785 lines
26 KiB
Python
785 lines
26 KiB
Python
|
|
|
|
import json
|
|
import os
|
|
import sys
|
|
from .mz_prompt_utils import Utils
|
|
from nodes import MAX_RESOLUTION
|
|
import comfy.utils
|
|
import shutil
|
|
import comfy.samplers
|
|
import folder_paths
|
|
|
|
|
|
WEB_DIRECTORY = "./web"
|
|
|
|
AUTHOR_NAME = u"MinusZone"
|
|
CATEGORY_NAME = f"{AUTHOR_NAME} - Prompt"
|
|
|
|
# sys.path.append(os.path.join(os.path.dirname(__file__)))
|
|
|
|
import importlib
|
|
|
|
from . import mz_prompt_webserver
|
|
# mz_prompt_webserver.start_server()
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
}
|
|
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
}
|
|
|
|
|
|
from . import mz_llama_cpp
|
|
|
|
|
|
def getCommonCLIPTextEncodeInput():
|
|
from . import mz_llama_core_nodes
|
|
style_presets = mz_llama_core_nodes.get_style_presets()
|
|
CommonCLIPTextEncodeInput = {
|
|
"required": {
|
|
"style_presets": (
|
|
style_presets, {"default": style_presets[1]}
|
|
),
|
|
"text": ("STRING", {"multiline": True, }),
|
|
"keep_device": ([False, True], {"default": False}),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
},
|
|
"optional": {
|
|
"clip": ("CLIP", ),
|
|
"llama_cpp_options": ("LLamaCPPOptions", ),
|
|
"customize_instruct": ("CustomizeInstruct", ),
|
|
}
|
|
}
|
|
|
|
return CommonCLIPTextEncodeInput
|
|
|
|
|
|
class MZ_OllamaModelConfig_ManualSelect:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
search_dirs = [
|
|
os.path.join(os.path.expanduser('~'), ".ollama", "models"),
|
|
os.path.join(os.environ.get("APPDATA", ""), ".ollama", "models"),
|
|
]
|
|
|
|
ollama_models_dir = None
|
|
for dir in search_dirs:
|
|
if os.path.exists(dir):
|
|
ollama_models_dir = dir
|
|
break
|
|
|
|
ollamas = []
|
|
if ollama_models_dir is not None:
|
|
manifests_dir = os.path.join(ollama_models_dir, "manifests")
|
|
for root, dirs, files in os.walk(manifests_dir):
|
|
for file in files:
|
|
ollamas.append(os.path.join(root, file))
|
|
|
|
chat_format = mz_llama_cpp.get_llama_cpp_chat_handlers()
|
|
return {
|
|
"required": {
|
|
"ollama": (ollamas,),
|
|
"chat_format": (["auto"] + chat_format, {"default": "auto"}),
|
|
},
|
|
"optional": {
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("LLamaCPPModelConfig",)
|
|
RETURN_NAMES = ("llama_cpp_model_config",)
|
|
|
|
FUNCTION = "create"
|
|
CATEGORY = f"{CATEGORY_NAME}/others"
|
|
|
|
def create(self, **kwargs):
|
|
kwargs = kwargs.copy()
|
|
|
|
ollama = kwargs.get("ollama", "")
|
|
ollama_cpp_model = None
|
|
if os.path.exists(ollama):
|
|
# {"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}
|
|
with open(ollama, "r", encoding="utf-8") as f:
|
|
data = json.load(f)
|
|
if "layers" in data:
|
|
for layer in data["layers"]:
|
|
if "mediaType" in layer and layer["mediaType"] == "application/vnd.ollama.image.model":
|
|
ollama_cpp_model = layer["digest"]
|
|
break
|
|
|
|
if ollama_cpp_model is None:
|
|
raise ValueError("Invalid ollama file")
|
|
|
|
