feat: Add ComfyUI tool for Stable Diffusion (#8160)

api/core/tools/provider/builtin/comfyui/comfyui.py

@@ -0,0 +1,17 @@
+from typing import Any
+
+from core.tools.errors import ToolProviderCredentialValidationError
+from core.tools.provider.builtin.comfyui.tools.comfyui_stable_diffusion import ComfyuiStableDiffusionTool
+from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
+
+
+class ComfyUIProvider(BuiltinToolProviderController):
+    def _validate_credentials(self, credentials: dict[str, Any]) -> None:
+        try:
+            ComfyuiStableDiffusionTool().fork_tool_runtime(
+                runtime={
+                    "credentials": credentials,
+                }
+            ).validate_models()
+        except Exception as e:
+            raise ToolProviderCredentialValidationError(str(e))

api/core/tools/provider/builtin/comfyui/comfyui.yaml

@@ -0,0 +1,42 @@
+identity:
+  author: Qun
+  name: comfyui
+  label:
+    en_US: ComfyUI
+    zh_Hans: ComfyUI
+    pt_BR: ComfyUI
+  description:
+    en_US: ComfyUI is a tool for generating images which can be deployed locally.
+    zh_Hans: ComfyUI 是一个可以在本地部署的图片生成的工具。
+    pt_BR: ComfyUI is a tool for generating images which can be deployed locally.
+  icon: icon.png
+  tags:
+    - image
+credentials_for_provider:
+  base_url:
+    type: text-input
+    required: true
+    label:
+      en_US: Base URL
+      zh_Hans: ComfyUI服务器的Base URL
+      pt_BR: Base URL
+    placeholder:
+      en_US: Please input your ComfyUI server's Base URL
+      zh_Hans: 请输入你的 ComfyUI 服务器的 Base URL
+      pt_BR: Please input your ComfyUI server's Base URL
+  model:
+    type: text-input
+    required: true
+    label:
+      en_US: Model with suffix
+      zh_Hans: 模型, 需要带后缀
+      pt_BR: Model with suffix
+    placeholder:
+      en_US: Please input your model
+      zh_Hans: 请输入你的模型名称
+      pt_BR: Please input your model
+    help:
+      en_US: The checkpoint name of the ComfyUI server, e.g. xxx.safetensors
+      zh_Hans: ComfyUI服务器的模型名称, 比如 xxx.safetensors
+      pt_BR: The checkpoint name of the ComfyUI server, e.g. xxx.safetensors
+    url: https://docs.dify.ai/tutorials/tool-configuration/comfyui

