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Author SHA1 Message Date
bennykok 12ddad3cfb op load model load 2024-09-05 14:51:41 -07:00
43 changed files with 723 additions and 6544 deletions
+2 -6
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@@ -7,19 +7,15 @@ on:
paths:
- "pyproject.toml"
permissions:
issues: write
jobs:
publish-node:
name: Publish Custom Node to registry
runs-on: ubuntu-latest
if: ${{ github.repository_owner == 'BennyKok' }}
steps:
- name: Check out code
uses: actions/checkout@v4
- name: Publish Custom Node
uses: Comfy-Org/publish-node-action@v1
uses: Comfy-Org/publish-node-action@main
with:
## Add your own personal access token to your Github Repository secrets and reference it here.
personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
+1 -2
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@@ -1,3 +1,2 @@
__pycache__
.DS_Store
file-hash-cache.json
.DS_Store
+4 -3
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@@ -2,9 +2,6 @@
Open source comfyui deployment platform, a `vercel` for generative workflow infra. (serverless hosted gpu with vertical intergation with comfyui)
> [!NOTE]
> Im looking for creative hacker to join ComfyDeploy's core team! DM me on [twitter](https://x.com/BennyKokMusic)
Join [Discord](https://discord.gg/EEYcQmdYZw) to chat more or visit [Comfy Deploy](https://comfydeploy.com/) to get started!
Check out our latest [nextjs starter kit](https://github.com/BennyKok/comfyui-deploy-next-example) with Comfy Deploy
@@ -96,6 +93,10 @@ Major areas
# Self Hosting with Vercel
[![Video](https://img.mytsi.org/i/nFOG479.png)](https://www.youtube.com/watch?v=hWvsEY1cS2M)
Tutorial Created by [Ross](https://github.com/rossman22590) and [Syn](https://github.com/mortlsyn)
Build command
```
-58
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@@ -1,58 +0,0 @@
import os
import io
import torchaudio
from folder_paths import get_annotated_filepath
class ComfyUIDeployExternalAudio:
RETURN_TYPES = ("AUDIO",)
RETURN_NAMES = ("audio",)
FUNCTION = "load_audio"
CATEGORY = "🔗ComfyDeploy"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"input_id": (
"STRING",
{"multiline": False, "default": "input_audio"},
),
"audio_file": ("STRING", {"default": ""}),
},
"optional": {
"default_value": ("AUDIO",),
"display_name": (
"STRING",
{"multiline": False, "default": ""},
),
"description": (
"STRING",
{"multiline": False, "default": ""},
),
}
}
@classmethod
def VALIDATE_INPUTS(s, audio_file, **kwargs):
return True
def load_audio(self, input_id, audio_file, default_value=None, display_name=None, description=None):
if audio_file and audio_file != "":
if audio_file.startswith(('http://', 'https://')):
# Handle URL input
import requests
response = requests.get(audio_file)
audio_data = io.BytesIO(response.content)
waveform, sample_rate = torchaudio.load(audio_data)
else:
# Handle local file
audio_path = get_annotated_filepath(audio_file)
waveform, sample_rate = torchaudio.load(audio_path)
audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
return (audio,)
else:
return (default_value,)
NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalAudio": ComfyUIDeployExternalAudio}
NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalAudio": "External Audio (ComfyUI Deploy)"}
+1 -2
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@@ -23,9 +23,8 @@ class ComfyUIDeployExternalBoolean:
RETURN_TYPES = ("BOOLEAN",)
RETURN_NAMES = ("bool_value",)
FUNCTION = "run"
CATEGORY = "🔗ComfyDeploy"
def run(self, input_id, default_value=None, display_name=None, description=None):
print(f"Node '{input_id}' processing with switch set to {default_value}")
+1 -2
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@@ -36,11 +36,10 @@ class ComfyUIDeployExternalCheckpoint:
RETURN_TYPES = (WILDCARD,)
RETURN_NAMES = ("path",)
FUNCTION = "run"
CATEGORY = "🔗ComfyDeploy"
CATEGORY = "deploy"
def run(self, input_id, default_value=None, display_name=None, description=None):
import requests
-110
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@@ -1,110 +0,0 @@
import os
import io
import cv2 as cv
import numpy as np
import torch
import requests
from folder_paths import get_annotated_filepath
class ComfyUIDeployExternalEXR:
RETURN_TYPES = ("IMAGE", "MASK")
RETURN_NAMES = ("image", "mask")
FUNCTION = "load_exr"
CATEGORY = "🔗ComfyDeploy"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"input_id": (
"STRING",
{"multiline": False, "default": "input_exr"},
),
"exr_file": ("STRING", {"default": ""}),
"tonemap": (["linear", "sRGB", "Reinhard"], {"default": "sRGB"}),
},
"optional": {
"default_image": ("IMAGE",),
"default_mask": ("MASK",),
"display_name": (
"STRING",
{"multiline": False, "default": ""},
),
"description": (
"STRING",
{"multiline": False, "default": ""},
),
}
}
@classmethod
def VALIDATE_INPUTS(s, exr_file, **kwargs):
return True
def sRGBtoLinear(self, npArray):
less = npArray <= 0.0404482362771082
npArray[less] = npArray[less] / 12.92
npArray[~less] = np.power((npArray[~less] + 0.055) / 1.055, 2.4)
def linearToSRGB(self, npArray):
less = npArray <= 0.0031308
npArray[less] = npArray[less] * 12.92
npArray[~less] = np.power(npArray[~less], 1/2.4) * 1.055 - 0.055
def load_exr(self, input_id, exr_file, tonemap="sRGB",
default_image=None, default_mask=None,
display_name=None, description=None):
try:
if exr_file and exr_file != "":
if exr_file.startswith(('http://', 'https://')):
# Handle URL input
response = requests.get(exr_file)
# Write to temp buffer
buffer = io.BytesIO(response.content)
nparr = np.frombuffer(buffer.getvalue(), np.uint8)
image = cv.imdecode(nparr, cv.IMREAD_UNCHANGED).astype(np.float32)
else:
# Handle local file
exr_path = get_annotated_filepath(exr_file)
image = cv.imread(exr_path, cv.IMREAD_UNCHANGED).astype(np.float32)
if len(image.shape) == 2:
image = np.repeat(image[..., np.newaxis], 3, axis=2)
# Extract RGB and flip channels
rgb = np.flip(image[:,:,:3], 2).copy()
# Apply tonemapping
if tonemap == "sRGB":
self.linearToSRGB(rgb)
rgb = np.clip(rgb, 0, 1)
elif tonemap == "Reinhard":
rgb = np.clip(rgb, 0, None)
rgb = rgb / (rgb + 1)
self.linearToSRGB(rgb)
rgb = np.clip(rgb, 0, 1)
rgb = torch.unsqueeze(torch.from_numpy(rgb), 0)
# Handle alpha/mask
mask = torch.zeros((1, image.shape[0], image.shape[1]), dtype=torch.float32)
if image.shape[2] > 3:
mask[0] = torch.from_numpy(np.clip(image[:,:,3], 0, 1))
return (rgb, mask)
else:
# Return defaults if no file provided
return (default_image, default_mask)
except Exception as e:
