How to train an image as a control element?

Nobody has claimed this yet.

sample data set is
```
source = cv2.cvtColor(source, cv2.COLOR_BGR2RGB)
target = cv2.cvtColor(target, cv2.COLOR_BGR2RGB)

# Normalize source images to [0, 1].
source = source.astype(np.float32) / 255.0

# Normalize target images to [-1, 1].
target = (target.astype(np.float32) / 127.5) - 1.0

return dict(jpg=target, txt=prompt, hint=source)
```

I tried hint source as 6 channel by concat 2images
and make yaml file
```
model:
target: cldm.cldm.ControlLDM
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
control_key: "hint"
image_size: 64
channels: 7
cond_stage_trainable: false
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
only_mid_control: False

control_stage_config:
target: cldm.cldm.ControlNet
params:
image_size: 32 # unused
in_channels: 7
hint_channels: 6
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False

unet_config:
target: cldm.cldm.ControlledUnetModel
params:
image_size: 32 # unused
in_channels: 7
out_channels: 7
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False

first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity

cond_stage_config:
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
```

but I got error
```
Traceback (most recent call last):
File "tool_add_control.py", line 49, in
model.load_state_dict(target_dict, strict=True)
File "/home/anaconda3/envs/control/lib/python3.8/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for ControlLDM:
size mismatch for model.diffusion_model.input_blocks.0.0.weight: copying a param with shape torch.Size([320, 4, 3, 3]) from checkpoint, the shape in current model is torch.Size([320, 7, 3, 3]).
size mismatch for model.diffusion_model.out.2.weight: copying a param with shape torch.Size([4, 320, 3, 3]) from checkpoint, the shape in current model is torch.Size([7, 320, 3, 3]).
size mismatch for model.diffusion_model.out.2.bias: copying a param with shape torch.Size([4]) from checkpoint, the shape in current model is torch.Size([7]).
size mismatch for control_model.input_blocks.0.0.weight: copying a param with shape torch.Size([320, 4, 3, 3]) from checkpoint, the shape in current model is torch.Size([320, 7, 3, 3]).
```
or I want to image instead of txt
do you know how to set ?

Inspect tool_add_control.py at line 49 and the supplied ControlLDM YAML; compare the checkpoint tensor shapes with the configured channel counts. Confirm the intended conditioning input and checkpoint can be loaded without size-mismatch errors.

Written by the indexing model from the issue text.