if ollama_cpp_model.startswith("sha256:"):
|
|
ollama_cpp_model = ollama_cpp_model[7:]
|
|
# ollama = C:\Users\admin\.ollama\models\manifests\registry.ollama.ai\library\gemma\2b
|
|
models_dir = ollama[:ollama.rfind("manifests")]
|
|
ollama_cpp_model = os.path.join(
|
|
models_dir, "blobs", f"sha256-{ollama_cpp_model}")
|
|
|
|
if not os.path.exists(ollama_cpp_model):
|
|
raise ValueError(f"Model not found at: {ollama_cpp_model}")
|
|
|
|
llama_cpp_model = ollama_cpp_model
|
|
|
|
chat_format = kwargs.get("chat_format", "auto")
|
|
if chat_format == "auto":
|
|
chat_format = None
|
|
return ({
|
|
"type": "ManualSelect",
|
|
"model_path": llama_cpp_model,
|
|
"chat_format": chat_format,
|
|
},)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_OllamaModelConfig_ManualSelect"] = MZ_OllamaModelConfig_ManualSelect
|
|
NODE_DISPLAY_NAME_MAPPINGS[
|
|
"MZ_OllamaModelConfig_ManualSelect"] = f"{AUTHOR_NAME} - ModelConfigManualSelect(OllamaFile)"
|
|
|
|
|
|
class MZ_LLamaCPPModelConfig_ManualSelect:
|
|
@ classmethod
|
|
def INPUT_TYPES(s):
|
|
gguf_files = Utils.get_gguf_files()
|
|
|
|
chat_format = mz_llama_cpp.get_llama_cpp_chat_handlers()
|
|
return {
|
|
"required": {
|
|
"llama_cpp_model": (gguf_files,),
|
|
"chat_format": (["auto"] + chat_format, {"default": "auto"}),
|
|
},
|
|
"optional": {
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("LLamaCPPModelConfig",)
|
|
RETURN_NAMES = ("llama_cpp_model_config",)
|
|
|
|
FUNCTION = "create"
|
|
CATEGORY = f"{CATEGORY_NAME}/others"
|
|
|
|
def create(self, **kwargs):
|
|
kwargs = kwargs.copy()
|
|
|
|
llama_cpp_model = kwargs.get("llama_cpp_model", "")
|
|
if llama_cpp_model != "":
|
|
llama_cpp_model = os.path.join(
|
|
Utils.get_gguf_models_path(), llama_cpp_model)
|
|
|
|
chat_format = kwargs.get("chat_format", "auto")
|
|
if chat_format == "auto":
|
|
chat_format = None
|
|
return ({
|
|
"type": "ManualSelect",
|
|
"model_path": llama_cpp_model,
|
|
"chat_format": chat_format,
|
|
},)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_LLamaCPPModelConfig_ManualSelect"] = MZ_LLamaCPPModelConfig_ManualSelect
|
|
NODE_DISPLAY_NAME_MAPPINGS[
|
|
"MZ_LLamaCPPModelConfig_ManualSelect"] = f"{AUTHOR_NAME} - ModelConfigManualSelect(LLamaCPP)"
|
|
|
|
|
|
class MZ_LLamaCPPModelConfig_DownloaderSelect:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
optional_models = Utils.get_model_zoo(tags_filter="llama")
|
|
model_names = [
|
|
model["model"] for model in optional_models
|
|
]
|
|
chat_format = mz_llama_cpp.get_llama_cpp_chat_handlers()
|
|
return {
|
|
"required": {
|
|
"model_name": (model_names,),
|
|
"chat_format": (["auto"] + chat_format, {"default": "auto"}),
|
|
},
|
|
"optional": {
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("LLamaCPPModelConfig",)
|
|
RETURN_NAMES = ("llama_cpp_model_config",)
|
|
|
|
FUNCTION = "create"
|
|
CATEGORY = f"{CATEGORY_NAME}/others"
|
|
|
|
def create(self, **kwargs):
|
|
kwargs = kwargs.copy()
|
|
|
|