print(f"Error loading EXR: {str(e)}")
# Return defaults on error
return (default_image, default_mask)
NODE_CLASS_MAPPINGS = {
"ComfyUIDeployExternalEXR": ComfyUIDeployExternalEXR
}
NODE_DISPLAY_NAME_MAPPINGS = {
"ComfyUIDeployExternalEXR": "External EXR (ComfyUI Deploy)"
}
-106
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@@ -1,106 +0,0 @@
from PIL import Image, ImageOps
import numpy as np
import torch
import folder_paths
class AnyType(str):
def __ne__(self, __value: object) -> bool:
return False
WILDCARD = AnyType("*")
class ComfyUIDeployExternalFaceModel:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"input_id": (
"STRING",
{"multiline": False, "default": "input_reactor_face_model"},
),
},
"optional": {
"default_face_model_name": (
"STRING",
{"multiline": False, "default": ""},
),
"face_model_save_name": ( # if `default_face_model_name` is a link to download a file, we will attempt to save it with this name
"STRING",
{"multiline": False, "default": ""},
),
"display_name": (
"STRING",
{"multiline": False, "default": ""},
),
"description": (
"STRING",
{"multiline": True, "default": ""},
),
"face_model_url": (
"STRING",
{"multiline": False, "default": ""},
),
},
}
RETURN_TYPES = (WILDCARD,)
RETURN_NAMES = ("path",)
FUNCTION = "run"
CATEGORY = "🔗ComfyDeploy"
def run(
self,
input_id,
default_face_model_name=None,
face_model_save_name=None,
display_name=None,
description=None,
face_model_url=None,
):
import requests
import os
import uuid
if face_model_url and face_model_url.startswith("http"):
if face_model_save_name:
existing_face_models = folder_paths.get_filename_list("reactor/faces")
# Check if face_model_save_name exists in the list
if face_model_save_name in existing_face_models:
print(f"using face model: {face_model_save_name}")
return (face_model_save_name,)
else:
face_model_save_name = str(uuid.uuid4()) + ".safetensors"
print(face_model_save_name)
print(folder_paths.folder_names_and_paths["reactor/faces"][0][0])
destination_path = os.path.join(
folder_paths.folder_names_and_paths["reactor/faces"][0][0],
face_model_save_name,
)
print(destination_path)
print(
"Downloading external face model - "
+ face_model_url
+ " to "
+ destination_path
)
response = requests.get(
face_model_url,
headers={"User-Agent": "Mozilla/5.0"},
allow_redirects=True,
)
with open(destination_path, "wb") as out_file:
out_file.write(response.content)
return (face_model_save_name,)
else:
print(f"using face model: {default_face_model_name}")
return (default_face_model_name,)
NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalFaceModel": ComfyUIDeployExternalFaceModel}
NODE_DISPLAY_NAME_MAPPINGS = {
"ComfyUIDeployExternalFaceModel": "External Face Model (ComfyUI Deploy)"
}
+30 -41
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@@ -21,55 +21,44 @@ class ComfyUIDeployExternalImage:
),
"description": (
"STRING",
{"multiline": False, "default": ""},
{"multiline": True, "default": ""},
),
"default_value_url": ("STRING", {"image_preview": True, "default": ""}),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "run"
CATEGORY = "🔗ComfyDeploy"
def run(self, input_id, default_value=None, display_name=None, description=None, default_value_url=None):
FUNCTION = "run"
CATEGORY = "image"
def run(self, input_id, default_value=None, display_name=None, description=None):
image = default_value
# Try both input_id and default_value_url
urls_to_try = [url for url in [input_id, default_value_url] if url]
print(default_value_url)
for url in urls_to_try:
try:
if url.startswith('http'):
import requests
from io import BytesIO
print(f"Fetching image from url: {url}")
response = requests.get(url)
image = Image.open(BytesIO(response.content))
break
elif url.startswith(('data:image/png;base64,', 'data:image/jpeg;base64,', 'data:image/jpg;base64,')):
import base64
from io import BytesIO
print("Decoding base64 image")
base64_image = url[url.find(",")+1:]
decoded_image = base64.b64decode(base64_image)
image = Image.open(BytesIO(decoded_image))
break
except:
continue
if image is not None:
try:
image = ImageOps.exif_transpose(image)
image = image.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
except:
pass
return [image]
try:
if input_id.startswith('http'):
import requests
from io import BytesIO
print("Fetching image from url: ", input_id)
response = requests.get(input_id)
image = Image.open(BytesIO(response.content))
elif input_id.startswith('data:image/png;base64,') or input_id.startswith('data:image/jpeg;base64,') or input_id.startswith('data:image/jpg;base64,'):
import base64
from io import BytesIO
print("Decoding base64 image")
base64_image = input_id[input_id.find(",")+1:]
decoded_image = base64.b64decode(base64_image)
image = Image.open(BytesIO(decoded_image))
else:
raise ValueError("Invalid image url provided.")
image = ImageOps.exif_transpose(image)
image = image.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
return [image]
except:
return [image]
NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalImage": ComfyUIDeployExternalImage}
+3 -1
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@@ -28,8 +28,10 @@ class ComfyUIDeployExternalImageAlpha:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "run"
CATEGORY = "🔗ComfyDeploy"
CATEGORY = "image"
def run(self, input_id, default_value=None, display_name=None, description=None):
image = default_value
+5 -23
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@@ -34,37 +34,19 @@ class ComfyUIDeployExternalImageBatch:
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "run"
CATEGORY = "🔗ComfyDeploy"
def process_image(self, image):
image = ImageOps.exif_transpose(image)
image = image.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image_tensor = torch.from_numpy(image)[None,]
return image_tensor
CATEGORY = "image"
def run(self, input_id, images=None, default_value=None, display_name=None, description=None):
import requests
import zipfile
import io
processed_images = []
try:
images_list = json.loads(images) # Assuming images is a JSON array string
print(images_list)
for img_input in images_list:
if img_input.startswith('http') and img_input.endswith('.zip'):
print("Fetching zip file from url: ", img_input)
response = requests.get(img_input)
zip_file = zipfile.ZipFile(io.BytesIO(response.content))
for file_name in zip_file.namelist():
if file_name.lower().endswith(('.png', '.jpg', '.jpeg')):
with zip_file.open(file_name) as file:
image = Image.open(file)
image = self.process_image(image)
processed_images.append(image)
