Train problem,the guided-diffusion;s pretrained does't work

[zhangbaijin]

Thanks for your contributions, and i changed the repaint into guided-diffusion, there is problem
RuntimeError: Error(s) in loading state_dict for UNetModel: Missing key(s) in state_dict: "input_blocks.3.0.in_layers.0.weight", "input_blocks.3.0.in_layers.0.bias", "input_blocks.3.0.in_layers.2.weight", "input_blocks.3.0.in_layers.2.bias", "input_blocks.3.0.emb_layers.1.weight", "input_blocks.3.0.emb_layers.1.bias", "input_blocks.3.0.out_layers.0.weight", "input_blocks.3.0.out_layers.0.bias", "input_blocks.3.0.out_layers.3.weight", "input_blocks.3.0.out_layers.3.bias", "input_blocks.6.0.in_layers.0.weight", "input_blocks.6.0.in_layers.0.bias", "input_blocks.6.0.in_layers.2.weight", "input_blocks.6.0.in_layers.2.bias", "input_blocks.6.0.emb_layers.1.weight", "input_blocks.6.0.emb_layers.1.bias", "input_blocks.6.0.out_layers.0.weight", "input_blocks.6.0.out_layers.0.bias", "input_blocks.6.0.out_layers.3.weight", "input_blocks.6.0.out_layers.3.bias", "input_blocks.9.0.in_layers.0.weight", "input_blocks.9.0.in_layers.0.bias", "input_blocks.9.0.in_layers.2.weight", "input_blocks.9.0.in_layers.2.bias", "input_blocks.9.0.emb_layers.1.weight", "input_blocks.9.0.emb_layers.1.bias", "input_blocks.9.0.out_layers.0.weight", "input_blocks.9.0.out_layers.0.bias", "input_blocks.9.0.out_layers.3.weight", "input_blocks.9.0.out_layers.3.bias", "input_blocks.10.1.norm.weight", "input_blocks.10.1.norm.bias", "input_blocks.10.1.qkv.weight", "input_blocks.10.1.qkv.bias", "input_blocks.10.1.proj_out.weight", "input_blocks.10.1.proj_out.bias", "input_blocks.11.1.norm.weight", "input_blocks.11.1.norm.bias", "input_blocks.11.1.qkv.weight", "input_blocks.11.1.qkv.bias", "input_blocks.11.1.proj_out.weight", "input_blocks.11.1.proj_out.bias", "input_blocks.12.0.in_layers.0.weight", "input_blocks.12.0.in_layers.0.bias", "input_blocks.12.0.in_layers.2.weight", "input_blocks.12.0.in_layers.2.bias", "input_blocks.12.0.emb_layers.1.weight", "input_blocks.12.0.emb_layers.1.bias", "input_blocks.12.0.out_layers.0.weight", "input_blocks.12.0.out_layers.0.bias", "input_blocks.12.0.out_layers.3.weight", "input_blocks.12.0.out_layers.3.bias", "input_blocks.15.0.in_layers.0.weight", "input_blocks.15.0.in_layers.0.bias", "input_blocks.15.0.in_layers.2.weight", "input_blocks.15.0.in_layers.2.bias", "input_blocks.15.0.emb_layers.1.weight", "input_blocks.15.0.emb_layers.1.bias", "input_blocks.15.0.out_layers.0.weight", "input_blocks.15.0.out_layers.0.bias", "input_blocks.15.0.out_layers.3.weight", "input_blocks.15.0.out_layers.3.bias", "output_blocks.2.2.in_layers.0.weight", "output_blocks.2.2.in_layers.0.bias", "output_blocks.2.2.in_layers.2.weight", "output_blocks.2.2.in_layers.2.bias", "output_blocks.2.2.emb_layers.1.weight", "output_blocks.2.2.emb_layers.1.bias", "output_blocks.2.2.out_layers.0.weight", "output_blocks.2.2.out_layers.0.bias", "output_blocks.2.2.out_layers.3.weight", "output_blocks.2.2.out_layers.3.bias", "output_blocks.5.2.in_layers.0.weight", "output_blocks.5.2.in_layers.0.bias", "output_blocks.5.2.in_layers.2.weight", "output_blocks.5.2.in_layers.2.bias", "output_blocks.5.2.emb_layers.1.weight", "output_blocks.5.2.emb_layers.1.bias", "output_blocks.5.2.out_layers.0.weight", "output_blocks.5.2.out_layers.0.bias", "output_blocks.5.2.out_layers.3.weight", "output_blocks.5.2.out_layers.3.bias", "output_blocks.6.1.norm.weight", "output_blocks.6.1.norm.bias", "output_blocks.6.1.qkv.weight", "output_blocks.6.1.qkv.bias", "output_blocks.6.1.proj_out.weight", "output_blocks.6.1.proj_out.bias", "output_blocks.7.1.norm.weight", "output_blocks.7.1.norm.bias", "output_blocks.7.1.qkv.weight", "output_blocks.7.1.qkv.bias", "output_blocks.7.1.proj_out.weight", "output_blocks.7.1.proj_out.bias", "output_blocks.8.1.norm.weight", "output_blocks.8.1.norm.bias", "output_blocks.8.1.qkv.weight", "output_blocks.8.1.qkv.bias", "output_blocks.8.1.proj_out.weight", "output_blocks.8.1.proj_out.bias", "output_blocks.8.2.in_layers.0.weight", "output_blocks.8.2.in_layers.0.bias", "output_blocks.8.2.in_layers.2.weight", "output_blocks.8.2.in_layers.2.bias", "output_blocks.8.2.emb_layers.1.weight", "output_blocks.8.2.emb_layers.1.bias", "output_blocks.8.2.out_layers.0.weight", "output_blocks.8.2.out_layers.0.bias", "output_blocks.8.2.out_layers.3.weight", "output_blocks.8.2.out_layers.3.bias", "output_blocks.11.1.in_layers.0.weight", "output_blocks.11.1.in_layers.0.bias", "output_blocks.11.1.in_layers.2.weight", "output_blocks.11.1.in_layers.2.bias", "output_blocks.11.1.emb_layers.1.weight", "output_blocks.11.1.emb_layers.1.bias", "output_blocks.11.1.out_layers.0.weight", "output_blocks.11.1.out_layers.0.bias", "output_blocks.11.1.out_layers.3.weight", "output_blocks.11.1.out_layers.3.bias", "output_blocks.14.1.in_layers.0.weight", "output_blocks.14.1.in_layers.0.bias", "output_blocks.14.1.in_layers.2.weight", "output_blocks.14.1.in_layers.2.bias", "output_blocks.14.1.emb_layers.1.weight", "output_blocks.14.1.emb_layers.1.bias", "output_blocks.14.1.out_layers.0.weight", "output_blocks.14.1.out_layers.0.bias", "output_blocks.14.1.out_layers.3.weight", "output_blocks.14.1.out_layers.3.bias". Unexpected key(s) in state_dict: "input_blocks.3.0.op.weight", "input_blocks.3.0.op.bias", "input_blocks.6.0.op.weight", "input_blocks.6.0.op.bias", "input_blocks.9.0.op.weight", "input_blocks.9.0.op.bias", "input_blocks.12.0.op.weight", "input_blocks.12.0.op.bias", "input_blocks.15.0.op.weight", "input_blocks.15.0.op.bias", "output_blocks.2.2.conv.weight", "output_blocks.2.2.conv.bias", "output_blocks.5.2.conv.weight", "output_blocks.5.2.conv.bias", "output_blocks.8.1.conv.weight", "output_blocks.8.1.conv.bias", "output_blocks.11.1.conv.weight", "output_blocks.11.1.conv.bias", "output_blocks.14.1.conv.weight", "output_blocks.14.1.conv.bias".

