Initial commit
This commit is contained in:
Jashin chan
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|
||||
./ComfyUI_API_Request/.env
|
||||
Generated
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|
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|
||||
<orderEntry type="jdk" jdkName="Python 3.12 (ComfyUI_API_Request)" jdkType="Python SDK" />
|
||||
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Generated
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|
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|
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|
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Generated
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<module fileurl="file://$PROJECT_DIR$/.idea/ComfyUI_API_Request.iml" filepath="$PROJECT_DIR$/.idea/ComfyUI_API_Request.iml" />
|
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<component name="Git.Settings">
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<option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
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<created>1734772300891</created>
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<option name="number" value="Default" />
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<option name="presentableId" value="Default" />
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<updated>1734772300891</updated>
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<servers />
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</component>
|
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</project>
|
||||
@@ -0,0 +1,7 @@
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||||
from .nodes import NODE_CLASS_MAPPINGS,NODE_DISPLAY_NAME_MAPPINGS
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
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||||
|
||||
|
||||
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
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import os
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||||
import zipfile
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||||
import aiohttp
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||||
import asyncio
|
||||
|
||||
import requests
|
||||
import json
|
||||
import base64
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||||
import torch
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||||
import numpy as np
|
||||
from PIL import Image
|
||||
from io import BytesIO
|
||||
from typing import Optional, List, Union, Dict, Any
|
||||
|
||||
ucPreset_list = [
|
||||
"blurry, lowres, error, film grain, scan artifacts, worst quality, bad quality, jpeg artifacts, very displeasing, chromatic aberration, logo, dated, signature, multiple views, gigantic breasts",
|
||||
"blurry, lowres, error, worst quality, bad quality, jpeg artifacts, very displeasing, logo, dated, signature",
|
||||
""
|
||||
]
|
||||
|
||||
|
||||
async def post_novelai(url, data, header, proxy):
|
||||
async with aiohttp.ClientSession(headers=header) as session:
|
||||
try:
|
||||
async with session.post(url, json=data, proxy=proxy) as response:
|
||||
if response.status == 429:
|
||||
resp = await response.json()
|
||||
message = resp.get("message", "Too many requests, retrying...")
|
||||
await asyncio.sleep(5)
|
||||
return await post_novelai(url, data, header, proxy)
|
||||
elif response.status == 200:
|
||||
return await response.read()
|
||||
else:
|
||||
raise Exception(f"Request failed with status {response.status}")
|
||||
except Exception as e:
|
||||
print(f"Error during request: {e}")
|
||||
raise e
|
||||
|
||||
|
||||
class CharacterPrompt:
|
||||
def __init__(self, prompt: str, uc: str, x: float, y: float):
|
||||
self.prompt = prompt
|
||||
self.uc = uc
|
||||
self.x = x
|
||||
self.y = y
|
||||
self.center = {"x": self.x, "y": self.y}
|
||||
|
||||
|
||||
class BaseRequest:
|
||||
def __init__(
|
||||
self,
|
||||
prompt: Optional[str] = "",
|
||||
negative_prompt: Optional[str] = "",
|
||||
seed: int = -1,
|
||||
sampler_name: str = "Euler a",
|
||||
batch_size: int = 1,
|
||||
n_iter: int = 1,
|
||||
steps: int = 28,
|
||||
cfg_scale: float = 7,
|
||||
width: int = 832,
|
||||
height: int = 1216,