elif img_input.startswith('http'):
if img_input.startswith('http'):
import requests
from io import BytesIO
print("Fetching image from url: ", img_input)
response = requests.get(img_input)
+27 -35
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@@ -46,8 +46,10 @@ class ComfyUIDeployExternalLora:
RETURN_TYPES = (WILDCARD,)
RETURN_NAMES = ("path",)
FUNCTION = "run"
CATEGORY = "🔗ComfyDeploy"
CATEGORY = "deploy"
def run(
self,
@@ -62,42 +64,32 @@ class ComfyUIDeployExternalLora:
import os
import uuid
if lora_url:
if lora_url.startswith("http"):
if lora_save_name:
existing_loras = folder_paths.get_filename_list("loras")
# Check if lora_save_name exists in the list
if lora_save_name in existing_loras:
print(f"using lora: {lora_save_name}")
return (lora_save_name,)
else:
lora_save_name = str(uuid.uuid4()) + ".safetensors"
print(lora_save_name)
print(folder_paths.folder_names_and_paths["loras"][0][0])
destination_path = os.path.join(
folder_paths.folder_names_and_paths["loras"][0][0], lora_save_name
)
print(destination_path)
print(
"Downloading external lora - "
+ lora_url
+ " to "
+ destination_path
)
response = requests.get(
lora_url,
headers={"User-Agent": "Mozilla/5.0"},
allow_redirects=True,
)
with open(destination_path, "wb") as out_file:
out_file.write(response.content)
print(f"Ext Lora loading: {lora_url} to {lora_save_name}")
return (lora_save_name,)
if lora_url and lora_url.startswith("http"):
if lora_save_name:
existing_loras = folder_paths.get_filename_list("loras")
# Check if lora_save_name exists in the list
if lora_save_name in existing_loras:
print(f"using lora: {lora_save_name}")
return (lora_save_name,)
else:
print(f"Ext Lora loading: {lora_url}")
return (lora_url,)
lora_save_name = str(uuid.uuid4()) + ".safetensors"
print(lora_save_name)
print(folder_paths.folder_names_and_paths["loras"][0][0])
destination_path = os.path.join(
folder_paths.folder_names_and_paths["loras"][0][0], lora_save_name
)
print(destination_path)
print("Downloading external lora - " + lora_url + " to " + destination_path)
response = requests.get(
lora_url,
headers={"User-Agent": "Mozilla/5.0"},
allow_redirects=True,
)
with open(destination_path, "wb") as out_file:
out_file.write(response.content)
return (lora_save_name,)
else:
print(f"Ext Lora loading: {default_lora_name}")
print(f"using lora: {default_lora_name}")
return (default_lora_name,)
+3 -1
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@@ -31,8 +31,10 @@ class ComfyUIDeployExternalNumber:
RETURN_TYPES = ("FLOAT",)
RETURN_NAMES = ("value",)
FUNCTION = "run"
CATEGORY = "🔗ComfyDeploy"
CATEGORY = "number"
def run(self, input_id, default_value=None, display_name=None, description=None):
try:
+3 -1
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@@ -31,8 +31,10 @@ class ComfyUIDeployExternalNumberInt:
RETURN_TYPES = ("INT",)
RETURN_NAMES = ("value",)
FUNCTION = "run"
CATEGORY = "🔗ComfyDeploy"
CATEGORY = "number"
def run(self, input_id, default_value=None, display_name=None, description=None):
if not input_id or (isinstance(input_id, str) and not input_id.strip().isdigit()):
+3 -1
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@@ -34,8 +34,10 @@ class ComfyUIDeployExternalNumberSlider:
RETURN_TYPES = ("FLOAT",)
RETURN_NAMES = ("value",)
FUNCTION = "run"
CATEGORY = "🔗ComfyDeploy"
CATEGORY = "number"
def run(self, input_id, default_value=None, min_value=0, max_value=1, display_name=None, description=None):
try:
-53
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@@ -1,53 +0,0 @@
import re
class StringFunction:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"action": (["append", "replace"], {}),
"tidy_tags": (["yes", "no"], {}),
},
"optional": {
"text_a": ("STRING", {"multiline": True, "dynamicPrompts": False}),
"text_b": ("STRING", {"multiline": True, "dynamicPrompts": False}),
"text_c": ("STRING", {"multiline": True, "dynamicPrompts": False}),
},
}
RETURN_TYPES = ("STRING",)
FUNCTION = "exec"
CATEGORY = "🔗ComfyDeploy"
OUTPUT_NODE = True
def exec(self, action, tidy_tags, text_a="", text_b="", text_c=""):
tidy_tags = tidy_tags == "yes"
out = ""
if action == "append":
out = (", " if tidy_tags else "").join(
filter(None, [text_a, text_b, text_c])
)
else:
if text_c is None:
text_c = ""
if text_b.startswith("/") and text_b.endswith("/"):
regex = text_b[1:-1]
out = re.sub(regex, text_c, text_a)
else:
out = text_a.replace(text_b, text_c)
if tidy_tags:
out = re.sub(r"\s{2,}", " ", out)
out = out.replace(" ,", ",")
out = re.sub(r",{2,}", ",", out)
out = out.strip()
return {"ui": {"text": (out,)}, "result": (out,)}
NODE_CLASS_MAPPINGS = {
"ComfyUIDeployStringCombine": StringFunction,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"ComfyUIDeployStringCombine": "String Combine (ComfyUI Deploy)",
}
+1 -1
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@@ -34,7 +34,7 @@ class ComfyUIDeployExternalText:
FUNCTION = "run"
CATEGORY = "🔗ComfyDeploy"
CATEGORY = "text"
def run(self, input_id, default_value=None, display_name=None, description=None):
return [default_value]
-46
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@@ -1,46 +0,0 @@
class AnyType(str):
def __ne__(self, __value: object) -> bool:
return False
WILDCARD = AnyType("*")
class ComfyUIDeployExternalTextAny:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"input_id": (
"STRING",
{"multiline": False, "default": "input_text"},
),
},
"optional": {
"default_value": (
"STRING",
{"multiline": True, "default": ""},
),
"display_name": (
"STRING",
{"multiline": False, "default": ""},
),
"description": (
"STRING",
{"multiline": True, "default": ""},
),
}
}
RETURN_TYPES = (WILDCARD,)
RETURN_NAMES = ("text",)
FUNCTION = "run"
CATEGORY = "🔗ComfyDeploy"
def run(self, input_id, default_value=None, display_name=None, description=None):
return [default_value]
NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalTextAny": ComfyUIDeployExternalTextAny}
NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalTextAny": "External Text Any (ComfyUI Deploy)"}
+52
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@@ -0,0 +1,52 @@
import folder_paths
from PIL import Image, ImageOps
import numpy as np
import torch
import json
class ComfyUIDeployExternalTextList:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"input_id": (
"STRING",
{"multiline": False, "default": 'input_text_list'},
),
"text": (
"STRING",
{"multiline": True, "default": "[]"},
),
},
"optional": {
"display_name": (
"STRING",
{"multiline": False, "default": ""},
),
"description": (
"STRING",
