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feat: Add ComfyUI tool for Stable Diffusion (#8160)
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Qun
GitHub
parent
e896d1e9d7
commit
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BIN
api/core/tools/provider/builtin/comfyui/_assets/icon.png
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api/core/tools/provider/builtin/comfyui/_assets/icon.png
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api/core/tools/provider/builtin/comfyui/comfyui.py
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api/core/tools/provider/builtin/comfyui/comfyui.py
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from typing import Any
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from core.tools.errors import ToolProviderCredentialValidationError
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from core.tools.provider.builtin.comfyui.tools.comfyui_stable_diffusion import ComfyuiStableDiffusionTool
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from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
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class ComfyUIProvider(BuiltinToolProviderController):
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def _validate_credentials(self, credentials: dict[str, Any]) -> None:
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try:
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ComfyuiStableDiffusionTool().fork_tool_runtime(
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runtime={
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"credentials": credentials,
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}
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).validate_models()
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except Exception as e:
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raise ToolProviderCredentialValidationError(str(e))
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42
api/core/tools/provider/builtin/comfyui/comfyui.yaml
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api/core/tools/provider/builtin/comfyui/comfyui.yaml
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identity:
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author: Qun
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name: comfyui
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label:
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en_US: ComfyUI
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zh_Hans: ComfyUI
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pt_BR: ComfyUI
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description:
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en_US: ComfyUI is a tool for generating images which can be deployed locally.
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zh_Hans: ComfyUI 是一个可以在本地部署的图片生成的工具。
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pt_BR: ComfyUI is a tool for generating images which can be deployed locally.
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icon: icon.png
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tags:
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- image
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credentials_for_provider:
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base_url:
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type: text-input
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required: true
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label:
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en_US: Base URL
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zh_Hans: ComfyUI服务器的Base URL
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pt_BR: Base URL
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placeholder:
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en_US: Please input your ComfyUI server's Base URL
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zh_Hans: 请输入你的 ComfyUI 服务器的 Base URL
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pt_BR: Please input your ComfyUI server's Base URL
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model:
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type: text-input
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required: true
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label:
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en_US: Model with suffix
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zh_Hans: 模型, 需要带后缀
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pt_BR: Model with suffix
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placeholder:
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en_US: Please input your model
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zh_Hans: 请输入你的模型名称
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pt_BR: Please input your model
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help:
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en_US: The checkpoint name of the ComfyUI server, e.g. xxx.safetensors
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zh_Hans: ComfyUI服务器的模型名称, 比如 xxx.safetensors
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pt_BR: The checkpoint name of the ComfyUI server, e.g. xxx.safetensors
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url: https://docs.dify.ai/tutorials/tool-configuration/comfyui
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@ -0,0 +1,475 @@
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import json