|
||||
denoising_strength: float = 1,
|
||||
scheduler: str = "Automatic",
|
||||
send_images: bool = True,
|
||||
save_images: bool = True,
|
||||
override_settings: Dict[str, Any] = {},
|
||||
override_settings_restore_afterwards: bool = False
|
||||
):
|
||||
self.prompt = prompt
|
||||
self.negative_prompt = negative_prompt
|
||||
self.seed = seed
|
||||
self.sampler_name = sampler_name
|
||||
self.batch_size = batch_size
|
||||
self.n_iter = n_iter
|
||||
self.steps = steps
|
||||
self.cfg_scale = cfg_scale
|
||||
self.width = width
|
||||
self.height = height
|
||||
self.denoising_strength = denoising_strength
|
||||
self.scheduler = scheduler
|
||||
self.send_images = send_images
|
||||
self.save_images = save_images
|
||||
self.override_settings = override_settings
|
||||
self.override_settings_restore_afterwards = override_settings_restore_afterwards
|
||||
|
||||
|
||||
class NovelAITXT2IMGPayload(BaseRequest):
|
||||
def __init__(
|
||||
self, ucPreset: int,
|
||||
cfg_rescale: int, characterPrompts: list[CharacterPrompt]|list,
|
||||
prefer_brownian: bool, base_request: BaseRequest, model
|
||||
):
|
||||
super().__init__(**vars(base_request))
|
||||
|
||||
self.scale = base_request.cfg_scale
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||||
self.sampler = base_request.sampler_name
|
||||
self.n_samples = base_request.n_iter
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||||
self.ucPreset = ucPreset
|
||||
self.cfg_rescale = cfg_rescale
|
||||
self.noise_schedule = base_request.scheduler
|
||||
self.characterPrompts = characterPrompts
|
||||
self.model = model
|
||||
characterPrompts_list = []
|
||||
|
||||
for cp in self.characterPrompts:
|
||||
cp = dict(vars(cp))
|
||||
del cp['x']
|
||||
del cp['y']
|
||||
characterPrompts_list.append(cp)
|
||||
|
||||
self.characterPrompts = characterPrompts_list
|
||||
char_captions = []
|
||||
for cp in self.characterPrompts:
|
||||
char_captions.append({"char_caption": cp["prompt"], "centers": [cp["center"]]})
|
||||
self.v4_prompt = {
|
||||
"caption": {
|
||||
"base_caption": self.prompt,
|
||||
"char_captions": char_captions
|
||||
}
|
||||
}
|
||||
char_captions = []
|
||||
for cp in self.characterPrompts:
|
||||
char_captions.append({"char_caption": cp["uc"], "centers": [cp["center"]]})
|
||||
self.v4_negative_prompt = {
|
||||
"caption": {
|
||||
"base_caption": self.negative_prompt,
|
||||
"char_captions": char_captions
|
||||
}
|
||||
}
|
||||
self.prefer_brownian = prefer_brownian
|
||||
|
||||
|
||||
class TXT2IMGRequestExtend(BaseRequest):
|
||||
|
||||
def __init__(self, script_name: str = "",
|
||||
restore_faces: bool = False, tiling: bool = False, subseed: int = -1, subseed_strength: float = 0,
|
||||
styles: List[str] = [], sampler_index: str = "Euler a", script_args: List[Any] = [],
|
||||
alwayson_scripts: Dict[str, Any] = {}, hr_scale: float = 2, hr_upscaler: str = "",
|
||||
hr_second_pass_steps: int = 10, hr_resize_x: int = 0, hr_resize_y: int = 0,
|
||||
hr_checkpoint_name: str = "", hr_sampler_name: str = "", hr_prompt: str = "",
|
||||
hr_negative_prompt: str = "", s_min_uncond: float = 0, s_churn: float = 0, s_tmax: float = 0,
|
||||
s_tmin: float = 0, s_noise: float = 0, refiner_checkpoint: str = "",
|
||||
refiner_switch_at: int = 0, disable_extra_networks: bool = False, comments: Dict[str, Any] = {},
|
||||
enable_hr: bool = False, firstphase_width: int = 0, firstphase_height: int = 0,
|
||||
do_not_save_samples: bool = False, do_not_save_grid: bool = False, eta: float = 0,
|
||||
seed_resize_from_h: int = -1, seed_resize_from_w: int = -1):
|
||||
|
||||
super().__init__()
|
||||
|
||||
self.script_name = script_name
|
||||
self.restore_faces = restore_faces
|
||||
self.tiling = tiling
|
||||
self.subseed = subseed
|
||||
self.subseed_strength = subseed_strength
|
||||
self.styles = styles
|
||||
self.sampler_index = sampler_index
|
||||
self.script_args = script_args
|
||||
self.alwayson_scripts = alwayson_scripts
|
||||
self.hr_scale = hr_scale
|
||||
self.hr_upscaler = hr_upscaler
|
||||
self.hr_second_pass_steps = hr_second_pass_steps
|
||||
self.hr_resize_x = hr_resize_x
|
||||
self.hr_resize_y = hr_resize_y
|
||||
self.hr_checkpoint_name = hr_checkpoint_name