api/core/tools/provider/builtin/comfyui/tools/comfyui_stable_diffusion.py

@@ -0,0 +1,475 @@
+import json
+import os
+import random
+import uuid
+from copy import deepcopy
+from enum import Enum
+from typing import Any, Union
+
+import websocket
+from httpx import get, post
+from yarl import URL
+
+from core.tools.entities.common_entities import I18nObject
+from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParameter, ToolParameterOption
+from core.tools.errors import ToolProviderCredentialValidationError
+from core.tools.tool.builtin_tool import BuiltinTool
+
+SD_TXT2IMG_OPTIONS = {}
+LORA_NODE = {
+    "inputs": {"lora_name": "", "strength_model": 1, "strength_clip": 1, "model": ["11", 0], "clip": ["11", 1]},
+    "class_type": "LoraLoader",
+    "_meta": {"title": "Load LoRA"},
+}
+FluxGuidanceNode = {
+    "inputs": {"guidance": 3.5, "conditioning": ["6", 0]},
+    "class_type": "FluxGuidance",
+    "_meta": {"title": "FluxGuidance"},
+}
+
+
+class ModelType(Enum):
+    SD15 = 1
+    SDXL = 2
+    SD3 = 3
+    FLUX = 4
+
+
+class ComfyuiStableDiffusionTool(BuiltinTool):
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        """
+        invoke tools
+        """
+        # base url
+        base_url = self.runtime.credentials.get("base_url", "")
+        if not base_url:
+            return self.create_text_message("Please input base_url")
+
+        if tool_parameters.get("model"):
+            self.runtime.credentials["model"] = tool_parameters["model"]
+
+        model = self.runtime.credentials.get("model", None)
+        if not model:
+            return self.create_text_message("Please input model")
+
+        # prompt
+        prompt = tool_parameters.get("prompt", "")
+        if not prompt:
+            return self.create_text_message("Please input prompt")
+
+        # get negative prompt
+        negative_prompt = tool_parameters.get("negative_prompt", "")
+
+        # get size
+        width = tool_parameters.get("width", 1024)
+        height = tool_parameters.get("height", 1024)
+
+        # get steps
+        steps = tool_parameters.get("steps", 1)
+
+        # get sampler_name
+        sampler_name = tool_parameters.get("sampler_name", "euler")
+
+        # scheduler
+        scheduler = tool_parameters.get("scheduler", "normal")
+
+        # get cfg
+        cfg = tool_parameters.get("cfg", 7.0)
+
+        # get model type
+        model_type = tool_parameters.get("model_type", ModelType.SD15.name)
+
+        # get lora
+        # supports up to 3 loras
+        lora_list = []
+        lora_strength_list = []
+        if tool_parameters.get("lora_1"):
+            lora_list.append(tool_parameters["lora_1"])
+            lora_strength_list.append(tool_parameters.get("lora_strength_1", 1))
+        if tool_parameters.get("lora_2"):
+            lora_list.append(tool_parameters["lora_2"])
+            lora_strength_list.append(tool_parameters.get("lora_strength_2", 1))
+        if tool_parameters.get("lora_3"):
+            lora_list.append(tool_parameters["lora_3"])
+            lora_strength_list.append(tool_parameters.get("lora_strength_3", 1))
+
+        return self.text2img(
+            base_url=base_url,
+            model=model,
+            model_type=model_type,
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            width=width,
+            height=height,
+            steps=steps,
+            sampler_name=sampler_name,
+            scheduler=scheduler,
+            cfg=cfg,
+            lora_list=lora_list,
+            lora_strength_list=lora_strength_list,
+        )
+
+    def get_checkpoints(self) -> list[str]:
+        """
+        get checkpoints
+        """
+        try:
+            base_url = self.runtime.credentials.get("base_url", None)
+            if not base_url:
+                return []
+            api_url = str(URL(base_url) / "models" / "checkpoints")
+            response = get(url=api_url, timeout=(2, 10))
+            if response.status_code != 200:
+                return []
+            else:
+                return response.json()
+        except Exception as e:
+            return []
+
+    def get_loras(self) -> list[str]:
+        """
+        get loras
+        """
+        try:
+            base_url = self.runtime.credentials.get("base_url", None)
+            if not base_url:
+                return []
+            api_url = str(URL(base_url) / "models" / "loras")
+            response = get(url=api_url, timeout=(2, 10))
+            if response.status_code != 200:
+                return []
+            else:
+                return response.json()
+        except Exception as e:
+            return []
+
+    def get_sample_methods(self) -> tuple[list[str], list[str]]:
+        """
+        get sample method
+        """
+        try:
+            base_url = self.runtime.credentials.get("base_url", None)
+            if not base_url:
+                return [], []
+            api_url = str(URL(base_url) / "object_info" / "KSampler")
+            response = get(url=api_url, timeout=(2, 10))
+            if response.status_code != 200:
+                return [], []
+            else:
+                data = response.json()["KSampler"]["input"]["required"]
+                return data["sampler_name"][0], data["scheduler"][0]
+        except Exception as e:
+            return [], []
+
+    def validate_models(self) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        """
+        validate models
+        """
+        try:
+            base_url = self.runtime.credentials.get("base_url", None)
+            if not base_url:
+                raise ToolProviderCredentialValidationError("Please input base_url")
+            model = self.runtime.credentials.get("model", None)
+            if not model:
+                raise ToolProviderCredentialValidationError("Please input model")
+
+            api_url = str(URL(base_url) / "models" / "checkpoints")
+            response = get(url=api_url, timeout=(2, 10))
+            if response.status_code != 200:
+                raise ToolProviderCredentialValidationError("Failed to get models")
+            else:
+                models = response.json()
+                if len([d for d in models if d == model]) > 0:
+                    return self.create_text_message(json.dumps(models))
+                else:
+                    raise ToolProviderCredentialValidationError(f"model {model} does not exist")
+        except Exception as e:
+            raise ToolProviderCredentialValidationError(f"Failed to get models, {e}")
+
+    def get_history(self, base_url, prompt_id):
+        """
+        get history
+        """
+        url = str(URL(base_url) / "history")
+        respond = get(url, params={"prompt_id": prompt_id}, timeout=(2, 10))
+        return respond.json()
+
+    def download_image(self, base_url, filename, subfolder, folder_type):