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import os
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import random
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import uuid
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from copy import deepcopy
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from enum import Enum
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from typing import Any, Union
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import websocket
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from httpx import get, post
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from yarl import URL
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from core.tools.entities.common_entities import I18nObject
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from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParameter, ToolParameterOption
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from core.tools.errors import ToolProviderCredentialValidationError
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from core.tools.tool.builtin_tool import BuiltinTool
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SD_TXT2IMG_OPTIONS = {}
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LORA_NODE = {
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"inputs": {"lora_name": "", "strength_model": 1, "strength_clip": 1, "model": ["11", 0], "clip": ["11", 1]},
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"class_type": "LoraLoader",
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"_meta": {"title": "Load LoRA"},
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}
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FluxGuidanceNode = {
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"inputs": {"guidance": 3.5, "conditioning": ["6", 0]},
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"class_type": "FluxGuidance",
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"_meta": {"title": "FluxGuidance"},
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}
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class ModelType(Enum):
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SD15 = 1
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SDXL = 2
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SD3 = 3
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FLUX = 4
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class ComfyuiStableDiffusionTool(BuiltinTool):
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def _invoke(
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self, user_id: str, tool_parameters: dict[str, Any]
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) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
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"""
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invoke tools
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"""
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# base url
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base_url = self.runtime.credentials.get("base_url", "")
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if not base_url:
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return self.create_text_message("Please input base_url")
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if tool_parameters.get("model"):
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self.runtime.credentials["model"] = tool_parameters["model"]
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model = self.runtime.credentials.get("model", None)
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if not model:
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return self.create_text_message("Please input model")
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# prompt
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prompt = tool_parameters.get("prompt", "")
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if not prompt:
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return self.create_text_message("Please input prompt")
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# get negative prompt
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negative_prompt = tool_parameters.get("negative_prompt", "")
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# get size
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width = tool_parameters.get("width", 1024)
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height = tool_parameters.get("height", 1024)
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# get steps
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steps = tool_parameters.get("steps", 1)
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# get sampler_name
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sampler_name = tool_parameters.get("sampler_name", "euler")
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# scheduler
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scheduler = tool_parameters.get("scheduler", "normal")
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# get cfg
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cfg = tool_parameters.get("cfg", 7.0)
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# get model type
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model_type = tool_parameters.get("model_type", ModelType.SD15.name)
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# get lora
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# supports up to 3 loras
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lora_list = []