|
||||
self.hr_sampler_name = hr_sampler_name
|
||||
self.hr_prompt = hr_prompt
|
||||
self.hr_negative_prompt = hr_negative_prompt
|
||||
self.s_min_uncond = s_min_uncond
|
||||
self.s_churn = s_churn
|
||||
self.s_tmax = s_tmax
|
||||
self.s_tmin = s_tmin
|
||||
self.s_noise = s_noise
|
||||
self.refiner_checkpoint = refiner_checkpoint
|
||||
self.refiner_switch_at = refiner_switch_at
|
||||
self.disable_extra_networks = disable_extra_networks
|
||||
self.comments = comments
|
||||
self.enable_hr = enable_hr
|
||||
self.firstphase_width = firstphase_width
|
||||
self.firstphase_height = firstphase_height
|
||||
self.do_not_save_samples = do_not_save_samples
|
||||
self.do_not_save_grid = do_not_save_grid
|
||||
self.eta = eta
|
||||
self.seed_resize_from_h = seed_resize_from_h
|
||||
self.seed_resize_from_w = seed_resize_from_w
|
||||
|
||||
|
||||
def tensor_to_pil(img_tensor, batch_index=0):
|
||||
# Takes an image in a batch in the form of a tensor of shape [batch_size, channels, height, width]
|
||||
# and returns an PIL Image with the corresponding mode deduced by the number of channels
|
||||
|
||||
# Take the image in the batch given by batch_index
|
||||
img_tensor = img_tensor[batch_index].unsqueeze(0)
|
||||
i = 255. * img_tensor.cpu().numpy()
|
||||
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8).squeeze())
|
||||
return img
|
||||
|
||||
|
||||
def pil_to_tensor(image):
|
||||
# Takes a PIL image and returns a tensor of shape [1, height, width, channels]
|
||||
image = np.array(image).astype(np.float32) / 255.0
|
||||
image = torch.from_numpy(image).unsqueeze(0)
|
||||
if len(image.shape) == 3: # If the image is grayscale, add a channel dimension
|
||||
image = image.unsqueeze(-1)
|
||||
return image
|
||||
|
||||
|
||||
class NovelAIRequest:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"payload": ("NovelAITXT2IMGPayload",),
|
||||
},
|
||||
"optional": {
|
||||
"token": ("STRING", {"default": "ey...."}),
|
||||
"proxy": ("STRING", {"default": "http://127.0.0.1:7890"})
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
RETURN_NAMES = ("image",)
|
||||
FUNCTION = "novelai_generate_image"
|
||||
OUTPUT_NODE = True
|
||||
CATEGORY = "SDWebUI-API/SDWebUI-API"
|
||||
|
||||
def novelai_generate_image(self, payload, token, proxy):
|
||||
|
||||
save_path = "./temp"
|
||||
token = token if token else os.getenv("NOVELAI_TOKEN", "")
|
||||
header = {
|
||||
"authorization": "Bearer " + token,
|
||||
":authority": "https://api.novelai.net",
|
||||
":path": "/ai/generate-image",
|
||||
"content-type": "application/json",
|
||||
"referer": "https://novelai.net",
|
||||
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/106.0.0.0 Safari/537.36",
|
||||
}
|
||||
|
||||
payload = {
|
||||
"input": payload.prompt,
|
||||
"model": payload.model,
|
||||
"parameters": dict(vars(payload))
|
||||
|
||||
}
|
||||
|
||||
response_data = asyncio.run(post_novelai("https://image.novelai.net/ai/generate-image", payload, header, proxy))
|
||||
|
||||
with zipfile.ZipFile(BytesIO(response_data)) as z:
|
||||
z.extractall(save_path)
|
||||
|
||||
image_bytes = []
|
||||
|
||||
def images_to_base64(save_path):
|
||||
|
||||
for filename in os.listdir(save_path):
|
||||
if filename.endswith('.png'):
|
||||
file_path = os.path.join(save_path, filename)
|
||||
with open(file_path, "rb") as image_file:
|
||||
image_data = image_file.read()
|
||||
image_bytes.append(image_data)
|
||||
|
||||
images_to_base64(save_path)
|
||||
|
||||
def pil_and_tensor(img_data: bytes):
|
||||
img = Image.open(BytesIO(img_data))
|
||||
tensor_img = pil_to_tensor(img)
|
||||
|
||||
return tensor_img
|
||||
|
||||
tensor_images = [pil_and_tensor(img_data) for img_data in image_bytes]
|
||||
|
||||
tensor = torch.cat(tensor_images, dim=0)
|
||||
|
||||
return (tensor,)
|
||||
|
||||
|
||||
class CharacterPromptSelect:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"character1": ("STRING", {"default": "firefly_(honkai:_star_rail)"}),
|
||||
"character1_uc": ("STRING", {"default": "negative_prompt"}),
|
||||
"character1_x": ("FLOAT", {"default": 0.5}),
|
||||
"character1_y": ("FLOAT", {"default": 0.5}),
|
||||
},
|
||||
"optional": {
|
||||