{"multiline": True, "default": ""},
),
}
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("text",)
OUTPUT_IS_LIST = (True,)
FUNCTION = "run"
CATEGORY = "text"
def run(self, input_id, text=None, display_name=None, description=None):
text_list = []
try:
text_list = json.loads(text) # Assuming text is a JSON array string
except Exception as e:
print(f"Error processing images: {e}")
pass
return ([text_list],)
NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalTextList": ComfyUIDeployExternalTextList}
NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalTextList": "External Text List (ComfyUI Deploy)"}
-1
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@@ -36,7 +36,6 @@ class ComfyUIDeployExternalVideo:
RETURN_NAMES = ("video")
FUNCTION = "load_video"
CATEGORY = "🔗ComfyDeploy"
def load_video(self, input_id, default_value):
input_dir = folder_paths.get_input_directory()
-1
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@@ -791,7 +791,6 @@ class ComfyUIDeployExternalVideo:
)
FUNCTION = "load_video"
CATEGORY = "🔗ComfyDeploy"
def load_video(self, **kwargs):
input_id = kwargs.get("input_id")
-1
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@@ -33,7 +33,6 @@ class ComfyDeployWebscoketImageInput:
RETURN_NAMES = ("images",)
FUNCTION = "run"
CATEGORY = "🔗ComfyDeploy"
@classmethod
def VALIDATE_INPUTS(s, input_id):
-60
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@@ -1,60 +0,0 @@
import folder_paths
class AnyType(str):
def __ne__(self, __value: object) -> bool:
return False
from os import walk
WILDCARD = AnyType("*")
MODEL_EXTENSIONS = {
"safetensors": "SafeTensors file format",
"ckpt": "Checkpoint file",
"pth": "PyTorch serialized file",
"pkl": "Pickle file",
"onnx": "ONNX file",
}
def fetch_files(path):
for (dirpath, dirnames, filenames) in walk(path):
fs = []
if len(dirnames) > 0:
for dirname in dirnames:
fs.extend(fetch_files(f"{dirpath}/{dirname}"))
for filename in filenames:
# Remove "./models/" from the beginning of dirpath
relative_dirpath = dirpath.replace("./models/", "", 1)
file_path = f"{relative_dirpath}/{filename}"
# Only add files that are known model extensions
file_extension = filename.split('.')[-1].lower()
if file_extension in MODEL_EXTENSIONS:
fs.append(file_path)
return fs
allModels = fetch_files("./models")
class ComfyUIDeployModalList:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": (allModels, ),
}
}
RETURN_TYPES = (WILDCARD,)
RETURN_NAMES = ("model",)
FUNCTION = "run"
CATEGORY = "🔗ComfyDeploy"
def run(self, model=""):
# Split the model path by '/' and select the last item
model_name = model.split('/')[-1]
return [model_name]
NODE_CLASS_MAPPINGS = {"ComfyUIDeployModelList": ComfyUIDeployModalList}
NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployModelList": "Model List (ComfyUI Deploy)"}
+39
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@@ -0,0 +1,39 @@
import folder_paths
from PIL import Image, ImageOps
import numpy as np
import torch
import folder_paths
from tqdm import tqdm
class AnyType(str):
def __ne__(self, __value: object) -> bool:
return False
WILDCARD = AnyType("*")
class OuterPortLoadModel:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"tooltip": "The name of the checkpoint (model) to load."}),
}
}
RETURN_TYPES = ("MODEL", "CLIP", "VAE")
OUTPUT_TOOLTIPS = ("The model used for denoising latents.",
"The CLIP model used for encoding text prompts.",
"The VAE model used for encoding and decoding images to and from latent space.")
FUNCTION = "load_checkpoint"
CATEGORY = "loaders"
DESCRIPTION = "Loads a diffusion model checkpoint, diffusion models are used to denoise latents."
def load_checkpoint(self, ckpt_name):
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
return out[:3]
NODE_CLASS_MAPPINGS = {"OuterPortLoadModel": OuterPortLoadModel}
NODE_DISPLAY_NAME_MAPPINGS = {"OuterPortLoadModel": "Outer Port Load Model"}
-101
View File
@@ -1,101 +0,0 @@
import os
import json
import numpy as np
from PIL import Image
from PIL.PngImagePlugin import PngInfo
import folder_paths
class ComfyDeployOutputImage:
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
self.type = "output"
self.prefix_append = ""
self.compress_level = 4
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE", {"tooltip": "The images to save."}),
"filename_prefix": (
"STRING",
{
"default": "ComfyUI",
"tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.",
},
),
"file_type": (["png", "jpg", "webp"], {"default": "webp"}),
"quality": ("INT", {"default": 80, "min": 1, "max": 100, "step": 1}),
},
"optional": {
"output_id": (
"STRING",
{"multiline": False, "default": "output_images"},
),
},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
RETURN_TYPES = ()
FUNCTION = "run"
OUTPUT_NODE = True
CATEGORY = "🔗ComfyDeploy"
DESCRIPTION = "Saves the input images to your ComfyUI output directory."
def run(
self,
images,
filename_prefix="ComfyUI",
file_type="png",
quality=80,
output_id="output_images",
prompt=None,
extra_pnginfo=None,
):
filename_prefix += self.prefix_append
full_output_folder, filename, counter, subfolder, filename_prefix = (
folder_paths.get_save_image_path(
filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]
)
)
results = list()
for batch_number, image in enumerate(images):
i = 255.0 * image.cpu().numpy()
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
metadata = PngInfo()
if prompt is not None:
metadata.add_text("prompt", json.dumps(prompt))
if extra_pnginfo is not None:
for x in extra_pnginfo:
metadata.add_text(x, json.dumps(extra_pnginfo[x]))
filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
file = f"{filename_with_batch_num}_{counter:05}_.{file_type}"
file_path = os.path.join(full_output_folder, file)
if file_type == "png":
img.save(
file_path, pnginfo=metadata, compress_level=self.compress_level
)
elif file_type == "jpg":
img.save(file_path, quality=quality, optimize=True)
elif file_type == "webp":
img.save(file_path, quality=quality)
results.append(
{
"filename": file,
"subfolder": subfolder,
"type": self.type,
"output_id": output_id,
}
)
counter += 1
return {"ui": {"images": results}}
NODE_CLASS_MAPPINGS = {"ComfyDeployOutputImage": ComfyDeployOutputImage}
NODE_DISPLAY_NAME_MAPPINGS = {"ComfyDeployOutputImage": "Image Output (ComfyDeploy)"}
+3 -1
View File
@@ -33,8 +33,10 @@ class ComfyDeployWebscoketImageOutput:
RETURN_TYPES = ()
RETURN_NAMES = ("text",)
FUNCTION = "run"
CATEGORY = "🔗ComfyDeploy"
CATEGORY = "output"
@classmethod
def VALIDATE_INPUTS(s, output_id):
+406 -1755