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lora_strength_list = []
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if tool_parameters.get("lora_1"):
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lora_list.append(tool_parameters["lora_1"])
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lora_strength_list.append(tool_parameters.get("lora_strength_1", 1))
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if tool_parameters.get("lora_2"):
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lora_list.append(tool_parameters["lora_2"])
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lora_strength_list.append(tool_parameters.get("lora_strength_2", 1))
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if tool_parameters.get("lora_3"):
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lora_list.append(tool_parameters["lora_3"])
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lora_strength_list.append(tool_parameters.get("lora_strength_3", 1))
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return self.text2img(
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base_url=base_url,
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model=model,
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model_type=model_type,
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prompt=prompt,
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negative_prompt=negative_prompt,
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width=width,
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height=height,
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steps=steps,
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sampler_name=sampler_name,
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scheduler=scheduler,
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cfg=cfg,
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lora_list=lora_list,
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lora_strength_list=lora_strength_list,
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)
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def get_checkpoints(self) -> list[str]:
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"""
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get checkpoints
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"""
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try:
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base_url = self.runtime.credentials.get("base_url", None)
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if not base_url:
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return []
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api_url = str(URL(base_url) / "models" / "checkpoints")
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response = get(url=api_url, timeout=(2, 10))
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if response.status_code != 200:
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return []
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else:
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return response.json()
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except Exception as e:
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return []
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def get_loras(self) -> list[str]:
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"""
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get loras
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"""
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try:
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base_url = self.runtime.credentials.get("base_url", None)
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if not base_url:
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return []
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api_url = str(URL(base_url) / "models" / "loras")
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response = get(url=api_url, timeout=(2, 10))
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if response.status_code != 200:
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return []
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else:
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return response.json()
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except Exception as e:
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return []
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def get_sample_methods(self) -> tuple[list[str], list[str]]:
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"""
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get sample method
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"""
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try:
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base_url = self.runtime.credentials.get("base_url", None)
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if not base_url:
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return [], []
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api_url = str(URL(base_url) / "object_info" / "KSampler")
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response = get(url=api_url, timeout=(2, 10))
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if response.status_code != 200:
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return [], []
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else:
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data = response.json()["KSampler"]["input"]["required"]