+        """
+        download image
+        """
+        url = str(URL(base_url) / "view")
+        response = get(url, params={"filename": filename, "subfolder": subfolder, "type": folder_type}, timeout=(2, 10))
+        return response.content
+
+    def queue_prompt_image(self, base_url, client_id, prompt):
+        """
+        send prompt task and rotate
+        """
+        # initiate task execution
+        url = str(URL(base_url) / "prompt")
+        respond = post(url, data=json.dumps({"client_id": client_id, "prompt": prompt}), timeout=(2, 10))
+        prompt_id = respond.json()["prompt_id"]
+
+        ws = websocket.WebSocket()
+        if "https" in base_url:
+            ws_url = base_url.replace("https", "ws")
+        else:
+            ws_url = base_url.replace("http", "ws")
+        ws.connect(str(URL(f"{ws_url}") / "ws") + f"?clientId={client_id}", timeout=120)
+
+        # websocket rotate execution status
+        output_images = {}
+        while True:
+            out = ws.recv()
+            if isinstance(out, str):
+                message = json.loads(out)
+                if message["type"] == "executing":
+                    data = message["data"]
+                    if data["node"] is None and data["prompt_id"] == prompt_id:
+                        break  # Execution is done
+                elif message["type"] == "status":
+                    data = message["data"]
+                    if data["status"]["exec_info"]["queue_remaining"] == 0 and data.get("sid"):
+                        break  # Execution is done
+            else:
+                continue  # previews are binary data
+
+        # download image when execution finished
+        history = self.get_history(base_url, prompt_id)[prompt_id]
+        for o in history["outputs"]:
+            for node_id in history["outputs"]:
+                node_output = history["outputs"][node_id]
+                if "images" in node_output:
+                    images_output = []
+                    for image in node_output["images"]:
+                        image_data = self.download_image(base_url, image["filename"], image["subfolder"], image["type"])
+                        images_output.append(image_data)
+                    output_images[node_id] = images_output
+
+        ws.close()
+
+        return output_images
+
+    def text2img(
+        self,
+        base_url: str,
+        model: str,
+        model_type: str,
+        prompt: str,
+        negative_prompt: str,
+        width: int,
+        height: int,
+        steps: int,
+        sampler_name: str,
+        scheduler: str,
+        cfg: float,
+        lora_list: list,
+        lora_strength_list: list,
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        """
+        generate image
+        """
+        if not SD_TXT2IMG_OPTIONS:
+            current_dir = os.path.dirname(os.path.realpath(__file__))
+            with open(os.path.join(current_dir, "txt2img.json")) as file:
+                SD_TXT2IMG_OPTIONS.update(json.load(file))
+
+        draw_options = deepcopy(SD_TXT2IMG_OPTIONS)
+        draw_options["3"]["inputs"]["steps"] = steps
+        draw_options["3"]["inputs"]["sampler_name"] = sampler_name
+        draw_options["3"]["inputs"]["scheduler"] = scheduler
+        draw_options["3"]["inputs"]["cfg"] = cfg
+        # generate different image when using same prompt next time
+        draw_options["3"]["inputs"]["seed"] = random.randint(0, 100000000)
+        draw_options["4"]["inputs"]["ckpt_name"] = model
+        draw_options["5"]["inputs"]["width"] = width
+        draw_options["5"]["inputs"]["height"] = height
+        draw_options["6"]["inputs"]["text"] = prompt
+        draw_options["7"]["inputs"]["text"] = negative_prompt
+        # if the model is SD3 or FLUX series, the Latent class should be corresponding to SD3 Latent
+        if model_type in (ModelType.SD3.name, ModelType.FLUX.name):
+            draw_options["5"]["class_type"] = "EmptySD3LatentImage"
+
+        if lora_list:
+            # last Lora node link to KSampler node
+            draw_options["3"]["inputs"]["model"][0] = "10"
+            # last Lora node link to positive and negative Clip node
+            draw_options["6"]["inputs"]["clip"][0] = "10"
+            draw_options["7"]["inputs"]["clip"][0] = "10"
+            # every Lora node link to next Lora node, and Checkpoints node link to first Lora node
+            for i, (lora, strength) in enumerate(zip(lora_list, lora_strength_list), 10):
+                if i - 10 == len(lora_list) - 1:
+                    next_node_id = "4"
+                else:
+                    next_node_id = str(i + 1)
+                lora_node = deepcopy(LORA_NODE)
+                lora_node["inputs"]["lora_name"] = lora
+                lora_node["inputs"]["strength_model"] = strength
+                lora_node["inputs"]["strength_clip"] = strength
+                lora_node["inputs"]["model"][0] = next_node_id
+                lora_node["inputs"]["clip"][0] = next_node_id
+                draw_options[str(i)] = lora_node
+
+        # FLUX need to add FluxGuidance Node
+        if model_type == ModelType.FLUX.name:
+            last_node_id = str(10 + len(lora_list))
+            draw_options[last_node_id] = deepcopy(FluxGuidanceNode)
+            draw_options[last_node_id]["inputs"]["conditioning"][0] = "6"
+            draw_options["3"]["inputs"]["positive"][0] = last_node_id
+
+        try:
+            client_id = str(uuid.uuid4())
+            result = self.queue_prompt_image(base_url, client_id, prompt=draw_options)
+
+            # get first image
+            image = b""
+            for node in result:
+                for img in result[node]:
+                    if img:
+                        image = img
+                        break
+
+            return self.create_blob_message(
+                blob=image, meta={"mime_type": "image/png"}, save_as=self.VARIABLE_KEY.IMAGE.value
+            )
+
+        except Exception as e:
+            return self.create_text_message(f"Failed to generate image: {str(e)}")
+
+    def get_runtime_parameters(self) -> list[ToolParameter]:
+        parameters = [
+            ToolParameter(
+                name="prompt",
+                label=I18nObject(en_US="Prompt", zh_Hans="Prompt"),
+                human_description=I18nObject(
+                    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",
+                ),
+                type=ToolParameter.ToolParameterType.STRING,
+                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

api/core/tools/provider/builtin/comfyui/tools/comfyui_stable_diffusion.yaml

@@ -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

api/core/tools/provider/builtin/comfyui/tools/txt2img.json

@@ -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"
+    }
+  }
+}