model_name = kwargs.get("model_name", "")
|
|
chat_format = kwargs.get("chat_format", "auto")
|
|
if chat_format == "auto":
|
|
chat_format = None
|
|
return ({
|
|
"type": "DownloaderSelect",
|
|
"model_name": model_name,
|
|
"chat_format": chat_format,
|
|
},)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_LLamaCPPModelConfig_DownloaderSelect"] = MZ_LLamaCPPModelConfig_DownloaderSelect
|
|
NODE_DISPLAY_NAME_MAPPINGS[
|
|
"MZ_LLamaCPPModelConfig_DownloaderSelect"] = f"{AUTHOR_NAME} - ModelConfigDownloaderSelect(LLamaCPP)"
|
|
|
|
|
|
class MZ_LLamaCPPCLIPTextEncode:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
importlib.reload(mz_llama_cpp)
|
|
|
|
result = {
|
|
"required": {
|
|
},
|
|
"optional": {
|
|
"llama_cpp_model": ("LLamaCPPModelConfig",),
|
|
},
|
|
}
|
|
|
|
common_input = getCommonCLIPTextEncodeInput()
|
|
for key in common_input["required"]:
|
|
result["required"][key] = common_input["required"][key]
|
|
for key in common_input["optional"]:
|
|
result["optional"][key] = common_input["optional"][key]
|
|
|
|
return result
|
|
|
|
RETURN_TYPES = ("STRING", "CONDITIONING",)
|
|
RETURN_NAMES = ("text", "conditioning",)
|
|
OUTPUT_NODE = True
|
|
FUNCTION = "encode"
|
|
CATEGORY = CATEGORY_NAME
|
|
|
|
DESCRIPTION = """
|
|
llama_cpp_model不设置时，将使用默认模型: Meta-Llama-3-8B-Instruct.Q4_K_M.gguf
|
|
"""
|
|
|
|
def encode(self, **kwargs):
|
|
kwargs = kwargs.copy()
|
|
from . import mz_llama_core_nodes
|
|
importlib.reload(mz_llama_core_nodes)
|
|
|
|
return mz_llama_core_nodes.llama_cpp_node_encode(kwargs)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_LLamaCPPCLIPTextEncode"] = MZ_LLamaCPPCLIPTextEncode
|
|
NODE_DISPLAY_NAME_MAPPINGS[
|
|
"MZ_LLamaCPPCLIPTextEncode"] = f"{AUTHOR_NAME} - CLIPTextEncode(LLamaCPP Universal)"
|
|
|
|
|
|
class MZ_LLamaCPPOptions:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
value = mz_llama_cpp.LlamaCppOptions()
|
|
result = {}
|
|
|
|
for key in value:
|
|
if type(value[key]) == bool:
|
|
result[key] = ([True, False], {"default": value[key]})
|
|
elif type(value[key]) == int:
|
|
result[key] = ("INT", {
|
|
"default": value[key], "min": -0xffffffffffffffff, "max": 0xffffffffffffffff})
|
|
elif type(value[key]) == float:
|
|
result[key] = ("FLOAT", {
|
|
"default": value[key], "min": -0xffffffffffffffff, "max": 0xffffffffffffffff})
|
|
elif type(value[key]) == str:
|
|
result[key] = ("STRING", {"default": value[key]})
|
|
elif type(value[key]) == list:
|
|
result[key] = (value[key], {"default": value[key][0]})
|
|
else:
|
|
raise Exception(f"Unknown type: {type(value[key])}")
|
|
|
|
return {
|
|
"required": result,
|
|
}
|
|
|
|
RETURN_TYPES = ("LLamaCPPOptions",)
|
|
RETURN_NAMES = ("llama_cpp_options",)
|
|
|
|
FUNCTION = "create"
|
|
CATEGORY = f"{CATEGORY_NAME}/others"
|
|
|
|
def create(self, **kwargs):
|
|
kwargs = kwargs.copy()
|
|
importlib.reload(mz_llama_cpp)
|
|
opt = {}
|
|
for key in kwargs:
|
|
opt[key] = kwargs[key]
|
|
|
|
# if opt.get("chat_format", None) == "auto":
|