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return data["sampler_name"][0], data["scheduler"][0]
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except Exception as e:
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return [], []
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def validate_models(self) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
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"""
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validate models
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"""
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try:
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base_url = self.runtime.credentials.get("base_url", None)
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if not base_url:
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raise ToolProviderCredentialValidationError("Please input base_url")
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model = self.runtime.credentials.get("model", None)
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if not model:
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raise ToolProviderCredentialValidationError("Please input model")
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api_url = str(URL(base_url) / "models" / "checkpoints")
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response = get(url=api_url, timeout=(2, 10))
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if response.status_code != 200:
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raise ToolProviderCredentialValidationError("Failed to get models")
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else:
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models = response.json()
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if len([d for d in models if d == model]) > 0:
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return self.create_text_message(json.dumps(models))
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else:
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raise ToolProviderCredentialValidationError(f"model {model} does not exist")
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except Exception as e:
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raise ToolProviderCredentialValidationError(f"Failed to get models, {e}")
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def get_history(self, base_url, prompt_id):
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"""
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get history
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"""
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url = str(URL(base_url) / "history")
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respond = get(url, params={"prompt_id": prompt_id}, timeout=(2, 10))
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return respond.json()
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def download_image(self, base_url, filename, subfolder, folder_type):
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"""
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download image
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"""
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url = str(URL(base_url) / "view")
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response = get(url, params={"filename": filename, "subfolder": subfolder, "type": folder_type}, timeout=(2, 10))
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return response.content
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def queue_prompt_image(self, base_url, client_id, prompt):
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"""
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send prompt task and rotate
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"""
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# initiate task execution
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url = str(URL(base_url) / "prompt")
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respond = post(url, data=json.dumps({"client_id": client_id, "prompt": prompt}), timeout=(2, 10))
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prompt_id = respond.json()["prompt_id"]
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ws = websocket.WebSocket()
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if "https" in base_url:
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ws_url = base_url.replace("https", "ws")
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else:
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ws_url = base_url.replace("http", "ws")
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ws.connect(str(URL(f"{ws_url}") / "ws") + f"?clientId={client_id}", timeout=120)
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# websocket rotate execution status
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output_images = {}
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while True:
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out = ws.recv()
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if isinstance(out, str):
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message = json.loads(out)
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if message["type"] == "executing":
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data = message["data"]
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if data["node"] is None and data["prompt_id"] == prompt_id:
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break # Execution is done
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elif message["type"] == "status":
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data = message["data"]
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if data["status"]["exec_info"]["queue_remaining"] == 0 and data.get("sid"):
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break # Execution is done
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else:
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continue # previews are binary data
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# download image when execution finished
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history = self.get_history(base_url, prompt_id)[prompt_id]
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for o in history["outputs"]:
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for node_id in history["outputs"]:
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node_output = history["outputs"][node_id]
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if "images" in node_output:
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images_output = []
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for image in node_output["images"]:
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image_data = self.download_image(base_url, image["filename"], image["subfolder"], image["type"])
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images_output.append(image_data)
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output_images[node_id] = images_output
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ws.close()
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return output_images
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def text2img(
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self,
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base_url: str,
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model: str,
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model_type: str,
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prompt: str,
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negative_prompt: str,
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width: int,
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height: int,
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steps: int,
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sampler_name: str,
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scheduler: str,
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cfg: float,
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lora_list: list,
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lora_strength_list: list,
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) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
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"""
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generate image
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"""
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if not SD_TXT2IMG_OPTIONS:
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current_dir = os.path.dirname(os.path.realpath(__file__))
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with open(os.path.join(current_dir, "txt2img.json")) as file:
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SD_TXT2IMG_OPTIONS.update(json.load(file))
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draw_options = deepcopy(SD_TXT2IMG_OPTIONS)
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draw_options["3"]["inputs"]["steps"] = steps
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draw_options["3"]["inputs"]["sampler_name"] = sampler_name
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draw_options["3"]["inputs"]["scheduler"] = scheduler
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draw_options["3"]["inputs"]["cfg"] = cfg
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# generate different image when using same prompt next time
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draw_options["3"]["inputs"]["seed"] = random.randint(0, 100000000)
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draw_options["4"]["inputs"]["ckpt_name"] = model
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draw_options["5"]["inputs"]["width"] = width
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draw_options["5"]["inputs"]["height"] = height
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draw_options["6"]["inputs"]["text"] = prompt
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draw_options["7"]["inputs"]["text"] = negative_prompt
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# if the model is SD3 or FLUX series, the Latent class should be corresponding to SD3 Latent
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if model_type in (ModelType.SD3.name, ModelType.FLUX.name):
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draw_options["5"]["class_type"] = "EmptySD3LatentImage"
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if lora_list:
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# last Lora node link to KSampler node
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draw_options["3"]["inputs"]["model"][0] = "10"
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# last Lora node link to positive and negative Clip node
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draw_options["6"]["inputs"]["clip"][0] = "10"
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draw_options["7"]["inputs"]["clip"][0] = "10"
