Using Multiple ControlNet in Blender.
First, of course, is to run web ui with --api
commandline argument
set COMMANDLINE_ARGS=--api
You have to install the Mikubill/sd-webui-controlnet
extension in A1111 and download the ControlNet models.
Please refer to the installation instructions from Mikubill/sd-webui-controlnet.
Notes
Allow other script to control this extension
in settings for API access.Multi ControlNet: Max models amount (requires restart)
in the settings. Note that you will need to restart the WebUI for changes to take effect.multicn.py
code into your blender Scripting pane.Basically, the script utilizes Blender Compositor to generate the required maps and then sends them to AUTOMATIC1111.
To generate the desired output, you need to make adjustments to either the code or Blender Compositor nodes before pressing F12
. To simplify this process, I have provided a basic Blender template that sends depth and segmentation maps to ControlNet.
Here is a brief tutorial on how to modify to suit @toyxyz3's rig if you wish to send openpose/depth/canny maps.
Notes
controlnet_model
, hash does matter. You can get your local installed ControlNet models list from here.Before you hit "Run Script", here are the parameters that you may want to modify in the scripts:
# specify your images output folder
IMAGE_FOLDER = "//sd_results"
# if you don't want to send your maps to AI, set this option to False
is_using_ai = True
# which maps are you going to send to AI
is_send_canny = False
is_send_depth = False
is_send_bone = True
is_send_seg = False
# prepare data for API
params = {
"prompt": "a room",
"negative_prompt": "(worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality",
"width": get_output_width(scene),
"height": get_output_height(scene),
"sampler_index": "DPM++ SDE Karras",
"sampler_name": "",
"batch_size": 1,
"n_iter": 1,
"steps": 20,
"cfg_scale": 7,
"seed": -1,
"subseed": -1,
"subseed_strength": 0,
"restore_faces": False,
"enable_hr": False,
"hr_scale": 1.5,
"hr_upscaler": "R-ESRGAN General WDN 4xV3",
"denoising_strength": 0.5,
"hr_second_pass_steps": 10,
"hr_resize_x": 0,
"hr_resize_y": 0,
"firstphase_width": 0,
"firstphase_height": 0,
"override_settings": {"CLIP_stop_at_last_layers": 2},
"override_settings_restore_afterwards": True,
"alwayson_scripts": {"controlnet": {"args": []}},
}
canny_cn_units = {
"mask": "",
"module": "none",
"model": "diff_control_sd15_canny_fp16 [ea6e3b9c]",
"weight": 1.2,
"resize_mode": "Scale to Fit (Inner Fit)",
"lowvram": False,
"processor_res": 64,
"threshold_a": 64,
"threshold_b": 64,
"guidance": 1,
"guidance_start": 0.19,
"guidance_end": 1,
"guessmode": False,
}
depth_cn_units = {
"mask": "",
"module": "none",
"model": "diff_control_sd15_depth_fp16 [978ef0a1]",
"weight": 1.2,
"resize_mode": "Scale to Fit (Inner Fit)",
"lowvram": False,
"processor_res": 64,
"threshold_a": 64,
"threshold_b": 64,
"guidance": 1,
"guidance_start": 0.19,
"guidance_end": 1,
"guessmode": False,
}
bone_cn_units = {
"mask": "",
"module": "none",
"model": "diff_control_sd15_openpose_fp16 [1723948e]",
"weight": 1.1,
"resize_mode": "Scale to Fit (Inner Fit)",
"lowvram": False,
"processor_res": 64,
"threshold_a": 64,
"threshold_b": 64,
"guidance": 1,
"guidance_start": 0,
"guidance_end": 1,
"guessmode": False,
}
seg_cn_units = {
"mask": "",
"module": "none",
"model": "diff_control_sd15_seg_fp16 [a1e85e27]",
"weight": 1,
"resize_mode": "Scale to Fit (Inner Fit)",
"lowvram": False,
"processor_res": 64,
"threshold_a": 64,
"threshold_b": 64,
"guidance": 1,
"guidance_start": 0,
"guidance_end": 1,
"guessmode": False,
}
To create 150 ControlNet segmentation colors materials, run seg.py
. Check out this tweet for instructions.