"character2_enable": ("BOOLEAN", {"default": True}),
|
||||
"character2": ("STRING", {"default": "kafka__(honkai:_star_rail)"}),
|
||||
"character2_uc": ("STRING", {"default": "negative_prompt"}),
|
||||
"character2_x": ("FLOAT", {"default": 0.1}),
|
||||
"character2_y": ("FLOAT", {"default": 0.5}),
|
||||
"character3_enable": ("BOOLEAN", {"default": False}),
|
||||
"character3": ("STRING", {"default": ""}),
|
||||
"character3_uc": ("STRING", {"default": "negative_prompt"}),
|
||||
"character3_x": ("FLOAT", {"default": 0.1}),
|
||||
"character3_y": ("FLOAT", {"default": 0.5}),
|
||||
"character4_enable": ("BOOLEAN", {"default": False}),
|
||||
"character4": ("STRING", {"default": ""}),
|
||||
"character4_uc": ("STRING", {"default": "negative_prompt"}),
|
||||
"character4_x": ("FLOAT", {"default": 0.1}),
|
||||
"character4_y": ("FLOAT", {"default": 0.5}),
|
||||
"character5_enable": ("BOOLEAN", {"default": False}),
|
||||
"character5": ("STRING", {"default": ""}),
|
||||
"character5_uc": ("STRING", {"default": "negative_prompt"}),
|
||||
"character5_x": ("FLOAT", {"default": 0.1}),
|
||||
"character5_y": ("FLOAT", {"default": 0.5}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("LIST",)
|
||||
RETURN_NAMES = ("CharacterPrompt",)
|
||||
FUNCTION = "build_character_prompt"
|
||||
OUTPUT_NODE = False
|
||||
CATEGORY = "SDWebUI-API/SDWebUI-API"
|
||||
|
||||
def build_character_prompt(
|
||||
self, character1, character1_uc, character1_x, character1_y,
|
||||
character2_enable, character2, character2_uc, character2_x, character2_y,
|
||||
character3_enable, character3, character3_uc, character3_x, character3_y,
|
||||
character4_enable, character4, character4_uc, character4_x, character4_y,
|
||||
character5_enable, character5, character5_uc, character5_x, character5_y
|
||||
):
|
||||
character1_pos = {"x": character1_x, "y": character1_y}
|
||||
character2_pos = {"x": character2_x, "y": character2_y}
|
||||
character3_pos = {"x": character3_x, "y": character3_y}
|
||||
character4_pos = {"x": character4_x, "y": character4_y}
|
||||
character5_pos = {"x": character5_x, "y": character5_y}
|
||||
|
||||
character_prompts = []
|
||||
|
||||
character_prompts.append(CharacterPrompt(character1, character1_uc, character1_pos["x"], character1_pos["y"]))
|
||||
|
||||
if character2_enable:
|
||||
character_prompts.append(CharacterPrompt(character2, character2_uc, character2_pos["x"], character2_pos["y"]))
|
||||
if character3_enable:
|
||||
character_prompts.append(CharacterPrompt(character3, character3_uc, character3_pos["x"], character3_pos["y"]))
|
||||
if character4_enable:
|
||||
character_prompts.append(CharacterPrompt(character4, character4_uc, character4_pos["x"], character4_pos["y"]))
|
||||
if character5_enable:
|
||||
character_prompts.append(CharacterPrompt(character5, character5_uc, character5_pos["x"], character5_pos["y"]))
|
||||
|
||||
return (character_prompts,)
|
||||
|
||||
|
||||
class NovelAIRequestPayload:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
sampler_list = [
|
||||
"k_dpmpp_2m", "k_dpmpp_sde", "k_dpmpp_2m_sde", "k_dpmpp_2s_ancestral",
|
||||
"k_euler_ancestral", "k_euler", "ddim_v3"
|
||||
]
|
||||
scheduler_list = ["karras"]
|
||||
|
||||
model_list = [
|
||||
"nai-diffusion-4-curated-preview",
|
||||
"nai-diffusion-3",
|
||||
"nai-diffusion-furry-3",
|
||||
"nai-diffusion-2",
|
||||
]
|
||||
|
||||
return {
|
||||
"required": {
|
||||
"prompt": ("STRING", {"default": "prompt here"})
|
||||
},
|
||||
"optional": {
|
||||
"negative_prompt": ("STRING", {"default": "negative prompt here"}),
|
||||
"seed": ("INT", {"default": -1}),
|
||||
"sampler": (sampler_list,),
|
||||
"n_iter": ("INT", {"default": 1}),
|
||||
"steps": ("INT", {"default": 28}),
|
||||
"cfg_scale": ("FLOAT", {"default": 6}),
|
||||
"width": ("INT", {"default": 832}),
|
||||
"height": ("INT", {"default": 1216}),
|
||||
|
||||
"scheduler": (scheduler_list,),
|
||||
"ucPreset": (ucPreset_list,),
|
||||
"cfg_rescale": ("INT", {"default": 0}),
|
||||
"characterPrompts": ("LIST",),
|
||||
"prefer_brownian": ("BOOLEAN", {"default": False}),
|
||||
"model": (model_list, )
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("NovelAITXT2IMGPayload", "DICT")
|
||||