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File diff suppressed because it is too large Load Diff
-359
View File
@@ -1,359 +0,0 @@
{
"extra": {
"ds": {
"scale": 0.6010518407212623,
"offset": [815.5938895649746, 84.94304700477853]
},
"node_versions": {
"comfy-core": "0.3.19",
"comfyui-deploy": "b3df94d1affcf7ce05ee7eeda99989194bcd9159"
}
},
"links": [
[9, 8, 0, 9, 0, "IMAGE"],
[45, 30, 1, 6, 0, "CLIP"],
[46, 30, 2, 8, 1, "VAE"],
[47, 30, 0, 31, 0, "MODEL"],
[51, 27, 0, 31, 3, "LATENT"],
[52, 31, 0, 8, 0, "LATENT"],
[54, 30, 1, 33, 0, "CLIP"],
[55, 33, 0, 31, 2, "CONDITIONING"],
[56, 6, 0, 35, 0, "CONDITIONING"],
[57, 35, 0, 31, 1, "CONDITIONING"],
[58, 38, 0, 6, 1, "STRING"],
[59, 40, 0, 27, 0, "INT"],
[60, 39, 0, 27, 1, "INT"]
],
"nodes": [
{
"id": 6,
"pos": [384, 192],
"mode": 0,
"size": [422.8500061035156, 164.30999755859375],
"type": "CLIPTextEncode",
"color": "#232",
"flags": {},
"order": 7,
"title": "CLIP Text Encode (Positive Prompt)",
"inputs": [
{ "link": 45, "name": "clip", "type": "CLIP" },
{
"pos": [10, 36],
"link": 58,
"name": "text",
"type": "STRING",
"widget": { "name": "text" }
}
],
"bgcolor": "#353",
"outputs": [
{
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [56],
"slot_index": 0
}
],
"properties": { "Node name for S&R": "CLIPTextEncode" },
"widgets_values": [
"cute anime girl with massive fluffy fennec ears and a big fluffy tail blonde messy long hair blue eyes wearing a maid outfit with a long black gold leaf pattern dress and a white apron mouth open placing a fancy black forest cake with candles on top of a dinner table of an old dark Victorian mansion lit by candlelight with a bright window to the foggy forest and very expensive stuff everywhere there are paintings on the walls"
]
},
{
"id": 8,
"pos": [1151, 195],
"mode": 0,
"size": [210, 46],
"type": "VAEDecode",
"flags": {},
"order": 11,
"inputs": [
{ "link": 52, "name": "samples", "type": "LATENT" },
{ "link": 46, "name": "vae", "type": "VAE" }
],
"outputs": [
{ "name": "IMAGE", "type": "IMAGE", "links": [9], "slot_index": 0 }
],
"properties": { "Node name for S&R": "VAEDecode" },
"widgets_values": []
},
{
"id": 9,
"pos": [1375, 194],
"mode": 0,
"size": [985.2999877929688, 1060.3800048828125],
"type": "SaveImage",
"flags": {},
"order": 12,
"inputs": [{ "link": 9, "name": "images", "type": "IMAGE" }],
"outputs": [],
"properties": {},
"widgets_values": ["ComfyUI"]
},
{
"id": 31,
"pos": [816, 192],
"mode": 0,
"size": [315, 262],
"type": "KSampler",
"flags": {},
"order": 10,
"inputs": [
{ "link": 47, "name": "model", "type": "MODEL" },
{ "link": 57, "name": "positive", "type": "CONDITIONING" },
{ "link": 55, "name": "negative", "type": "CONDITIONING" },
{ "link": 51, "name": "latent_image", "type": "LATENT" }
],
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"links": [52],
"shape": 3,
"slot_index": 0
}
],
"properties": { "Node name for S&R": "KSampler" },
"widgets_values": [
1024035737089801,
"randomize",
20,
1,
"euler",
"simple",
1
]
},
{
"id": 35,
"pos": [576, 96],
"mode": 0,
"size": [211.60000610351562, 58],
"type": "FluxGuidance",
"flags": {},
"order": 9,
"inputs": [
{ "link": 56, "name": "conditioning", "type": "CONDITIONING" }
],
"outputs": [
{
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [57],
"shape": 3,
"slot_index": 0
}
],
"properties": { "Node name for S&R": "FluxGuidance" },
"widgets_values": [3.5]
},
{
"id": 37,
"pos": [60, 345],
"mode": 0,
"size": [225, 88],
"type": "MarkdownNote",
"color": "#432",
"flags": {},
"order": 0,
"inputs": [],
"bgcolor": "#653",
"outputs": [],
"properties": {},
"widgets_values": [
"🛈 [Learn more about this workflow](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-dev-1)"
]
},
{
"id": 34,
"pos": [825, 510],
"mode": 0,
"size": [282.8599853515625, 164.0800018310547],
"type": "Note",
"color": "#432",
"flags": {},
"order": 1,
"inputs": [],
"bgcolor": "#653",
"outputs": [],
"properties": { "text": "" },
"widgets_values": [
"Note that Flux dev and schnell do not have any negative prompt so CFG should be set to 1.0. Setting CFG to 1.0 means the negative prompt is ignored."
]
},
{
"id": 30,
"pos": [48, 192],
"mode": 0,
"size": [315, 98],
"type": "CheckpointLoaderSimple",
"flags": {},
"order": 2,
"inputs": [],
"outputs": [
{
"name": "MODEL",
"type": "MODEL",
"links": [47],
"shape": 3,
"slot_index": 0
},
{
"name": "CLIP",
"type": "CLIP",
"links": [45, 54],
"shape": 3,
"slot_index": 1
},
{
"name": "VAE",
"type": "VAE",
"links": [46],
"shape": 3,
"slot_index": 2
}
],
"properties": { "Node name for S&R": "CheckpointLoaderSimple" },
"widgets_values": ["FLUX1/flux1-dev-fp8.safetensors"]
},
{
"id": 33,
"pos": [390, 400],
"mode": 0,
"size": [422.8500061035156, 164.30999755859375],
"type": "CLIPTextEncode",
"color": "#322",
"flags": { "collapsed": true },
"order": 6,
"title": "CLIP Text Encode (Negative Prompt)",
"inputs": [
{ "link": 54, "name": "clip", "type": "CLIP", "slot_index": 0 }
],
"bgcolor": "#533",
"outputs": [
{
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [55],
"slot_index": 0
}
],
"properties": { "Node name for S&R": "CLIPTextEncode" },
"widgets_values": [""]
},
{
"id": 27,
"pos": [461.8629455566406, 460.2491149902344],
"mode": 0,
"size": [315, 126],
"type": "EmptySD3LatentImage",
"color": "#323",
"flags": {},
"order": 8,
"inputs": [
{
"pos": [10, 60],
"link": 59,
"name": "height",
"type": "INT",
"widget": { "name": "height" }
},
{
"pos": [10, 36],
"link": 60,
"name": "width",
"type": "INT",
"widget": { "name": "width" }
}
],
"bgcolor": "#535",
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"links": [51],
"shape": 3,
"slot_index": 0
}
],
"properties": { "Node name for S&R": "EmptySD3LatentImage" },
"widgets_values": [1024, 1024, 1]
},
{
"id": 38,
"pos": [-497.3238525390625, 306.5517578125],
"mode": 0,
"size": [400, 200],
"type": "ComfyUIDeployExternalText",
"flags": {},
"order": 3,
"inputs": [],
"outputs": [{ "name": "text", "type": "STRING", "links": [58] }],
"properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
"widgets_values": [
"prompt",
"cute anime girl with massive fluffy fennec ears and a big fluffy tail blonde messy long hair blue eyes wearing a maid outfit with a long black gold leaf pattern dress and a white apron mouth open placing a fancy black forest cake with candles on top of a dinner table of an old dark Victorian mansion lit by candlelight with a bright window to the foggy forest and very expensive stuff everywhere there are paintings on the walls",
"Prompt",
"The prompt to generate an image from."