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# every Lora node link to next Lora node, and Checkpoints node link to first Lora node
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for i, (lora, strength) in enumerate(zip(lora_list, lora_strength_list), 10):
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if i - 10 == len(lora_list) - 1:
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next_node_id = "4"
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else:
|
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next_node_id = str(i + 1)
|
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lora_node = deepcopy(LORA_NODE)
|
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lora_node["inputs"]["lora_name"] = lora
|
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lora_node["inputs"]["strength_model"] = strength
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lora_node["inputs"]["strength_clip"] = strength
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lora_node["inputs"]["model"][0] = next_node_id
|
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lora_node["inputs"]["clip"][0] = next_node_id
|
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draw_options[str(i)] = lora_node
|
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|
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# FLUX need to add FluxGuidance Node
|
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if model_type == ModelType.FLUX.name:
|
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last_node_id = str(10 + len(lora_list))
|
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draw_options[last_node_id] = deepcopy(FluxGuidanceNode)
|
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draw_options[last_node_id]["inputs"]["conditioning"][0] = "6"
|
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draw_options["3"]["inputs"]["positive"][0] = last_node_id
|
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|
||||
try:
|
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client_id = str(uuid.uuid4())
|
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result = self.queue_prompt_image(base_url, client_id, prompt=draw_options)
|
||||
|
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# get first image
|
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image = b""
|
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for node in result:
|
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for img in result[node]:
|
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if img:
|
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image = img
|
||||
break
|
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|
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return self.create_blob_message(
|
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blob=image, meta={"mime_type": "image/png"}, save_as=self.VARIABLE_KEY.IMAGE.value
|
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)
|
||||
|
||||
except Exception as e:
|
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return self.create_text_message(f"Failed to generate image: {str(e)}")
|
||||
|
||||
def get_runtime_parameters(self) -> list[ToolParameter]:
|
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parameters = [
|
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ToolParameter(
|
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name="prompt",
|
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label=I18nObject(en_US="Prompt", zh_Hans="Prompt"),
|
||||
human_description=I18nObject(
|
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en_US="Image prompt, you can check the official documentation of Stable Diffusion",
|
||||
zh_Hans="图像提示词，您可以查看 Stable Diffusion 的官方文档",
|
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),
|
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type=ToolParameter.ToolParameterType.STRING,
|
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form=ToolParameter.ToolParameterForm.LLM,
|
||||
llm_description="Image prompt of Stable Diffusion, you should describe the image "
|
||||
"you want to generate as a list of words as possible as detailed, "
|
||||
"the prompt must be written in English.",
|
||||
required=True,
|
||||
),
|
||||
]
|
||||
if self.runtime.credentials:
|
||||
try:
|
||||
models = self.get_checkpoints()
|
||||
if len(models) != 0:
|
||||
parameters.append(
|
||||
ToolParameter(
|
||||
name="model",
|
||||
label=I18nObject(en_US="Model", zh_Hans="Model"),
|
||||
human_description=I18nObject(
|
||||
en_US="Model of Stable Diffusion or FLUX, "
|
||||
"you can check the official documentation of Stable Diffusion or FLUX",
|
||||
zh_Hans="Stable Diffusion 或者 FLUX 的模型，您可以查看 Stable Diffusion 的官方文档",
|
||||
),
|
||||
type=ToolParameter.ToolParameterType.SELECT,
|
||||
form=ToolParameter.ToolParameterForm.FORM,
|
||||
llm_description="Model of Stable Diffusion or FLUX, "
|
||||
"you can check the official documentation of Stable Diffusion or FLUX",
|
||||
required=True,
|
||||
default=models[0],
|
||||
options=[
|
||||
ToolParameterOption(value=i, label=I18nObject(en_US=i, zh_Hans=i)) for i in models
|
||||
],
|
||||
)
|
||||
)
|
||||
loras = self.get_loras()
|
||||
if len(loras) != 0:
|
||||
for n in range(1, 4):
|
||||
parameters.append(
|
||||
ToolParameter(
|
||||
name=f"lora_{n}",
|
||||
label=I18nObject(en_US=f"Lora {n}", zh_Hans=f"Lora {n}"),
|
||||
human_description=I18nObject(
|
||||
en_US="Lora of Stable Diffusion, "
|
||||
"you can check the official documentation of Stable Diffusion",
|