|
# opt["chat_format"] = None
|
|
return (opt,)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_LLamaCPPOptions"] = MZ_LLamaCPPOptions
|
|
NODE_DISPLAY_NAME_MAPPINGS["MZ_LLamaCPPOptions"] = f"{AUTHOR_NAME} - LLamaCPPOptions"
|
|
|
|
|
|
class MZ_CustomizeInstruct:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
from . import mz_prompts
|
|
|
|
return {
|
|
"required": {
|
|
"system": ("STRING", {"multiline": True, "default": mz_prompts.Long_prompt}),
|
|
"instruct": ("STRING", {"multiline": True, "default": ""}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("CustomizeInstruct",)
|
|
RETURN_NAMES = ("customize_instruct",)
|
|
FUNCTION = "create"
|
|
CATEGORY = f"{CATEGORY_NAME}/others"
|
|
|
|
def create(self, **kwargs):
|
|
kwargs = kwargs.copy()
|
|
|
|
return (kwargs,)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_CustomizeInstruct"] = MZ_CustomizeInstruct
|
|
NODE_DISPLAY_NAME_MAPPINGS["MZ_CustomizeInstruct"] = f"{AUTHOR_NAME} - CustomizeInstruct"
|
|
|
|
|
|
class MZ_ImageCaptionerConfig:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"directory": ("STRING", {"default": "", "placeholder": "directory"}),
|
|
"caption_suffix": ("STRING", {"default": ".caption"}),
|
|
"force_update": ([False, True], {"default": False}),
|
|
"retry_keyword": ("STRING", {"default": "not,\",error"}),
|
|
"prompt_fixed_beginning": ("STRING", {"default": "", }),
|
|
},
|
|
"optional": {
|
|
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("ImageCaptionerConfig",)
|
|
RETURN_NAMES = ("captioner_config", )
|
|
|
|
FUNCTION = "interrogate_batch"
|
|
CATEGORY = f"{CATEGORY_NAME}/others"
|
|
|
|
def interrogate_batch(self, **kwargs):
|
|
kwargs = kwargs.copy()
|
|
|
|
return (kwargs, )
|
|
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_ImageCaptionerConfig"] = MZ_ImageCaptionerConfig
|
|
NODE_DISPLAY_NAME_MAPPINGS["MZ_ImageCaptionerConfig"] = f"{AUTHOR_NAME} - ImageCaptionerConfig"
|
|
|
|
|
|
class MZ_OpenAIApiCLIPTextEncode:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
importlib.reload(mz_llama_cpp)
|
|
|
|
s.openai_config_path = os.path.join(
|
|
Utils.get_models_path(),
|
|
"openai_config.json",
|
|
)
|
|
default_config = {
|
|
"base_url": "",
|
|
"api_key": "",
|
|
"model_name": "gpt-3.5-turbo-1106",
|
|
}
|
|
if os.path.exists(s.openai_config_path):
|
|
try:
|
|
with open(s.openai_config_path, "r", encoding="utf-8") as f:
|
|
default_config = json.load(f)
|
|
except Exception as e:
|
|
print(f"Failed to load openai_config.json: {e}")
|
|
|
|
default_api_key = default_config.get("api_key", "")
|
|
if default_api_key != "":
|
|
default_api_key = default_api_key[:4] + "******"
|
|
result = {
|
|
"required": {
|
|
"base_url": ("STRING", {"default": default_config.get("base_url", ""), "placeholder": ""}),
|
|
"api_key": ("STRING", {"default": default_api_key, "placeholder": ""}),
|
|
"model_name": ("STRING", {"default": default_config.get("model_name", ""), }),
|
|
},
|
|
"optional": {