RETURN_NAMES = ("payload", "payload_dict")
|
||||
FUNCTION = "build_payload"
|
||||
CATEGORY = "SDWebUI-API/SDWebUI-API"
|
||||
# INPUT_IS_LIST = False
|
||||
# OUTPUT_IS_LIST = (False,)
|
||||
|
||||
def build_payload(
|
||||
self,
|
||||
prompt,
|
||||
negative_prompt,
|
||||
seed,
|
||||
sampler,
|
||||
n_iter,
|
||||
steps,
|
||||
cfg_scale,
|
||||
width,
|
||||
height,
|
||||
scheduler,
|
||||
ucPreset,
|
||||
cfg_rescale,
|
||||
prefer_brownian,
|
||||
model,
|
||||
characterPrompts=[],
|
||||
):
|
||||
negative_prompt = negative_prompt + "," + ucPreset
|
||||
instance_ = BaseRequest(
|
||||
prompt=prompt,
|
||||
negative_prompt=negative_prompt,
|
||||
seed=seed,
|
||||
sampler_name=sampler,
|
||||
n_iter=n_iter,
|
||||
steps=steps,
|
||||
cfg_scale=cfg_scale,
|
||||
width=width,
|
||||
height=height,
|
||||
scheduler=scheduler,
|
||||
)
|
||||
|
||||
ucPreset = ucPreset_list.index(ucPreset)
|
||||
|
||||
instance_ = NovelAITXT2IMGPayload(ucPreset,cfg_rescale,characterPrompts,prefer_brownian, instance_, model)
|
||||
|
||||
return instance_, dict(vars(instance_))
|
||||
|
||||
|
||||
class SDWebUIRequest:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"payload": ("BaseRequest",),
|
||||
"backend_url": ("STRING", {"default": "http://127.0.0.1:7860"}),
|
||||
},
|
||||
"optional": {
|
||||
"payload_extend": ("TXT2IMGRequestExtend",)
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
RETURN_NAMES = ("image",)
|
||||
FUNCTION = "sdweb_generate_image"
|
||||
OUTPUT_NODE = True
|
||||
CATEGORY = "SDWebUI-API/SDWebUI-API"
|
||||
|
||||
def sdweb_generate_image(self, payload, backend_url):
|
||||
response = requests.post(backend_url + "/sdapi/v1/txt2img", json=dict(vars(payload)))
|
||||
resp = response.json()
|
||||
b64_images = resp["images"]
|
||||
|
||||
def b64_to_pil_and_tensor(b64_image: str):
|
||||
img_data = base64.b64decode(b64_image)
|
||||
img = Image.open(BytesIO(img_data))
|
||||
tensor_img = pil_to_tensor(img)
|
||||
|
||||
return tensor_img
|
||||
|
||||
tensor_images = [b64_to_pil_and_tensor(b64_image) for b64_image in b64_images]
|
||||
|
||||
tensor = torch.cat(tensor_images, dim=0)
|
||||
|
||||
return (tensor,)
|
||||
|
||||
|
||||
class SDWebUIRequestPayload:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
sampler_list = [
|
||||
"DPM++ 2M", "DPM++ SDE", "DPM++ 2M SDE", "DPM++ 2M SDE Heun", "DPM++ 2S a", "DPM++ 3M SDE",
|
||||
"Euler a", "Euler", "LMS", "Heun", "DPM2", "DPM2 a", "DPM fast", "DPM adaptive", "Restart",
|
||||
"HeunPP2", "IPNDM", "IPNDM_V", "DEIS", "DDIM", "DDIM CFG++", "PLMS", "UniPC", "LCM", "DDPM"
|
||||
]
|
||||
scheduler_list = ["Automatic", "Karras", "Exponential", "SGM Uniform", "Simple", "Normal", "DDIM", "Beta"]
|
||||
|
||||
return {
|
||||
"required": {
|
||||
"prompt": ("STRING", {"default": "prompt here"})
|
||||
},
|
||||
"optional": {
|
||||
"negative_prompt": ("STRING", {"default": "negative prompt here"}),
|
||||
"seed": ("INT", {"default": -1}),
|
||||
"sampler": (sampler_list,),
|
||||
"batch_size": ("INT", {"default": 1}),
|
||||
"n_iter": ("INT", {"default": 1}),
|
||||
"steps": ("INT", {"default": 20}),
|
||||
"cfg_scale": ("FLOAT", {"default": 7}),
|
||||
"width": ("INT", {"default": 512}),
|
||||
"height": ("INT", {"default": 512}),
|
||||
"denoising_strength": ("FLOAT", {"default": 1}),
|
||||
"scheduler": (scheduler_list,),
|
||||
"send_images": ("BOOLEAN", {"default": True}),
|
||||
"save_images": ("BOOLEAN", {"default": True}),
|
||||
"override_settings": ("STRING", {"default": ""}),
|
||||
"override_settings_restore_afterwards": ("BOOLEAN", {"default": False}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("BaseRequest", "DICT")
|
||||
RETURN_NAMES = ("payload", "payload_dict")
|
||||
FUNCTION = "build_payload"
|
||||
CATEGORY = "SDWebUI-API/SDWebUI-API"
|
||||
# INPUT_IS_LIST = False
|
||||
# OUTPUT_IS_LIST = (False,)
|
||||
|
||||
def build_payload(
|
||||
self,
|
||||
prompt,
|
||||
negative_prompt,
|
||||
seed,
|
||||
sampler,
|
||||
batch_size,
|
||||
n_iter,
|
||||
steps,
|
||||
cfg_scale,
|
||||
width,
|
||||
height,
|
||||
denoising_strength,
|
||||
scheduler,
|
||||
send_images,
|
||||
save_images,
|
||||
override_settings,
|
||||
override_settings_restore_afterwards
|
||||
):
