfyUI/custom_nodes/ComfyUI_bnb_nf4_fp4_Loaders
2024-11-21T03:52:12.114014 -    0.0 seconds: /content/ComfyUI/custom_nodes/example_node.py
2024-11-21T03:52:12.114078 -    0.0 seconds: /content/ComfyUI/custom_nodes/ComfyUI-GGUF
2024-11-21T03:52:12.114142 -    0.1 seconds: /content/ComfyUI/custom_nodes/ComfyUI-Manager
2024-11-21T03:52:12.114203 -    0.4 seconds: /content/ComfyUI/custom_nodes/ComfyUI_UNet_bitsandbytes_NF4
2024-11-21T03:52:12.114267 - 
2024-11-21T03:52:12.125396 - [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/model-list.json2024-11-21T03:52:12.125463 - 
2024-11-21T03:52:12.134704 - Starting server

2024-11-21T03:52:12.135073 - To see the GUI go to: http://127.0.0.1:8188
2024-11-21T03:52:12.158862 - [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/github-stats.json2024-11-21T03:52:12.158914 - 
2024-11-21T03:52:12.203435 - [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/extension-node-map.json2024-11-21T03:52:12.203499 - 
2024-11-21T03:52:12.254674 - [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json2024-11-21T03:52:12.254725 - 
2024-11-21T03:53:23.492952 - FETCH DATA from: /content/ComfyUI/custom_nodes/ComfyUI-Manager/extension-node-map.json2024-11-21T03:53:23.493021 - 2024-11-21T03:53:23.508399 -  [DONE]2024-11-21T03:53:23.508459 - 
2024-11-21T03:53:49.061136 - got prompt
2024-11-21T03:53:49.383607 - Using pytorch attention in VAE
2024-11-21T03:53:49.386239 - Using pytorch attention in VAE
2024-11-21T03:53:52.920672 - clip missing: ['text_projection.weight']
2024-11-21T03:53:52.928878 - Requested to load SD1ClipModel
2024-11-21T03:53:52.929028 - Loading 1 new model
2024-11-21T03:53:53.004989 - loaded completely 0.0 235.84423828125 True
2024-11-21T03:53:56.800627 - model weight dtype torch.bfloat16, manual cast: torch.float16
2024-11-21T03:53:56.818848 - model_type FLOW
2024-11-21T03:53:56.920491 - Requested to load Flux
2024-11-21T03:53:56.920651 - Loading 1 new model
2024-11-21T03:54:29.347181 - loaded completely 0.0 5854.812986373901 True
2024-11-21T03:54:29.465658 - 
  0% 0/20 [00:00<?, ?it/s]2024-11-21T03:54:29.891898 - 
  0% 0/20 [00:00<?, ?it/s]2024-11-21T03:54:29.891971 - 
2024-11-21T03:54:30.099972 - !!! Exception during processing !!! mat1 and mat2 shapes cannot be multiplied (154x768 and 4096x3072)
2024-11-21T03:54:30.119360 - Traceback (most recent call last):
  File "/content/ComfyUI/execution.py", line 323, in execute
    output_data, output_ui, has_subgraph = get_output_data(obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)
  File "/content/ComfyUI/execution.py", line 198, in get_output_data
    return_values = _map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)
  File "/content/ComfyUI/execution.py", line 169, in _map_node_over_list
    process_inputs(input_dict, i)
  File "/content/ComfyUI/execution.py", line 158, in process_inputs
    results.append(getattr(obj, func)(**inputs))
  File "/content/ComfyUI/nodes.py", line 1454, in sample
    return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
  File "/content/ComfyUI/nodes.py", line 1421, in common_ksampler
    samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
  File "/content/ComfyUI/comfy/sample.py", line 43, in sample
    samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
  File "/content/ComfyUI/comfy/samplers.py", line 855, in sample
    return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
  File "/content/ComfyUI/comfy/samplers.py", line 753, in sample
    return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
  File "/content/ComfyUI/comfy/samplers.py", line 740, in sample
    output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
  File "/content/ComfyUI/comfy/samplers.py", line 719, in inner_sample
    samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
  File "/content/ComfyUI/comfy/samplers.py", line 624, in sample
    samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
  File "/content/ComfyUI/comfy/k_diffusion/sampling.py", line 155, in sample_euler
    denoised = model(x, sigma_hat * s_in, **extra_args)
  File "/content/ComfyUI/comfy/samplers.py", line 299, in __call__
    out = self.inner_model(x, sigma, model_options=model_options, seed=seed)
  File "/content/ComfyUI/comfy/samplers.py", line 706, in __call__
    return self.predict_noise(*args, **kwargs)
  File "/content/ComfyUI/comfy/samplers.py", line 709, in predict_noise
    return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed)
  File "/content/ComfyUI/comfy/samplers.py", line 279, in sampling_function
    out = calc_cond_batch(model, conds, x, timestep, model_options)
  File "/content/ComfyUI/comfy/samplers.py", line 228, in calc_cond_batch
    output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
  File "/content/ComfyUI/comfy/model_base.py", line 144, in apply_model
    model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/content/ComfyUI/comfy/ldm/flux/model.py", line 181, in forward
    out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options)
  File "/content/ComfyUI/comfy/ldm/flux/model.py", line 114, in forward_orig
    txt = self.txt_in(txt)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/content/ComfyUI/custom_nodes/ComfyUI_bnb_nf4_fp4_Loaders/__init__.py", line 161, in forward
    return functional_linear_4bits(x, self.weight, self.bias)
  File "/content/ComfyUI/custom_nodes/ComfyUI_bnb_nf4_fp4_Loaders/__init__.py", line 15, in functional_linear_4bits
    out = bnb.matmul_4bit(x, weight.t(), bias=bias, quant_state=weight.quant_state)
  File "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py", line 579, in matmul_4bit
    return MatMul4Bit.apply(A, B, out, bias, quant_state)
  File "/usr/local/lib/python3.10/dist-packages/torch/autograd/function.py", line 575, in apply
    return super().apply(*args, **kwargs)  # type: ignore[misc]
  File "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py", line 509, in forward
    output = torch.nn.functional.linear(A, F.dequantize_4bit(B, quant_state).to(A.dtype).t(), bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (154x768 and 4096x3072)