||||
zh_Hans="Stable Diffusion 的 Lora 模型，您可以查看 Stable Diffusion 的官方文档",
|
||||
),
|
||||
type=ToolParameter.ToolParameterType.SELECT,
|
||||
form=ToolParameter.ToolParameterForm.FORM,
|
||||
llm_description="Lora of Stable Diffusion, "
|
||||
"you can check the official documentation of "
|
||||
"Stable Diffusion",
|
||||
required=False,
|
||||
options=[
|
||||
ToolParameterOption(value=i, label=I18nObject(en_US=i, zh_Hans=i)) for i in loras
|
||||
],
|
||||
)
|
||||
)
|
||||
sample_methods, schedulers = self.get_sample_methods()
|
||||
if len(sample_methods) != 0:
|
||||
parameters.append(
|
||||
ToolParameter(
|
||||
name="sampler_name",
|
||||
label=I18nObject(en_US="Sampling method", zh_Hans="Sampling method"),
|
||||
human_description=I18nObject(
|
||||
en_US="Sampling method of Stable Diffusion, "
|
||||
"you can check the official documentation of Stable Diffusion",
|
||||
zh_Hans="Stable Diffusion 的Sampling method，您可以查看 Stable Diffusion 的官方文档",
|
||||
),
|
||||
type=ToolParameter.ToolParameterType.SELECT,
|
||||
form=ToolParameter.ToolParameterForm.FORM,
|
||||
llm_description="Sampling method of Stable Diffusion, "
|
||||
"you can check the official documentation of Stable Diffusion",
|
||||
required=True,
|
||||
default=sample_methods[0],
|
||||
options=[
|
||||
ToolParameterOption(value=i, label=I18nObject(en_US=i, zh_Hans=i))
|
||||
for i in sample_methods
|
||||
],
|
||||
)
|
||||
)
|
||||
if len(schedulers) != 0:
|
||||
parameters.append(
|
||||
ToolParameter(
|
||||
name="scheduler",
|
||||
label=I18nObject(en_US="Scheduler", zh_Hans="Scheduler"),
|
||||
human_description=I18nObject(
|
||||
en_US="Scheduler of Stable Diffusion, "
|
||||
"you can check the official documentation of Stable Diffusion",
|
||||
zh_Hans="Stable Diffusion 的Scheduler，您可以查看 Stable Diffusion 的官方文档",
|
||||
),
|
||||
type=ToolParameter.ToolParameterType.SELECT,
|
||||
form=ToolParameter.ToolParameterForm.FORM,
|
||||
llm_description="Scheduler of Stable Diffusion, "
|
||||
"you can check the official documentation of Stable Diffusion",
|
||||
required=True,
|
||||
default=schedulers[0],
|
||||
options=[
|
||||
ToolParameterOption(value=i, label=I18nObject(en_US=i, zh_Hans=i)) for i in schedulers
|
||||
],
|
||||
)
|
||||
)
|
||||
parameters.append(
|
||||
ToolParameter(
|
||||
name="model_type",
|
||||
label=I18nObject(en_US="Model Type", zh_Hans="Model Type"),
|
||||
human_description=I18nObject(
|
||||
en_US="Model Type of Stable Diffusion or Flux, "
|
||||
"you can check the official documentation of Stable Diffusion or Flux",
|
||||
zh_Hans="Stable Diffusion 或 FLUX 的模型类型，"
|
||||
"您可以查看 Stable Diffusion 或 Flux 的官方文档",
|
||||
),
|
||||
type=ToolParameter.ToolParameterType.SELECT,
|
||||
form=ToolParameter.ToolParameterForm.FORM,
|
||||
llm_description="Model Type of Stable Diffusion or Flux, "
|
||||
"you can check the official documentation of Stable Diffusion or Flux",
|
||||
required=True,
|
||||
default=ModelType.SD15.name,
|
||||
options=[
|
||||
ToolParameterOption(value=i, label=I18nObject(en_US=i, zh_Hans=i))
|
||||
for i in ModelType.__members__
|
||||
],
|
||||
)
|
||||
)
|
||||
except:
|
||||
pass
|
||||
|
||||
return parameters
|
||||
@ -0,0 +1,212 @@
|
||||
identity:
|
||||
name: txt2img workflow
|
||||
author: Qun
|
||||
label:
|
||||
en_US: Txt2Img Workflow
|
||||
zh_Hans: Txt2Img Workflow
|
||||
pt_BR: Txt2Img Workflow
|
||||
description:
|
||||
human:
|
||||
en_US: a pre-defined comfyui workflow that can use one model and up to 3 loras to generate images. Support SD1.5, SDXL, SD3 and FLUX which contain text encoders/clip, but does not support models that requires a triple clip loader.
|
||||
zh_Hans: 一个预定义的 ComfyUI 工作流，可以使用一个模型和最多3个loras来生成图像。支持包含文本编码器/clip的SD1.5、SDXL、SD3和FLUX，但不支持需要clip加载器的模型。
|
||||
pt_BR: a pre-defined comfyui workflow that can use one model and up to 3 loras to generate images. Support SD1.5, SDXL, SD3 and FLUX which contain text encoders/clip, but does not support models that requires a triple clip loader.
|
||||
llm: draw the image you want based on your prompt.
|
||||
parameters:
|
||||
- name: prompt
|
||||
type: string
|
||||
required: true
|
||||
label:
|
||||
en_US: Prompt
|
||||
zh_Hans: 提示词
|
||||
pt_BR: Prompt
|
||||
human_description:
|
||||
en_US: Image prompt, you can check the official documentation of Stable Diffusion or FLUX
|
||||
zh_Hans: 图像提示词，您可以查看 Stable Diffusion 或者 FLUX 的官方文档
|
||||
pt_BR: Image prompt, you can check the official documentation of Stable Diffusion or FLUX
|
||||
llm_description: Image prompt of Stable Diffusion, you should describe the image you want to generate as a list of words as possible as detailed, the prompt must be written in English.
|
||||
form: llm
|
||||
- name: model
|
||||
type: string
|
||||
required: true
|
||||
label:
|
||||
en_US: Model Name
|
||||
zh_Hans: 模型名称
|
||||
pt_BR: Model Name
|
||||
human_description:
|
||||
en_US: Model Name
|
||||
zh_Hans: 模型名称
|
||||
pt_BR: Model Name
|
||||
form: form
|
||||
- name: model_type
|
||||
type: string
|
||||
required: true
|
||||
label:
|
||||
en_US: Model Type
|
||||
zh_Hans: 模型类型
|
||||
pt_BR: Model Type
|
||||
human_description:
|
||||
en_US: Model Type
|
||||
zh_Hans: 模型类型
|
||||
pt_BR: Model Type
|
||||
form: form
|
||||
- name: lora_1
|
||||
type: string
|
||||
required: false
|
||||
label:
|
||||
en_US: Lora 1
|
||||
zh_Hans: Lora 1
|
||||
pt_BR: Lora 1
|
||||
human_description:
|
||||
en_US: Lora 1
|
||||
zh_Hans: Lora 1
|
||||
pt_BR: Lora 1
|
||||
form: form
|
||||
- name: lora_strength_1
|
||||
type: number
|
||||
required: false
|
||||
label:
|
||||