|
||||
instance_ = BaseRequest(
|
||||
prompt=prompt,
|
||||
negative_prompt=negative_prompt,
|
||||
seed=seed,
|
||||
sampler_name=sampler,
|
||||
batch_size=batch_size,
|
||||
n_iter=n_iter,
|
||||
steps=steps,
|
||||
cfg_scale=cfg_scale,
|
||||
width=width,
|
||||
height=height,
|
||||
denoising_strength=denoising_strength,
|
||||
scheduler=scheduler,
|
||||
send_images=send_images,
|
||||
save_images=save_images,
|
||||
override_settings=json.loads(override_settings),
|
||||
override_settings_restore_afterwards=override_settings_restore_afterwards
|
||||
|
||||
)
|
||||
|
||||
return instance_, dict(vars(instance_))
|
||||
|
||||
|
||||
class SDWebUIRequestPayloadExtend:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"optional": {
|
||||
"script_name": ("STRING", {"default": ""}),
|
||||
"restore_faces": ("BOOLEAN", {"default": False}),
|
||||
"tiling": ("BOOLEAN", {"default": False}),
|
||||
"subseed": ("INT", {"default": -1}),
|
||||
"subseed_strength": ("FLOAT", {"default": 0}),
|
||||
"styles": ("LIST", {"default": []}),
|
||||
"sampler_index": ("STRING", {"default": ""}),
|
||||
"script_args": ("STRING", {"default": ""}),
|
||||
"alwayson_scripts": ("STRING", {"default": "alwayson_scripts"}),
|
||||
"hr_scale": ("FLOAT", {"default": 2}),
|
||||
"hr_upscaler": ("STRING", {"default": "hr_upscaler"}),
|
||||
"hr_second_pass_steps": ("INT", {"default": 0}),
|
||||
"hr_resize_x": ("INT", {"default": 0}),
|
||||
"hr_resize_y": ("INT", {"default": 0}),
|
||||
"hr_checkpoint_name": ("STRING", {"default": ""}),
|
||||
"hr_sampler_name": ("STRING", {"default": ""}),
|
||||
"hr_prompt": ("STRING", {"default": "hr_prompt"}),
|
||||
"hr_negative_prompt": ("STRING", {"default": ""}),
|
||||
"s_min_uncond": ("FLOAT", {"default": 0}),
|
||||
"s_churn": ("FLOAT", {"default": 0}),
|
||||
"s_tmax": ("FLOAT", {"default": 0}),
|
||||
"s_tmin": ("FLOAT", {"default": 0}),
|
||||
"s_noise": ("FLOAT", {"default": 0}),
|
||||
"refiner_checkpoint": ("STRING", {"default": ""}),
|
||||
"refiner_switch_at": ("INT", {"default": 0}),
|
||||
"disable_extra_networks": ("BOOLEAN", {"default": False}),
|
||||
"comments": ("STRING", {"default": ""}),
|
||||
"enable_hr": ("BOOLEAN", {"default": False}),
|
||||
"firstphase_width": ("INT", {"default": 0}),
|
||||
"firstphase_height": ("INT", {"default": 0}),
|
||||
"do_not_save_samples": ("BOOLEAN", {"default": False}),
|
||||
"do_not_save_grid": ("BOOLEAN", {"default": False}),
|
||||
"eta": ("FLOAT", {"default": 0}),
|
||||
"seed_resize_from_h": ("INT", {"default": 0}),
|
||||
"seed_resize_from_w": ("INT", {"default": 0})
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("TXT2IMGRequestExtend", "DICT")
|
||||
RETURN_NAMES = ("payload", "payload_dict")
|
||||
FUNCTION = "build_payload"
|
||||
CATEGORY = "SDWebUI-API/SDWebUI-API"
|
||||
|
||||
def build_payload(
|
||||
self,
|
||||
script_name: str = "",
|
||||
restore_faces: bool = False,
|
||||
tiling: bool = False,
|
||||
subseed: int = -1,
|
||||
subseed_strength: float = 0,
|
||||
styles: List[str] = [],
|
||||
sampler_index: str = "Euler a",
|
||||
script_args: List[Any] = [],
|
||||
alwayson_scripts: Dict[str, Any] = {},
|
||||
hr_scale: float = 2,
|
||||
hr_upscaler: str = "",
|
||||
hr_second_pass_steps: int = 0,
|
||||
hr_resize_x: int = 0,
|
||||
hr_resize_y: int = 0,
|
||||
hr_checkpoint_name: str = "",
|
||||
hr_sampler_name: str = "",
|
||||
hr_prompt: str = "",
|
||||
hr_negative_prompt: str = "",
|
||||
s_min_uncond: float = 0,
|
||||
s_churn: float = 0,
|
||||
s_tmax: float = 0,
|
||||
s_tmin: float = 0,
|
||||
s_noise: float = 0,
|
||||
refiner_checkpoint: str = "",
|
||||
refiner_switch_at: int = 0,
|
||||
disable_extra_networks: bool = False,
|
||||
comments: str = "",
|
||||
enable_hr: bool = False,
|
||||
firstphase_width: int = 0,
|
||||
firstphase_height: int = 0,
|
||||
do_not_save_samples: bool = False,
|
||||
do_not_save_grid: bool = False,
|
||||
eta: float = 0,
|
||||
seed_resize_from_h: int = 0,
|
||||
seed_resize_from_w: int = 0
|
||||
):
|
||||
instance_ = TXT2IMGRequestExtend(
|
||||
script_name, restore_faces,
|
||||
tiling, subseed, subseed_strength, styles, sampler_index,
|
||||
script_args, alwayson_scripts, hr_scale, hr_upscaler,
|
||||