2024-11-21T03:54:30.120069 - Prompt executed in 41.06 seconds

Attached Workflow

Please make sure that workflow does not contain any sensitive information such as API keys or passwords.

{"last_node_id":15,"last_link_id":14,"nodes":[{"id":3,"type":"KSampler","pos":[863,186],"size":[315,262],"flags":{},"order":6,"mode":0,"inputs":[{"name":"model","type":"MODEL","link":14},{"name":"positive","type":"CONDITIONING","link":4},{"name":"negative","type":"CONDITIONING","link":6},{"name":"latent_image","type":"LATENT","link":2}],"outputs":[{"name":"LATENT","type":"LATENT","links":[7],"slot_index":0}],"properties":{"Node name for S&R":"KSampler"},"widgets_values":[16368807634753,"randomize",20,8,"euler","normal",1]},{"id":5,"type":"EmptyLatentImage","pos":[473,609],"size":[315,106],"flags":{},"order":0,"mode":0,"inputs":[],"outputs":[{"name":"LATENT","type":"LATENT","links":[2],"slot_index":0}],"properties":{"Node name for S&R":"EmptyLatentImage"},"widgets_values":[512,512,1]},{"id":6,"type":"CLIPTextEncode","pos":[415,186],"size":[422.84503173828125,164.31304931640625],"flags":{},"order":4,"mode":0,"inputs":[{"name":"clip","type":"CLIP","link":12}],"outputs":[{"name":"CONDITIONING","type":"CONDITIONING","links":[4],"slot_index":0}],"properties":{"Node name for S&R":"CLIPTextEncode"},"widgets_values":["beautiful scenery nature glass bottle landscape, , purple galaxy bottle,"]},{"id":7,"type":"CLIPTextEncode","pos":[413,389],"size":[425.27801513671875,180.6060791015625],"flags":{},"order":5,"mode":0,"inputs":[{"name":"clip","type":"CLIP","link":13}],"outputs":[{"name":"CONDITIONING","type":"CONDITIONING","links":[6],"slot_index":0}],"properties":{"Node name for S&R":"CLIPTextEncode"},"widgets_values":["text, watermark"]},{"id":8,"type":"VAEDecode","pos":[1209,188],"size":[210,46],"flags":{},"order":7,"mode":0,"inputs":[{"name":"samples","type":"LATENT","link":7},{"name":"vae","type":"VAE","link":11}],"outputs":[{"name":"IMAGE","type":"IMAGE","links":[9],"slot_index":0}],"properties":{"Node name for S&R":"VAEDecode"},"widgets_values":[]},{"id":9,"type":"SaveImage","pos":[1451,189],"size":[210,58],"flags":{},"order":8,"mode":0,"inputs":[{"name":"images","type":"IMAGE","link":9}],"outputs":[],"properties":{},"widgets_values":["ComfyUI"]},{"id":13,"type":"VAELoader","pos":[38,499],"size":[315,58],"flags":{},"order":1,"mode":0,"inputs":[],"outputs":[{"name":"VAE","type":"VAE","links":[11],"slot_index":0}],"properties":{"Node name for