6.1 KiB
6.1 KiB
Documentation
- Class name: PromptEmbellish
- Category: 😺dzNodes/LayerUtility/Prompt
- Output node: False
- Repo Ref: https://github.com/chflame163/ComfyUI_LayerStyle
输入简单的提示词,输出经过润色的提示词,支持输入图片作为参考。这个节点目前使用Google Gemini API作为后端服务,请确保网络环境可以正常使用Gemini。 请在Google AI Studio申请你的API key, 并将其填到api_key.ini, 这个文件位于插件根目录下, 默认名字是api_key.ini.example, 初次使用这个文件需将文件后缀改为.ini。用文本编辑软件打开,在google_api_key=后面填入你的API key并保存。
Input types
Required
-
api
- API列表。
- Comfy dtype: ['google-gemini']
- Python dtype: str
-
token_limit
- 令牌限制。
- Comfy dtype: INT
- Python dtype: int
-
describe
- 描述。
- Comfy dtype: STRING
- Python dtype: str
Optional
- image
- 图片。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
Output types
- text
- 文本。
- Comfy dtype: STRING
- Python dtype: str
Usage tips
- Infra type: CPU
Source code
class PromptEmbellish:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(self):
api_list = ['google-gemini']
return {
"required": {
"api": (api_list,),
"token_limit": ("INT", {"default": 40, "min": 2, "max": 1024, "step": 1}),
"describe": ("STRING", {"default": "", "multiline": True}),
},
"optional": {
"image": ("IMAGE",),
}
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("text",)
FUNCTION = 'prompt_embellish'
CATEGORY = '😺dzNodes/LayerUtility/Prompt'
def prompt_embellish(self, api, token_limit, describe, image=None):
if describe == "" and image is None:
return ("",)
import google.generativeai as genai
ret_text = ""
first_step_prompt = (f"You are creating a prompt for Stable Diffusion to generate an image. "
f"First step:Using '{describe}' as the basic content, "
f"polish and embellish it to describe into text, keep it on {token_limit} tokens."
f"Second step: Generate a Stable Diffusion text prompt for based on first step in at least {token_limit} words."
f"Only respond with the prompt itself, but embellish it."
)
genai.configure(api_key=get_api_key('google_api_key'), transport='rest')
if describe != "":
model = genai.GenerativeModel('gemini-pro',
generation_config=gemini_generate_config,
safety_settings=gemini_safety_settings)
log(f"{NODE_NAME}: Request to gemini-pro...")
response = model.generate_content(first_step_prompt)
print(response)
ret_text = response.text
ret_text = ret_text[ret_text.rfind(':') + 1:]
ret_text = ret_text[ret_text.rfind('\n') + 1:]
# log(f"{NODE_NAME}: Text2Image Prompt is:\n\033[1;36m{ret_text}\033[m")
if is_contain_chinese(describe):
translate_prompt = (f"Please translate the text in parentheses into English:({describe})"
)
response = model.generate_content(translate_prompt)
print(response)
ret_discribe = response.text
else:
ret_discribe = describe
if image is not None:
if describe != "":
second_step_prompt = (f"You are creating a prompt for Stable Diffusion to generate an image. "
f"First step:Modify and polish the content in parentheses to match this photo,"
f"but must keep '{describe}': ({ret_text}) "
f"Second step: Find objects that is similar in parentheses from the content of the first step"
f" and replace it with the content in parentheses: ({describe})"
f"Third step: Generate a Stable Diffusion text prompt for based on second step in at least {token_limit} words."
f"Only respond with the prompt itself, but embellish it."
)
else:
second_step_prompt = (f"You are creating a prompt for Stable Diffusion to generate an image. "
f"First step: describe this image, "
f"polish and embellish it into text, discrete it in {token_limit} tokens."
f"Second step: Generate a Stable Diffusion text prompt for based on first step in at least {token_limit} words."
f"Only respond with the prompt itself, but embellish it."
)
_image = tensor2pil(image).convert('RGB')
model = genai.GenerativeModel('gemini-pro-vision',
generation_config=gemini_generate_config,
safety_settings=gemini_safety_settings)
log(f"{NODE_NAME}: Request to gemini-pro-vision...")
response = model.generate_content([second_step_prompt, _image])
print(response)
ret_text = response.text
ret_text = ret_text[ret_text.rfind(':') + 1:]
ret_text = ret_text.replace('(','').replace(')','')
if describe != "":
ret_text = f"((({ret_discribe}))), {ret_text}"
# log(f"{NODE_NAME}: Text2Image by ImageRefrence Prompt is:\n\033[1;36m{ret_text}\033[m")
log(f"{NODE_NAME}: Prompt is:\n\033[1;36m{ret_text}\033[m")
log(f"{NODE_NAME} Processed.", message_type='finish')
return (ret_text,)