hr_second_pass_steps, hr_resize_x, hr_resize_y, hr_checkpoint_name, hr_sampler_name, hr_prompt, hr_negative_prompt,
|
||||
s_min_uncond, s_churn, s_tmax, s_tmin, s_noise,
|
||||
refiner_checkpoint, refiner_switch_at, disable_extra_networks, comments, enable_hr, firstphase_width,
|
||||
firstphase_height, do_not_save_samples, do_not_save_grid, eta, seed_resize_from_h, seed_resize_from_w
|
||||
)
|
||||
|
||||
return instance_, dict(vars(instance_))
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"SDWebUI_Request": SDWebUIRequest,
|
||||
"SDWebUI_Request_Payload": SDWebUIRequestPayload,
|
||||
"SDWebUI_Request_PayloadExtend": SDWebUIRequestPayloadExtend,
|
||||
"NovelAI_Request": NovelAIRequest,
|
||||
"Character_Prompt_Select": CharacterPromptSelect,
|
||||
"NovelAI_Request_Payload": NovelAIRequestPayload
|
||||
}
|
||||
|
||||
# A dictionary that contains the friendly/humanly readable titles for the nodes
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"SDWebUI_Request": "SDWebUIRequest",
|
||||
"SDWebUI_Request_Payload": "SDWebUIRequestPayload",
|
||||
"SDWebUI_Request_PayloadExtend": "SDWebUIRequestPayloadExtend",
|
||||
"NovelAI_Request": "NovelAIRequest",
|
||||
"Character_Prompt_Select": "CharacterPromptSelect",
|
||||
"NovelAI_Request_Payload": "NovelAIRequestPayload"
|
||||
|
||||
}
|
||||
@@ -0,0 +1 @@
|
||||
python-dotenv
|
||||
@@ -0,0 +1,25 @@
|
||||
# Comfyui SD-API/NOVELAI API请求节点/request node
|
||||
|
||||
## support SD-WEBUI API and NOVELAI (support nai-diffusion-3 and nai-diffusion-4)/支持请求SD-WEBUI API以及 NOVELAI(支持nai-diffusion-3和nai-diffusion-4)
|
||||
|
||||
|
||||
- https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API
|
||||
- https://novelai.net/image
|
||||
|
||||
## SETUP
|
||||
- 在comfyui根目录下创建.env文件, 编写 NOVELAI_TOKEN 以调用NOVELAI
|
||||
- create .env file in the root directory of comfyui, write NOVELAI_TOKEN to call NOVELAI
|
||||
|
||||
```dotenv
|
||||
NOVELAI_TOKEN=ey....
|
||||
```
|
||||
|
||||
## Get Token
|
||||
- 打开控制台, 输入以下命令
|
||||
- Open console, type
|
||||
|
||||
```js
|
||||
console.log(JSON.parse(localStorage.session).auth_token);
|
||||
```
|
||||
|
||||
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 292 KiB |
@@ -0,0 +1,75 @@
|
||||
{
|
||||
"30": {
|
||||
"inputs": {
|
||||
"token": "",
|
||||
"proxy": "http://127.0.0.1:7890",
|
||||
"payload": [
|
||||
"39",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "NovelAI_Request",
|
||||
"_meta": {
|
||||
"title": "NovelAIRequest"
|
||||
}
|
||||
},
|
||||
"39": {
|
||||
"inputs": {
|
||||
"prompt": [
|
||||
"42",
|
||||
0
|
||||
],
|
||||
"negative_prompt": [
|
||||
"41",
|
||||
0
|
||||
],
|
||||
"seed": 198,
|
||||
"sampler": "k_euler_ancestral",
|
||||
"n_iter": 1,
|
||||
"steps": 28,
|
||||
"cfg_scale": 7,
|
||||
"width": 832,
|
||||
"height": 1216,
|
||||
"scheduler": "karras",
|
||||
"ucPreset": "blurry, lowres, error, film grain, scan artifacts, worst quality, bad quality, jpeg artifacts, very displeasing, chromatic aberration, logo, dated, signature, multiple views, gigantic breasts",
|
||||
"cfg_rescale": 0,
|
||||
"prefer_brownian": false,
|
||||
"model": "nai-diffusion-3"
|
||||
},
|
||||
"class_type": "NovelAI_Request_Payload",
|
||||
"_meta": {
|
||||
"title": "NovelAIRequestPayload"
|
||||
}
|
||||
},
|
||||
"41": {
|
||||
"inputs": {
|
||||
"Text": ""
|
||||
},
|
||||
"class_type": "DF_Text",
|
||||
"_meta": {
|
||||
"title": "Text"
|
||||
}
|
||||
},
|
||||
"42": {
|
||||
"inputs": {
|
||||
"Text": "{prompt},best quality, amazing quality, very aesthetic, absurdres,"
|
||||
},
|
||||
"class_type": "DF_Text",
|
||||
"_meta": {
|
||||
"title": "Text"
|
||||
}
|
||||
},
|
||||
"44": {
|
||||
"inputs": {
|
||||
"filename_prefix": "ComfyUI/nai3/nai",
|
||||
"images": [
|
||||
"30",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "SaveImage",
|
||||
"_meta": {
|
||||
"title": "Save Image"
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,75 @@
|
||||
{
|
||||
"30": {
|
||||
"inputs": {
|
||||
"token": "",
|
||||
"proxy": "http://127.0.0.1:7890",
|
||||
"payload": [
|
||||
"39",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "NovelAI_Request",
|
||||
"_meta": {
|
||||
"title": "NovelAIRequest"
|
||||
}
|
||||
},
|
||||
