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doctorpangloss
2024-05-21 11:38:20 -07:00
parent f69b6225c0
commit 5b9eadc165
6 changed files with 23 additions and 18 deletions
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docs/examples/configuration/extra_model_paths.yaml
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#Rename this to extra_model_paths.yaml and ComfyUI will load it
#config for a1111 ui
#all you have to do is change the base_path to where yours is installed
a1111:
base_path: path/to/stable-diffusion-webui/
checkpoints: models/Stable-diffusion
configs: models/Stable-diffusion
vae: models/VAE
loras: |
models/Lora
models/LyCORIS
upscale_models: |
models/ESRGAN
models/RealESRGAN
models/SwinIR
embeddings: embeddings
hypernetworks: models/hypernetworks
controlnet: models/ControlNet
#config for comfyui
#your base path should be either an existing comfy install or a central folder where you store all of your models, loras, etc.
#comfyui:
# base_path: path/to/comfyui/
# checkpoints: models/checkpoints/
# clip: models/clip/
# clip_vision: models/clip_vision/
# configs: models/configs/
# controlnet: models/controlnet/
# embeddings: models/embeddings/
# loras: models/loras/
# upscale_models: models/upscale_models/
# vae: models/vae/
#other_ui:
# base_path: path/to/ui
# checkpoints: models/checkpoints
# gligen: models/gligen
# custom_nodes: path/custom_nodes
docs/examples/script_examples/basic_api_example.py
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# This sample shows how to execute a ComfyUI workflow, saving an image file to the location you specify.
# It does not require the server to be run. It runs ComfyUI embedded, as a library. No process is started.
#
# This script does not need to run within a ComfyUI directory. Instead, this can be used inside your own
# Python application or located elsewhere. It should **not** be in the Git repository directory.
#
# First, you will need to install ComfyUI. Follow the **Manual Install (Windows, Linux, macOS)** instructions in the
# README.md. If you are an experienced developer, instead run `pip install git+https://github.com/hiddenswitch/ComfyUI.git`
#
# Now you should develop your workflow. Start ComfyUI as normal; navigate to "Settings" in the menu, and check "Enable
# Dev mode Options". Then click "Save (API Format)". Copy and paste the contents of this file here:
_PROMPT_FROM_WEB_UI = {
"3": {
"class_type": "KSampler",
"inputs": {
"cfg": 8,
"denoise": 1,
"latent_image": [
"5",
0
],
"model": [
"4",
0
],
"negative": [
"7",
0
],
"positive": [
"6",
0
],
"sampler_name": "euler",
"scheduler": "normal",
"seed": 8566257,
"steps": 20
}
},
"4": {
"class_type": "CheckpointLoaderSimple",
"inputs": {
"ckpt_name": "v1-5-pruned-emaonly.ckpt"
}
},
"5": {
"class_type": "EmptyLatentImage",
"inputs": {
"batch_size": 1,
"height": 512,
"width": 512
}
},
"6": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"4",
1
],
"text": "masterpiece best quality girl"
}
},
"7": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"4",
1
],
"text": "bad hands"
}
},
"8": {
"class_type": "VAEDecode",
"inputs": {
"samples": [
"3",
0
],
"vae": [
"4",
2
]
}
},
"9": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"8",
0
]
}
}
}
# Observe this is an ordinary dictionary. The JSON that was saved from the workflow is compatible with Python syntax.
#
# Now, QUIT AND CLOSE YOUR COMFYUI SERVER. You don't need it anymore. This script will handle starting and stopping
# the server for you. Actually, it will create an object that does the same thing that pressing Queue Prompt does.
#
# We'll now write the entrypoint of our script. This is an `async def main()` because async helps us start and stop the
# code object that will run your workflow, just like pressing the Queue Prompt button.
async def main():
import copy
# Let's make some changes to the prompt. First we'll change the input text:
prompt_dict = copy.deepcopy(_PROMPT_FROM_WEB_UI)
prompt_dict["6"]["inputs"]["text"] = "masterpiece best quality man"
# Let's set the seed for our KSampler node:
prompt_dict["3"]["inputs"]["seed"] = 5
# Now we will validate the prompt. This Prompt class contains everything we need to validate the prompt.
from comfy.api.components.schema.prompt import Prompt
prompt = Prompt.validate(prompt_dict)
# Your prompt is ready to be processed.
# You should **not** be running the ComfyUI application (the thing you start with /main.py). You don't need it. You
# are not making any HTTP requests, you are not running a server, you are not connecting to anything, you are not
# executing the main.py from the ComfyUI git repository, you don't even need that Git repository located anywhere.
from comfy.cli_args_types import Configuration
# Let's specify some settings. Suppose this is the structure of your directories:
# C:/Users/comfyanonymous/Documents/models
# C:/Users/comfyanonymous/Documents/models/checkpoints
# C:/Users/comfyanonymous/Documents/models/loras
# C:/Users/comfyanonymous/Documents/outputs
# Then your "current working directory" (`cwd`) should be set to "C:/Users/comfyanonymous/Documents":
# configuration.cwd = "C:/Users/comfyanonymous/Documents/"
# Or, if your models directory is located in the same directory as this script:
# configuration.cwd = os.path.dirname(__file__)
configuration = Configuration()
from comfy.client.embedded_comfy_client import EmbeddedComfyClient
async with EmbeddedComfyClient(configuration=configuration) as client:
# This will run your prompt
outputs = await client.queue_prompt(prompt)
