How to generate images using your own trained controlnet model?

Following the official training instructions, I have made the data and and trained it, also based on the test images, I found the best performance at the 22nd epoch, so I got the following from the training interrupted at the 22nd epoch:.
A file lighting_logs, I found that the updated ckpt file is stored in the lighting_logs folder, in version1 (because I trained 1 time before, the first time was version0, that time was just a trial). there are three files in version1, the first one is hparams.yaml, which has only one pair of {}. The first is hparams.yaml, which has only one pair of {}. The second one is events.out.tfevents.1689045604.autodl-container-63e511b6ae-8ebbd39d.10590.0, and the last one is the checkpoints folder, which has only one file named epoch=22- step=5335.
But I found that in the py files that call the weights for image generation, they are all either directly calling the pth file that controls controlnet:
model = create_model('. /models/cldm_v15.yaml').cpu()
model.load_state_dict(load_state_dict('. /models/control_sd15_scribble.pth', location='cuda'))
Either that or they call the pre-training ckpt weights of the sd model before calling the pth file that controls controlnet. :
model_name = 'control_v11p_sd15_scribble'
model = create_model(f'. /models/{model_name}.yaml').cpu()
model.load_state_dict(load_state_dict('. /models/v1-5-pruned.ckpt', location='cuda'), strict=False)
model.load_state_dict(load_state_dict(f'. /models/{model_name}.pth', location='cuda'), strict=False)
model = model.cuda()
Also the official yaml file they call has values:
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: 4
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: 4
hint_channels: 3
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: 4
out_channels: 4
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
May I ask what is going on here? I have considered the effect of the following
1 I didn't set the epoch number, because the official training tutorial didn't teach me how to set it, so I was stopping directly at round 22, which may have caused some loss to the model and thus nothing in the yaml
But if I need to set additional epoch number, how should I set it?
2 I set SD lock to true during training, which means that I might modify the weights of the SD model a bit as well, so the ckpt I get should be a direct replacement for the official one? And where should I get it from when PPT? Should I convert ckpt to pth?

Start with the official training instructions and the generation Python files referenced in the report. Compare the checkpoint at lighting_logs/version1/checkpoints/epoch=22-step=5335 with the loading paths for ./models/*.yaml, *.pth, and v1-5-pruned.ckpt. Done means documenting the supported checkpoint format and reproducible steps for generating images with the trained ControlNet model.

Written by the indexing model from the issue text.