not run

[werruww]

/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]

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]

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

[werruww]

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]

[werruww]

not run

[werruww]

Unsaved Workflow33.json

[werruww]

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