]
},
{
"id": 39,
"pos": [-507.4543762207031, 612.9552001953125],
"mode": 0,
"size": [453.5999755859375, 200],
"type": "ComfyUIDeployExternalNumberInt",
"flags": {},
"order": 4,
"inputs": [],
"outputs": [
{ "name": "value", "type": "INT", "links": [60], "slot_index": 0 }
],
"properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
"widgets_values": ["width", 1024, "Width", "The width of the image."]
},
{
"id": 40,
"pos": [-497.8695068359375, 864.68994140625],
"mode": 0,
"size": [453.5999755859375, 200],
"type": "ComfyUIDeployExternalNumberInt",
"flags": {},
"order": 5,
"inputs": [],
"outputs": [
{ "name": "value", "type": "INT", "links": [59], "slot_index": 0 }
],
"properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
"widgets_values": ["height", 1024, "Height", "The height of the image."]
}
],
"config": {},
"groups": [
{
"id": 1,
"color": "#3f789e",
"flags": {},
"title": "Input",
"bounding": [
-508.010986328125, 186.76593017578125, 453.23040771484375,
334.3218994140625
],
"font_size": 24
},
{
"id": 2,
"color": "#A88",
"flags": {},
"title": "Additional",
"bounding": [
-517.1227416992188, 537.53759765625, 644.4954223632812,
599.2609252929688
],
"font_size": 24
}
],
"version": 0.4,
"last_link_id": 60,
"last_node_id": 40
}
-423
View File
@@ -1,423 +0,0 @@
{
"extra": {
"ds": {
"scale": 0.6830134553650709,
"offset": [750.2474149300585, 120.48924647647146]
},
"node_versions": {
"comfy-core": "0.3.19",
"comfyui-deploy": "171a227856bd5f31e97828d89f83f3741004d05e"
}
},
"links": [
[9, 8, 0, 9, 0, "IMAGE"],
[10, 11, 0, 6, 0, "CLIP"],
[12, 10, 0, 8, 1, "VAE"],
[19, 16, 0, 13, 2, "SAMPLER"],
[20, 17, 0, 13, 3, "SIGMAS"],
[23, 5, 0, 13, 4, "LATENT"],
[24, 13, 0, 8, 0, "LATENT"],
[30, 22, 0, 13, 1, "GUIDER"],
[37, 25, 0, 13, 0, "NOISE"],
[38, 12, 0, 17, 0, "MODEL"],
[39, 12, 0, 22, 0, "MODEL"],
[40, 6, 0, 22, 1, "CONDITIONING"],
[41, 28, 0, 6, 1, "STRING"],
[44, 29, 0, 5, 0, "INT"],
[45, 30, 0, 5, 1, "INT"]
],
"nodes": [
{
"id": 13,
"pos": [842, 215],
"mode": 0,
"size": [355.20001220703125, 106],
"type": "SamplerCustomAdvanced",
"flags": {},
"order": 14,
"inputs": [
{ "link": 37, "name": "noise", "type": "NOISE", "slot_index": 0 },
{ "link": 30, "name": "guider", "type": "GUIDER", "slot_index": 1 },
{ "link": 19, "name": "sampler", "type": "SAMPLER", "slot_index": 2 },
{ "link": 20, "name": "sigmas", "type": "SIGMAS", "slot_index": 3 },
{
"link": 23,
"name": "latent_image",
"type": "LATENT",
"slot_index": 4
}
],
"outputs": [
{
"name": "output",
"type": "LATENT",
"links": [24],
"shape": 3,
"slot_index": 0
},
{
"name": "denoised_output",
"type": "LATENT",
"links": null,
"shape": 3
}
],
"properties": { "Node name for S&R": "SamplerCustomAdvanced" },
"widgets_values": []
},
{
"id": 8,
"pos": [1248, 192],
"mode": 0,
"size": [210, 46],
"type": "VAEDecode",
"flags": {},
"order": 15,
"inputs": [
{ "link": 24, "name": "samples", "type": "LATENT" },
{ "link": 12, "name": "vae", "type": "VAE" }
],
"outputs": [
{ "name": "IMAGE", "type": "IMAGE", "links": [9], "slot_index": 0 }
],
"properties": { "Node name for S&R": "VAEDecode" },
"widgets_values": []
},
{
"id": 22,
"pos": [559, 125],
"mode": 0,
"size": [241.79998779296875, 46],
"type": "BasicGuider",
"flags": {},
"order": 13,
"inputs": [
{ "link": 39, "name": "model", "type": "MODEL", "slot_index": 0 },
{
"link": 40,
"name": "conditioning",
"type": "CONDITIONING",
"slot_index": 1
}
],
"outputs": [
{
"name": "GUIDER",
"type": "GUIDER",
"links": [30],
"shape": 3,
"slot_index": 0
}
],
"properties": { "Node name for S&R": "BasicGuider" },
"widgets_values": []
},
{
"id": 16,
"pos": [480, 720],
"mode": 0,
"size": [315, 58],
"type": "KSamplerSelect",
"flags": {},
"order": 0,
"inputs": [],
"outputs": [
{ "name": "SAMPLER", "type": "SAMPLER", "links": [19], "shape": 3 }
],
"properties": { "Node name for S&R": "KSamplerSelect" },
"widgets_values": ["euler"]
},
{
"id": 17,
"pos": [480, 816],
"mode": 0,
"size": [315, 106],
"type": "BasicScheduler",
"flags": {},
"order": 10,
"inputs": [
{ "link": 38, "name": "model", "type": "MODEL", "slot_index": 0 }
],
"outputs": [
{ "name": "SIGMAS", "type": "SIGMAS", "links": [20], "shape": 3 }
],
"properties": { "Node name for S&R": "BasicScheduler" },
"widgets_values": ["simple", 4, 1]
},
{
"id": 27,
"pos": [480, 960],
"mode": 0,
"size": [311.3529052734375, 131.16229248046875],
"type": "Note",
"color": "#432",
"flags": {},
"order": 1,
"inputs": [],
"bgcolor": "#653",
"outputs": [],
"properties": { "text": "" },
"widgets_values": [
"The schnell model is a distilled model that can generate a good image with only 4 steps."