en_US: Lora Strength 1
|
||||
zh_Hans: Lora Strength 1
|
||||
pt_BR: Lora Strength 1
|
||||
human_description:
|
||||
en_US: Lora Strength 1
|
||||
zh_Hans: Lora模型的权重
|
||||
pt_BR: Lora Strength 1
|
||||
form: form
|
||||
- name: steps
|
||||
type: number
|
||||
required: false
|
||||
label:
|
||||
en_US: Steps
|
||||
zh_Hans: Steps
|
||||
pt_BR: Steps
|
||||
human_description:
|
||||
en_US: Steps
|
||||
zh_Hans: Steps
|
||||
pt_BR: Steps
|
||||
form: form
|
||||
default: 20
|
||||
- name: width
|
||||
type: number
|
||||
required: false
|
||||
label:
|
||||
en_US: Width
|
||||
zh_Hans: Width
|
||||
pt_BR: Width
|
||||
human_description:
|
||||
en_US: Width
|
||||
zh_Hans: Width
|
||||
pt_BR: Width
|
||||
form: form
|
||||
default: 1024
|
||||
- name: height
|
||||
type: number
|
||||
required: false
|
||||
label:
|
||||
en_US: Height
|
||||
zh_Hans: Height
|
||||
pt_BR: Height
|
||||
human_description:
|
||||
en_US: Height
|
||||
zh_Hans: Height
|
||||
pt_BR: Height
|
||||
form: form
|
||||
default: 1024
|
||||
- name: negative_prompt
|
||||
type: string
|
||||
required: false
|
||||
label:
|
||||
en_US: Negative prompt
|
||||
zh_Hans: Negative prompt
|
||||
pt_BR: Negative prompt
|
||||
human_description:
|
||||
en_US: Negative prompt
|
||||
zh_Hans: Negative prompt
|
||||
pt_BR: Negative prompt
|
||||
form: form
|
||||
default: bad art, ugly, deformed, watermark, duplicated, discontinuous lines
|
||||
- name: cfg
|
||||
type: number
|
||||
required: false
|
||||
label:
|
||||
en_US: CFG Scale
|
||||
zh_Hans: CFG Scale
|
||||
pt_BR: CFG Scale
|
||||
human_description:
|
||||
en_US: CFG Scale
|
||||
zh_Hans: 提示词相关性(CFG Scale)
|
||||
pt_BR: CFG Scale
|
||||
form: form
|
||||
default: 7.0
|
||||
- name: sampler_name
|
||||
type: string
|
||||
required: false
|
||||
label:
|
||||
en_US: Sampling method
|
||||
zh_Hans: Sampling method
|
||||
pt_BR: Sampling method
|
||||
human_description:
|
||||
en_US: Sampling method
|
||||
zh_Hans: Sampling method
|
||||
pt_BR: Sampling method
|
||||
form: form
|
||||
- name: scheduler
|
||||
type: string
|
||||
required: false
|
||||
label:
|
||||
en_US: Scheduler
|
||||
zh_Hans: Scheduler
|
||||
pt_BR: Scheduler
|
||||
human_description:
|
||||
en_US: Scheduler
|
||||
zh_Hans: Scheduler
|
||||
pt_BR: Scheduler
|
||||
form: form
|
||||
- name: lora_2
|
||||
type: string
|
||||
required: false
|
||||
label:
|
||||
en_US: Lora 2
|
||||
zh_Hans: Lora 2
|
||||
pt_BR: Lora 2
|
||||
human_description:
|
||||
en_US: Lora 2
|
||||
zh_Hans: Lora 2
|
||||
pt_BR: Lora 2
|
||||
form: form
|
||||
- name: lora_strength_2
|
||||
type: number
|
||||
required: false
|
||||
label:
|
||||
en_US: Lora Strength 2
|
||||
zh_Hans: Lora Strength 2
|
||||
pt_BR: Lora Strength 2
|
||||
human_description:
|
||||
en_US: Lora Strength 2
|
||||
zh_Hans: Lora模型的权重
|
||||
pt_BR: Lora Strength 2
|
||||
form: form
|
||||
- name: lora_3
|
||||
type: string
|
||||
required: false
|
||||
label:
|
||||
en_US: Lora 3
|
||||
zh_Hans: Lora 3
|
||||
pt_BR: Lora 3
|
||||
human_description:
|
||||
en_US: Lora 3
|
||||
zh_Hans: Lora 3
|
||||
pt_BR: Lora 3
|
||||
form: form
|
||||
- name: lora_strength_3
|
||||
type: number
|
||||
required: false
|
||||
label:
|
||||
en_US: Lora Strength 3
|
||||
zh_Hans: Lora Strength 3
|
||||
pt_BR: Lora Strength 3
|
||||
human_description:
|
||||
en_US: Lora Strength 3
|
||||
zh_Hans: Lora模型的权重
|
||||
pt_BR: Lora Strength 3
|
||||
form: form
|
||||
107
api/core/tools/provider/builtin/comfyui/tools/txt2img.json
Normal file
107
api/core/tools/provider/builtin/comfyui/tools/txt2img.json
Normal file
@ -0,0 +1,107 @@
|
||||
{
|
||||
"3": {
|
||||
"inputs": {
|
||||
"seed": 156680208700286,
|
||||
"steps": 20,
|
||||
"cfg": 8,
|
||||
"sampler_name": "euler",
|
||||
"scheduler": "normal",
|
||||
"denoise": 1,
|
||||
"model": [
|
||||
"4",
|
||||
0
|
||||
],
|
||||
"positive": [
|
||||
"6",
|
||||
0
|
||||
],
|
||||
"negative": [
|
||||
"7",
|
||||
0
|
||||
],
|
||||
"latent_image": [
|
||||
"5",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "KSampler",
|
||||
"_meta": {
|
||||
"title": "KSampler"
|
||||
}
|
||||
},
|
||||
"4": {
|
||||
"inputs": {
|
||||
"ckpt_name": "3dAnimationDiffusion_v10.safetensors"
|
||||
},
|
||||
"class_type": "CheckpointLoaderSimple",
|
||||
"_meta": {
|
||||
"title": "Load Checkpoint"
|
||||
}
|
||||
},
|
||||
"5": {
|
||||
"inputs": {
|
||||
"width": 512,
|
||||
"height": 512,
|
||||
"batch_size": 1
|
||||
},
|
||||
"class_type": "EmptyLatentImage",
|
||||
"_meta": {
|
||||
"title": "Empty Latent Image"
|
||||
}
|
||||
},
|
||||
"6": {
|
||||
"inputs": {
|
||||
"text": "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,",
|
||||
"clip": [
|
||||
"4",
|
||||
1
|
||||
]
|
||||
},
|
||||
"class_type": "CLIPTextEncode",
|
||||
"_meta": {
|
||||
"title": "CLIP Text Encode (Prompt)"
|
||||
}
|
||||
},
|
||||
"7": {
|
||||
"inputs": {
|
||||
"text": "text, watermark",
|
||||
"clip": [
|
||||
"4",
|
||||
1
|
||||
]
|
||||
},
|
||||
"class_type": "CLIPTextEncode",
|
||||
"_meta": {
|
||||
"title": "CLIP Text Encode (Prompt)"
|
||||
}
|
||||
},
|
||||
"8": {
|
||||
"inputs": {
|
||||
"samples": [
|
||||
"3",
|
||||
0
|
||||
],
|
||||
"vae": [
|
||||
"4",
|
||||
2
|
||||
]
|
||||
},
|
||||
"class_type": "VAEDecode",
|
||||
"_meta": {
|
||||
"title": "VAE Decode"
|
||||
}
|
||||
},
|
||||
"9": {
|
||||
"inputs": {
|
||||
"filename_prefix": "ComfyUI",
|
||||
"images": [
|
||||
"8",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "SaveImage",
|
||||
"_meta": {
|
||||
"title": "Save Image"
|
||||
}
|
||||
}
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user