# At this point, your prompt is finished and all the outputs, like saving images, have been completed.
# Now the outputs will contain the same thing that the Web UI expresses: a file path for each output.
# Let's find the node ID of the first SaveImage node. This will work when you change your workflow JSON from
# the example above.
save_image_node_id = next(key for key in prompt if prompt[key].class_type == "SaveImage")
# Now let's print the absolute path to the image.
print(outputs[save_image_node_id]["images"][0]["abs_path"])
# At this point, all the models have been unloaded from VRAM, and everything has been cleaned up.
# Now let's make this script runnable:
import asyncio
if __name__ == "__main__":
# Since our main function is async, it must be run as async too.
asyncio.run(main())
docs/examples/script_examples/remote_api_example.py
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# This sample shows how to execute a ComfyUI workflow against a remote ComfyUI server or the server running on your
# local machine. It will return the bytes of the image in the workflow.
#
# This script does not need to run within a ComfyUI directory. Instead, this can be used inside your own
# Python application or located elsewhere. It should **not** be in the Git repository directory.
#
# First, you will need to install ComfyUI. You do not need the ComfyUI repository or all of the ComfyUI dependencies to
# run a script against a server on your machine or elsewhere. You can install the convenient client and types with:
# pip install --no-deps git+https://github.com/hiddenswitch/ComfyUI.git
# pip install aiohttp[speedups]
#
# Now you should develop your workflow. Start ComfyUI as normal; navigate to "Settings" in the menu, and check "Enable
# Dev mode Options". Then click "Save (API Format)". Copy and paste the contents of this file here:
_PROMPT_FROM_WEB_UI = {
"3": {
"class_type": "KSampler",
"inputs": {
"cfg": 8,
"denoise": 1,
"latent_image": [
"5",
0
],
"model": [
"4",
0
],
"negative": [
"7",
0
],
"positive": [
"6",
0
],
"sampler_name": "euler",
"scheduler": "normal",
"seed": 8566257,
"steps": 20
}
},
"4": {
"class_type": "CheckpointLoaderSimple",
"inputs": {
"ckpt_name": "v1-5-pruned-emaonly.ckpt"
}
},
"5": {
"class_type": "EmptyLatentImage",
"inputs": {
"batch_size": 1,
"height": 512,
"width": 512
}
},
"6": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"4",
1
],
"text": "masterpiece best quality girl"
}
},
"7": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"4",
1
],
"text": "bad hands"
}
},
"8": {
"class_type": "VAEDecode",
"inputs": {
"samples": [
"3",
0
],
"vae": [
"4",
2
]
}
},
"9": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"8",
0
]
}
}
}
# Observe this is an ordinary dictionary. The JSON that was saved from the workflow is compatible with Python syntax.
#
# Because you will be accessing a remote ComfyUI server, it should remain running somewhere. It can be your local machine,
# or some other machine. Do what makes sense for your application.
#
# We'll now write the entrypoint of our script. This is an `async def main()` because async helps us start and stop the
# code object that will run your workflow, just like pressing the Queue Prompt button.
async def main():
import copy
# Let's make some changes to the prompt. First we'll change the input text:
prompt_dict = copy.deepcopy(_PROMPT_FROM_WEB_UI)
prompt_dict["6"]["inputs"]["text"] = "masterpiece best quality man"
# Let's set the seed for our KSampler node:
prompt_dict["3"]["inputs"]["seed"] = 5
# Now we will validate the prompt. This Prompt class contains everything we need to validate the prompt.
from comfy.api.components.schema.prompt import Prompt
prompt = Prompt.validate(prompt_dict)
# Your prompt is ready to be processed. You should start your ComfyUI server; or, specify a remote URL for it.
# If you want to run your own ComfyUI server, follow the **Manual Install (Windows, Linux, macOS)** instructions.
# Then, as the README specifies, `cd` into the directory that contains your `models/` folder and run:
# comfyui --listen
# Let's create the client we will use to access it:
from comfy.client.aio_client import AsyncRemoteComfyClient
client = AsyncRemoteComfyClient(server_address="http://localhost:8188")
# Now let's get the bytes of the PNG image saved by the SaveImage node:
png_image_bytes = await client.queue_prompt(prompt)
# You can save these bytes wherever you need!
with open("image.png", "rb") as f:
f.write(png_image_bytes)
# Now let's make this script runnable:
import asyncio
if __name__ == "__main__":
# Since our main function is async, it must be run as async too.
asyncio.run(main())