]
},
{
"id": 9,
"pos": [1488, 192],
"mode": 0,
"size": [985.3012084960938, 1060.3828125],
"type": "SaveImage",
"flags": {},
"order": 16,
"inputs": [{ "link": 9, "name": "images", "type": "IMAGE" }],
"outputs": [],
"properties": {},
"widgets_values": ["ComfyUI"]
},
{
"id": 25,
"pos": [480, 587.7890625],
"mode": 0,
"size": [315, 82],
"type": "RandomNoise",
"color": "#2a363b",
"flags": {},
"order": 2,
"inputs": [],
"bgcolor": "#3f5159",
"outputs": [
{ "name": "NOISE", "type": "NOISE", "links": [37], "shape": 3 }
],
"properties": { "Node name for S&R": "RandomNoise" },
"widgets_values": [393845863554716, "randomize"]
},
{
"id": 5,
"pos": [471.7746276855469, 423.91571044921875],
"mode": 0,
"size": [315, 126],
"type": "EmptyLatentImage",
"color": "#323",
"flags": {},
"order": 12,
"inputs": [
{
"pos": [10, 36],
"link": 44,
"name": "width",
"type": "INT",
"widget": { "name": "width" }
},
{
"pos": [10, 60],
"link": 45,
"name": "height",
"type": "INT",
"widget": { "name": "height" }
}
],
"bgcolor": "#535",
"outputs": [
{ "name": "LATENT", "type": "LATENT", "links": [23], "slot_index": 0 }
],
"properties": { "Node name for S&R": "EmptyLatentImage" },
"widgets_values": [1024, 1024, 1]
},
{
"id": 26,
"pos": [41.60395812988281, 517.059326171875],
"mode": 0,
"size": [336, 288],
"type": "Note",
"color": "#432",
"flags": {},
"order": 3,
"inputs": [],
"bgcolor": "#653",
"outputs": [],
"properties": { "text": "" },
"widgets_values": [
"If you get an error in any of the nodes above make sure the files are in the correct directories.\n\nSee the top of the examples page for the links : https://comfyanonymous.github.io/ComfyUI_examples/flux/\n\nflux1-schnell.safetensors goes in: ComfyUI/models/unet/\n\nt5xxl_fp16.safetensors and clip_l.safetensors go in: ComfyUI/models/clip/\n\nae.safetensors goes in: ComfyUI/models/vae/\n\n\nTip: You can set the weight_dtype above to one of the fp8 types if you have memory issues."
]
},
{
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-409
View File
@@ -1,409 +0,0 @@
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"inputs": [],
"outputs": [
{ "name": "MODEL", "type": "MODEL", "links": [94], "slot_index": 0 }
],
"properties": { "Node name for S&R": "UNETLoader" },
"widgets_values": ["wan2.1_t2v_1.3B_fp16.safetensors", "default"]
},
{
"id": 6,
"pos": [415, 186],
"mode": 0,
"size": [422.84503173828125, 164.31304931640625],
"type": "CLIPTextEncode",
"color": "#232",
"flags": {},
"order": 8,
"title": "CLIP Text Encode (Positive Prompt)",
"inputs": [
{ "link": 74, "name": "clip", "type": "CLIP" },
{
"pos": [10, 36],
"link": 96,
"name": "text",
"type": "STRING",
"widget": { "name": "text" }
}
],
"bgcolor": "#353",
"outputs": [
{
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [46],
"slot_index": 0
}
],
"properties": { "Node name for S&R": "CLIPTextEncode" },
"widgets_values": [
"a fox moving quickly in a beautiful winter scenery nature trees mountains daytime tracking camera"
]
},
{
"id": 7,
"pos": [413, 389],
"mode": 0,
"size": [425.27801513671875, 180.6060791015625],
"type": "CLIPTextEncode",
"color": "#322",
"flags": {},
"order": 9,
"title": "CLIP Text Encode (Negative Prompt)",
"inputs": [
{ "link": 75, "name": "clip", "type": "CLIP" },
{
"pos": [10, 36],
"link": 97,
"name": "text",
"type": "STRING",
"widget": { "name": "text" }
}
],
"bgcolor": "#533",
"outputs": [
{
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [52],
"slot_index": 0
}
],
"properties": { "Node name for S&R": "CLIPTextEncode" },
"widgets_values": [
"色调艳丽,过曝,静态,细节模糊不清,字幕,风格,作品,画作,画面,静止,整体发灰,最差质量,低质量,JPEG压缩残留,丑陋的,残缺的,多余的手指,画得不好的手部,画得不好的脸部,畸形的,毁容的,形态畸形的肢体,手指融合,静止不动的画面,杂乱的背景,三条腿,背景人很多,倒着走"
]
},
{
"id": 38,
"pos": [-10.047812461853027, 187.37384033203125],
"mode": 0,
"size": [390, 98],
"type": "CLIPLoader",
"flags": {},
"order": 2,
"inputs": [],
"outputs": [
{ "name": "CLIP", "type": "CLIP", "links": [74, 75], "slot_index": 0 }
],
"properties": { "Node name for S&R": "CLIPLoader" },
"widgets_values": [
"umt5_xxl_fp8_e4m3fn_scaled.safetensors",
"wan",
"default"
]
},
{
"id": 49,
"pos": [-535.2967529296875, 342.3277587890625],
"mode": 0,
"size": [400, 200],
"type": "ComfyUIDeployExternalText",
"flags": {},
"order": 3,
"inputs": [],
"outputs": [{ "name": "text", "type": "STRING", "links": [96] }],
"properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
"widgets_values": [
"positive_prompt",
"a fox moving quickly in a beautiful winter scenery nature trees mountains daytime tracking camera",
"Prompt",
"The text prompt to guide video generation. "
]
},
{
"id": 50,
"pos": [-526.2716064453125, 703.8343505859375],
"mode": 0,
"size": [400, 200],
"type": "ComfyUIDeployExternalText",
"flags": {},
"order": 4,
"inputs": [],
"outputs": [{ "name": "text", "type": "STRING", "links": [97] }],
"properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
"widgets_values": [
"negative_prompt",
"色调艳丽,过曝,静态,细节模糊不清,字幕,风格,作品,画作,画面,静止,整体发灰,最差质量,低质量,JPEG压缩残留,丑陋的,残缺的,多余的手指,画得不好的手部,画得不好的脸部,畸形的,毁容的,形态畸形的肢体,手指融合,静止不动的画面,杂乱的背景,三条腿,背景人很多,倒着走",
"Negative Prompt",
"The negative prompt to use. Use it to address details that you don't want in the image. This could be colors, objects, scenery and even the small details (e.g. moustache, blurry, low resolution). "
]
},
{
"id": 40,
"pos": [516.926513671875, 619.59716796875],
"mode": 0,
"size": [315, 150],
"type": "EmptyHunyuanLatentVideo",
"flags": {},
"order": 10,
"inputs": [
{
"pos": [10, 36],
"link": 100,
"name": "width",
"type": "INT",
"widget": { "name": "width" }
},
{
"pos": [10, 60],
"link": 99,
"name": "height",
"type": "INT",
"widget": { "name": "height" }
}
],
"outputs": [
{ "name": "LATENT", "type": "LATENT", "links": [91], "slot_index": 0 }
],
"properties": { "Node name for S&R": "EmptyHunyuanLatentVideo" },
"widgets_values": [832, 480, 33, 1]
},
{
"id": 28,
"pos": [1460, 190],
"mode": 0,
"size": [870.8511352539062, 643.7430419921875],
"type": "SaveAnimatedWEBP",
"flags": {},
"order": 13,
"inputs": [{ "link": 56, "name": "images", "type": "IMAGE" }],
"outputs": [],
"properties": {},
"widgets_values": ["ComfyUI", 16, false, 90, "default"]
},
{
"id": 51,
"pos": [-522.7415161132812, 959.3386840820312],
"mode": 0,
"size": [453.5999755859375, 200],
"type": "ComfyUIDeployExternalNumberInt",
"flags": {},
"order": 5,
"inputs": [],
"outputs": [
{ "name": "value", "type": "INT", "links": [100], "slot_index": 0 }
],
"properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