S&R":"VAELoader"},"widgets_values":["sdxl_vae.safetensors"]},{"id":14,"type":"CLIPLoader","pos":[-14,665],"size":[315,82],"flags":{},"order":2,"mode":0,"inputs":[],"outputs":[{"name":"CLIP","type":"CLIP","links":[12,13],"slot_index":0}],"properties":{"Node name for S&R":"CLIPLoader"},"widgets_values":["clip_l.safetensors","stable_diffusion"]},{"id":15,"type":"UNETLoaderNF4","pos":[17,281],"size":[315,58],"flags":{},"order":3,"mode":0,"inputs":[],"outputs":[{"name":"MODEL","type":"MODEL","links":[14],"slot_index":0}],"properties":{"Node name for S&R":"UNETLoaderNF4"},"widgets_values":["flux1-schnell-bnb-nf4-unet.safetensors"]}],"links":[[2,5,0,3,3,"LATENT"],[4,6,0,3,1,"CONDITIONING"],[6,7,0,3,2,"CONDITIONING"],[7,3,0,8,0,"LATENT"],[9,8,0,9,0,"IMAGE"],[11,13,0,8,1,"VAE"],[12,14,0,6,0,"CLIP"],[13,14,0,7,0,"CLIP"],[14,15,0,3,0,"MODEL"]],"groups":[],"config":{},"extra":{"ds":{"scale":1,"offset":[-2,0]}},"version":0.4}

Additional Context

(Please add any additional context or steps to reproduce the error here)

werruww · 2024-11-21T04:01:10Z

werruww · 2024-11-21T04:27:02Z

not run

werruww · 2024-11-21T04:27:14Z

Unsaved Workflow33.json

werruww · 2024-11-21T05:28:26Z

it is run
colab t4
https://www.patreon.com/posts/flux-how-to-109332332

Flux_NF4.json

/content/ComfyUI/models/checkpoints/flux1-dev-bnb-nf4.safetensors

/content/ComfyUI/models/clip/t5xxl_fp16.safetensors

/content/ComfyUI/models/vae/sdxl_vae.safetensors

%cd /content/ComfyUI
from pyngrok import ngrok

Set the authentication token

ngrok.set_auth_token("xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")

Open an ngrok tunnel to the Gazebo server port (11345)

public_url = ngrok.connect(8188)
print("Public URL:", public_url)
!python main.py

Provide feedback

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not run #51

not run #51

werruww commented Nov 21, 2024

werruww commented Nov 21, 2024

werruww commented Nov 21, 2024

werruww commented Nov 21, 2024

werruww commented Nov 21, 2024

werruww commented Nov 21, 2024

werruww commented Nov 21, 2024

werruww commented Nov 21, 2024

not run #51

not run #51

Comments

werruww commented Nov 21, 2024

werruww commented Nov 21, 2024

ComfyUI Error Report

Error Details

Stack Trace

System Information

Devices

Logs

Attached Workflow

Additional Context

werruww commented Nov 21, 2024

werruww commented Nov 21, 2024

ComfyUI Error Report

Error Details

Stack Trace

System Information

Devices

Logs

Attached Workflow

Additional Context

werruww commented Nov 21, 2024

werruww commented Nov 21, 2024

werruww commented Nov 21, 2024

werruww commented Nov 21, 2024

Set the authentication token

Open an ngrok tunnel to the Gazebo server port (11345)