Continuing training of a ControlNet

Nobody has claimed this yet.

Following [the tutorial](https://github.com/lllyasviel/ControlNet/blob/main/docs/train.md#step-3---what-sd-model-do-you-want-to-control) I can successfully download SD, add ControlNet, and train it.

Now, I want to continue training the [OpenPose model](https://huggingface.co/lllyasviel/sd-controlnet-openpose) using a different pose estimation tool. I downloaded the `bin` file from here https://huggingface.co/lllyasviel/sd-controlnet-openpose/tree/main and did not add ControlNet (since it should be there), but then when I try the following:

```py
model = create_model('./models/cldm_v15.yaml').cpu()
model.load_state_dict(load_state_dict('diffusion_pytorch_model.bin', location='cpu'))
```

I get this error (truncated because longer than 65K characters, full [here](https://pastebin.com/4kvEcxFH))

```log
Missing key(s) in state_dict: "betas", "alphas_cumprod", "alphas_cumprod_prev", "sqrt_alphas_cumprod", "sqrt_one_minus_alphas_cumprod", "log_one_minus_alphas_cumprod", "sqrt_recip_alphas_cumprod", "sqrt_recipm1_alphas_cumprod", "posterior_variance", "posterior_log_variance_clipped", "posterior_mean_coef1", "posterior_mean_coef2", "logvar", "model.diffusion_model.time_embed.0.weight", "model.diffusion_model.time_embed.0.bias", "model.diffusion_model.time_embed.2.weight", "model.diffusion_model.time_embed.2.bias",
Unexpected key(s) in state_dict: "conv_in.weight", "conv_in.bias", "time_embedding.linear_1.weight", "time_embedding.linear_1.bias", "time_embedding.linear_2.weight", "time_embedding.linear_2.bias", "controlnet_cond_embedding.conv_in.weight", "controlnet_cond_embedding.conv_in.bias", "controlnet_cond_embedding.blocks.0.weight", "controlnet_cond_embedding.blocks.0.bias", "controlnet_cond_embedding.blocks.1.weight", "controlnet_cond_embedding.blocks.1.bias", "controlnet_cond_embedding.blocks.2.weight", "controlnet_cond_embedding.blocks.2.bias", "controlnet_cond_embedding.blocks.3.weight", "controlnet_cond_embedding.blocks.3.bias", "controlnet_cond_embedding.blocks.4.weight", "controlnet_cond_embedding.blocks.4.bias", "controlnet_cond_embedding.blocks.5.weight", "controlnet_cond_embedding.blocks.5.bias", "controlnet_cond_embedding.conv_out.weight", "controlnet_cond_embedding.conv_out.bias", "down_blocks.0.attentions.0.norm.weight", "down_blocks.0.attentions.0.norm.bias", "down_blocks.0.attentions.0.proj_in.weight", "down_blocks.0.attentions.0.proj_in.bias", "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_q.weight", "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_k.weight",
```

How should I correctly load the OpenPose model for continuing training?

Start with docs/train.md at the Step 3 section, then inspect models/cldm_v15.yaml and the create_model and load_state_dict entry points shown in the report. Compare the expected keys with diffusion_pytorch_model.bin and use the full traceback if needed. Done means the OpenPose checkpoint loads successfully and can be used for continued training.

Written by the indexing model from the issue text.