"39": {
|
||||
"inputs": {
|
||||
"prompt": [
|
||||
"42",
|
||||
0
|
||||
],
|
||||
"negative_prompt": [
|
||||
"41",
|
||||
0
|
||||
],
|
||||
"seed": 198,
|
||||
"sampler": "k_euler_ancestral",
|
||||
"n_iter": 1,
|
||||
"steps": 28,
|
||||
"cfg_scale": 7,
|
||||
"width": 832,
|
||||
"height": 1216,
|
||||
"scheduler": "karras",
|
||||
"ucPreset": "blurry, lowres, error, film grain, scan artifacts, worst quality, bad quality, jpeg artifacts, very displeasing, chromatic aberration, logo, dated, signature, multiple views, gigantic breasts",
|
||||
"cfg_rescale": 0,
|
||||
"prefer_brownian": true,
|
||||
"model": "nai-diffusion-4-curated-preview"
|
||||
},
|
||||
"class_type": "NovelAI_Request_Payload",
|
||||
"_meta": {
|
||||
"title": "NovelAIRequestPayload"
|
||||
}
|
||||
},
|
||||
"41": {
|
||||
"inputs": {
|
||||
"Text": ""
|
||||
},
|
||||
"class_type": "DF_Text",
|
||||
"_meta": {
|
||||
"title": "Text"
|
||||
}
|
||||
},
|
||||
"42": {
|
||||
"inputs": {
|
||||
"Text": "{prompt},rating:general, amazing quality, very aesthetic, absurdres,"
|
||||
},
|
||||
"class_type": "DF_Text",
|
||||
"_meta": {
|
||||
"title": "Text"
|
||||
}
|
||||
},
|
||||
"44": {
|
||||
"inputs": {
|
||||
"filename_prefix": "ComfyUI/nai4/nai",
|
||||
"images": [
|
||||
"30",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "SaveImage",
|
||||
"_meta": {
|
||||
"title": "Save Image"
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,111 @@
|
||||
{
|
||||
"30": {
|
||||
"inputs": {
|
||||
"token": "",
|
||||
"proxy": "http://127.0.0.1:7890",
|
||||
"payload": [
|
||||
"39",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "NovelAI_Request",
|
||||
"_meta": {
|
||||
"title": "NovelAIRequest"
|
||||
}
|
||||
},
|
||||
"38": {
|
||||
"inputs": {
|
||||
"character1": "firefly_(honkai:_star_rail), kimono",
|
||||
"character1_uc": "",
|
||||
"character1_x": 0.3,
|
||||
"character1_y": 0.5,
|
||||
"character2_enable": true,
|
||||
"character2": "kafka_(honkai:_star_rail), serafuku",
|
||||
"character2_uc": "",
|
||||
"character2_x": 0.7000000000000001,
|
||||
"character2_y": 0.5,
|
||||
"character3_enable": false,
|
||||
"character3": "",
|
||||
"character3_uc": "negative_prompt",
|
||||
"character3_x": 0.1,
|
||||
"character3_y": 0.5,
|
||||
"character4_enable": false,
|
||||
"character4": "",
|
||||
"character4_uc": "negative_prompt",
|
||||
"character4_x": 0.1,
|
||||
"character4_y": 0.5,
|
||||
"character5_enable": false,
|
||||
"character5": "",
|
||||
"character5_uc": "negative_prompt",
|
||||
"character5_x": 0.1,
|
||||
"character5_y": 0.5
|
||||
},
|
||||
"class_type": "Character_Prompt_Select",
|
||||
"_meta": {
|
||||
"title": "CharacterPromptSelect"
|
||||
}
|
||||
},
|
||||
"39": {
|
||||
"inputs": {
|
||||
"prompt": [
|
||||
"42",
|
||||
0
|
||||
],
|
||||
"negative_prompt": [
|
||||
"41",
|
||||
0
|
||||
],
|
||||
"seed": 198,
|
||||
"sampler": "k_euler_ancestral",
|
||||
"n_iter": 1,
|
||||
"steps": 28,
|
||||
"cfg_scale": 7,
|
||||
"width": 832,
|
||||
"height": 1216,
|
||||
"scheduler": "karras",
|
||||
"ucPreset": "blurry, lowres, error, film grain, scan artifacts, worst quality, bad quality, jpeg artifacts, very displeasing, chromatic aberration, logo, dated, signature, multiple views, gigantic breasts",
|
||||
"cfg_rescale": 0,
|
||||
"prefer_brownian": true,
|
||||
"model": "nai-diffusion-4-curated-preview",
|
||||
"characterPrompts": [
|
||||
"38",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "NovelAI_Request_Payload",
|
||||
"_meta": {
|
||||
"title": "NovelAIRequestPayload"
|
||||
}
|
||||
},
|
||||
"41": {
|
||||
"inputs": {
|
||||
"Text": ""
|
||||
},
|
||||
"class_type": "DF_Text",
|
||||
"_meta": {
|
||||
"title": "Text"
|
||||
}
|
||||
},
|
||||
"42": {
|
||||
"inputs": {
|
||||
"Text": "{prompt},rating:general, amazing quality, very aesthetic, absurdres,"
|
||||
},
|
||||
"class_type": "DF_Text",
|
||||
"_meta": {
|
||||
"title": "Text"
|
||||
}
|
||||
},
|
||||
"44": {
|
||||
"inputs": {
|
||||
"filename_prefix": "ComfyUI/nai4/nai",
|
||||
"images": [
|
||||
"30",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "SaveImage",
|
||||
"_meta": {
|
||||
"title": "Save Image"
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user