Can you tell me how to train it ?

[zhangbaijin]

And we find the checkpoint of Repaint is 2.1G, the checkpoint of guided-diffusion and improved-diffusion is 1.8G, how to train it??
Only repalce the repaint's unet into guided-diffusion doesn't work. Can you provide the train code??

[caiyuhao]

same question

[zhangbaijin]

The problem is solved

[dlrlseong]

@zhangbaijin
Can you tell me how to solve it ?

[xyz-xdx]

@zhangbaijin
Could you please tell me how to solve the above problem? Thanks！

[alicedingyueming]

The problem is solved
I have a similar problem. I wish to use my pretrained-model from guide-diffusion to repaint, and meet the same miss-matching problem. could you please help me?

[shidedh]

This answer helped me solve part of the problem.

I'm running into a different error now but I hope the answer is helpful to you as well 🙂

[hzy-del]

The problem is solved

can you provide your train method?

[dlrlseong]

The problem is solved

can you provide your train method?

I solved this a long time ago by modifying the confs file appropriately. I'm sorry I couldn't help you because I couldn't find the information at the time.

[zhangbaijin]

MODEL_FLAGS="--image_size 256 --attention_resolutions 32,16,8 --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --num_heads 4 --resblock_updown true --learn_sigma True --use_scale_shift_norm true --learn_sigma true --timestep_respacing 250 --use_fp16 false --use_kl false " DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear --rescale_learned_sigmas False" TRAIN_FLAGS="--lr 1e-4 --microbatch 4 --dropout 0.0"