"widgets_values": ["width", 832, "Width", "The Width of the Video. "]
},
{
"id": 52,
"pos": [-518.9917602539062, 1207.9444580078125],
"mode": 0,
"size": [453.5999755859375, 200],
"type": "ComfyUIDeployExternalNumberInt",
"flags": {},
"order": 6,
"inputs": [],
"outputs": [
{ "name": "value", "type": "INT", "links": [99], "slot_index": 0 }
],
"properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
"widgets_values": ["height", 480, "Height", "The Height of the Video. "]
}
],
"config": {},
"groups": [
{
"id": 1,
"color": "#3f789e",
"flags": {},
"title": "Inputs",
"bounding": [
-560.9110717773438, 255.1485595703125, 500.94989013671875,
333.4786682128906
],
"font_size": 24
},
{
"id": 2,
"color": "#b06634",
"flags": {},
"title": "Additional",
"bounding": [
-556.5305786132812, 619.87548828125, 761.2673950195312,
811.6837768554688
],
"font_size": 24
}
],
"version": 0.4,
"last_link_id": 100,
"last_node_id": 52
}
+17 -37
View File
@@ -6,12 +6,10 @@ from PIL import Image, ImageOps
from io import BytesIO
from pydantic import BaseModel as PydanticBaseModel
class BaseModel(PydanticBaseModel):
class Config:
arbitrary_types_allowed = True
class Status(Enum):
NOT_STARTED = "not-started"
RUNNING = "running"
@@ -19,7 +17,6 @@ class Status(Enum):
FAILED = "failed"
UPLOADING = "uploading"
class StreamingPrompt(BaseModel):
workflow_api: Any
auth_token: str
@@ -27,52 +24,42 @@ class StreamingPrompt(BaseModel):
running_prompt_ids: set[str] = set()
status_endpoint: Optional[str]
file_upload_endpoint: Optional[str]
workflow: Any
gpu_event_id: Optional[str] = None
class SimplePrompt(BaseModel):
status_endpoint: Optional[str]
file_upload_endpoint: Optional[str]
token: Optional[str]
workflow_api: dict
status: Status = Status.NOT_STARTED
progress: set = set()
last_updated_node: Optional[str] = None
last_updated_node: Optional[str] = None,
uploading_nodes: set = set()
done: bool = False
is_realtime: bool = False
start_time: Optional[float] = None
gpu_event_id: Optional[str] = None
is_realtime: bool = False,
start_time: Optional[float] = None,
sockets = dict()
prompt_metadata: dict[str, SimplePrompt] = {}
streaming_prompt_metadata: dict[str, StreamingPrompt] = {}
class BinaryEventTypes:
PREVIEW_IMAGE = 1
UNENCODED_PREVIEW_IMAGE = 2
max_output_id_length = 24
async def send_image(image_data, sid=None, output_id: str = None):
async def send_image(image_data, sid=None, output_id:str = None):
max_length = max_output_id_length
output_id = output_id[:max_length]
padded_output_id = output_id.ljust(max_length, "\x00")
encoded_output_id = padded_output_id.encode("ascii", "replace")
padded_output_id = output_id.ljust(max_length, '\x00')
encoded_output_id = padded_output_id.encode('ascii', 'replace')
image_type = image_data[0]
image = image_data[1]
max_size = image_data[2]
quality = image_data[3]
if max_size is not None:
if hasattr(Image, "Resampling"):
if hasattr(Image, 'Resampling'):
resampling = Image.Resampling.BILINEAR
else:
resampling = Image.ANTIALIAS
@@ -96,23 +83,17 @@ async def send_image(image_data, sid=None, output_id: str = None):
position_after = bytesIO.tell()
bytes_written = position_after - position_before
print(f"Bytes written: {bytes_written}")
image.save(bytesIO, format=image_type, quality=quality, compress_level=1)
preview_bytes = bytesIO.getvalue()
await send_bytes(BinaryEventTypes.PREVIEW_IMAGE, preview_bytes, sid=sid)
async def send_socket_catch_exception(function, message):
try:
await function(message)
except (
aiohttp.ClientError,
aiohttp.ClientPayloadError,
ConnectionResetError,
) as err:
except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError) as err:
print("send error:", err)
def encode_bytes(event, data):
if not isinstance(event, int):
raise RuntimeError(f"Binary event types must be integers, got {event}")
@@ -122,10 +103,9 @@ def encode_bytes(event, data):
message.extend(data)
return message
async def send_bytes(event, data, sid=None):
message = encode_bytes(event, data)
print("sending image to ", event, sid)
if sid is None:
@@ -133,4 +113,4 @@ async def send_bytes(event, data, sid=None):
for ws in _sockets:
await send_socket_catch_exception(ws.send_bytes, message)
elif sid in sockets:
await send_socket_catch_exception(sockets[sid].send_bytes, message)
await send_socket_catch_exception(sockets[sid].send_bytes, message)
+3 -3
View File
@@ -1,9 +1,9 @@
[project]
name = "comfyui-deploy"
description = "Open source comfyui deployment platform, a vercel for generative workflow infra."
version = "2.1.0"
license = { file = "LICENSE" }
dependencies = ["aiofiles", "pydantic", "opencv-python", "imageio-ffmpeg", "tabulate", "brotli"]
version = "1.0.0"
license = "LICENSE"
dependencies = ["aiofiles", "pydantic", "opencv-python", "imageio-ffmpeg"]
[project.urls]
Repository = "https://github.com/BennyKok/comfyui-deploy"
-1
View File
@@ -3,5 +3,4 @@ pydantic
opencv-python
imageio-ffmpeg
brotli
tabulate
# logfire
+4
View File
@@ -0,0 +1,4 @@
/** @typedef {import('../../../web/scripts/api.js').api} API*/
import { api as _api } from '../../scripts/api.js';
/** @type {API} */
export const api = _api;
+4
View File
@@ -0,0 +1,4 @@
/** @typedef {import('../../../web/scripts/app.js').ComfyApp} ComfyApp*/
import { app as _app } from '../../scripts/app.js';
/** @type {ComfyApp} */
export const app = _app;
+93 -914
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+18
View File
@@ -0,0 +1,18 @@
// /** @typedef {import('../../../web/scripts/api.js').api} API*/
// import { api as _api } from "../../scripts/api.js";
// /** @type {API} */
// export const api = _api;
/** @typedef {typeof import('../../../web/scripts/widgets.js').ComfyWidgets} Widgets*/
import { ComfyWidgets as _ComfyWidgets } from "../../scripts/widgets.js";
/**
* @type {Widgets}
*/
export const ComfyWidgets = _ComfyWidgets;
// import { LGraphNode as _LGraphNode } from "../../types/litegraph.js";
/** @typedef {typeof import('../../../web/types/litegraph.js').LGraphNode} LGraphNode*/
/** @type {LGraphNode}*/
export const LGraphNode = LiteGraph.LGraphNode;
-1
View File
@@ -6,5 +6,4 @@ export const customInputNodes: Record<string, string> = {
ComfyUIDeployExternalNumberInt: "integer",
ComfyUIDeployExternalLora: "string - (public lora download url)",
ComfyUIDeployExternalCheckpoint: "string - (public checkpoints download url)",
ComfyUIDeployExternalFaceModel: "string - (public face model download url)",
};