diff --git a/api/core/tools/provider/builtin/comfyui/_assets/icon.png b/api/core/tools/provider/builtin/comfyui/_assets/icon.png new file mode 100644 index 00000000000000..958ec5c5cfe296 Binary files /dev/null and b/api/core/tools/provider/builtin/comfyui/_assets/icon.png differ diff --git a/api/core/tools/provider/builtin/comfyui/comfyui.py b/api/core/tools/provider/builtin/comfyui/comfyui.py new file mode 100644 index 00000000000000..7013a0b93c8dcb --- /dev/null +++ b/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)) diff --git a/api/core/tools/provider/builtin/comfyui/comfyui.yaml b/api/core/tools/provider/builtin/comfyui/comfyui.yaml new file mode 100644 index 00000000000000..066fd853082817 --- /dev/null +++ b/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 diff --git a/api/core/tools/provider/builtin/comfyui/tools/comfyui_stable_diffusion.py b/api/core/tools/provider/builtin/comfyui/tools/comfyui_stable_diffusion.py new file mode 100644 index 00000000000000..b9b52c0b4d4a81 --- /dev/null +++ b/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 diff --git a/api/core/tools/provider/builtin/comfyui/tools/comfyui_stable_diffusion.yaml b/api/core/tools/provider/builtin/comfyui/tools/comfyui_stable_diffusion.yaml new file mode 100644 index 00000000000000..4f4a6942b3bac4 --- /dev/null +++ b/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 diff --git a/api/core/tools/provider/builtin/comfyui/tools/txt2img.json b/api/core/tools/provider/builtin/comfyui/tools/txt2img.json new file mode 100644 index 00000000000000..8ea869ff106c38 --- /dev/null +++ b/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" + } + } +} \ No newline at end of file