|
|
},
|
|
}
|
|
|
|
common_input = getCommonCLIPTextEncodeInput()
|
|
for key in common_input["required"]:
|
|
if key not in ["seed", "keep_device"]:
|
|
result["required"][key] = common_input["required"][key]
|
|
for key in common_input["optional"]:
|
|
if key != "llama_cpp_options":
|
|
result["optional"][key] = common_input["optional"][key]
|
|
|
|
return result
|
|
RETURN_TYPES = ("STRING", "CONDITIONING",)
|
|
RETURN_NAMES = ("text", "conditioning",)
|
|
OUTPUT_NODE = True
|
|
FUNCTION = "encode"
|
|
CATEGORY = CATEGORY_NAME
|
|
|
|
def encode(self, **kwargs):
|
|
kwargs = kwargs.copy()
|
|
|
|
from . import mz_openaiapi
|
|
importlib.reload(mz_openaiapi)
|
|
|
|
if kwargs.get("api_key", "").endswith("******"):
|
|
kwargs["api_key"] = ""
|
|
try:
|
|
with open(self.openai_config_path, "r", encoding="utf-8") as f:
|
|
config = json.load(f)
|
|
kwargs["api_key"] = config.get("api_key", "")
|
|
except Exception as e:
|
|
print(f"Failed to load openai_config.json: {e}")
|
|
|
|
if kwargs.get("api_key", "") != "":
|
|
with open(self.openai_config_path, "w", encoding="utf-8") as f:
|
|
json.dump({
|
|
"base_url": kwargs.get("base_url", ""),
|
|
"api_key": kwargs.get("api_key", ""),
|
|
"model_name": kwargs.get("model_name", ""),
|
|
}, f, indent=4)
|
|
else:
|
|
raise ValueError("api_key is required")
|
|
|
|
text = mz_openaiapi.query_beautify_prompt_text(kwargs)
|
|
conditionings = None
|
|
clip = kwargs.get("clip", None)
|
|
if clip is not None:
|
|
conditionings = Utils.a1111_clip_text_encode(clip, text, )
|
|
|
|
return {"ui": {"string": [Utils.to_debug_prompt(text),]}, "result": (text, conditionings)}
|
|
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_OpenAIApiCLIPTextEncode"] = MZ_OpenAIApiCLIPTextEncode
|
|
NODE_DISPLAY_NAME_MAPPINGS[
|
|
"MZ_OpenAIApiCLIPTextEncode"] = f"{AUTHOR_NAME} - CLIPTextEncode(OpenAIApi)"
|
|
|
|
|
|
class MZ_ImageInterrogatorCLIPTextEncode:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"resolution": ("INT", {"default": 512, "min": 128, "max": 0xffffffffffffffff}),
|
|
"post_processing": ([False, True], {"default": True}),
|
|
"keep_device": ([False, True], {"default": False}),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
},
|
|
"optional": {
|
|
"image_interrogator_model": ("ImageInterrogatorModelConfig", ),
|
|
"image": ("IMAGE",),
|
|
"clip": ("CLIP", ),
|
|
"llama_cpp_options": ("LLamaCPPOptions", ),
|
|
"customize_instruct": ("CustomizeInstruct", ),
|
|
"captioner_config": ("ImageCaptionerConfig", ),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING", "CONDITIONING",)
|
|
RETURN_NAMES = ("text", "conditioning",)
|
|
OUTPUT_NODE = True
|
|
FUNCTION = "encode"
|
|
CATEGORY = CATEGORY_NAME
|
|
|
|
def encode(self, **kwargs):
|
|
kwargs = kwargs.copy()
|
|
from . import mz_llama_core_nodes
|
|
importlib.reload(mz_llama_core_nodes)
|
|
|
|
return mz_llama_core_nodes.image_interrogator_node_encode(kwargs)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_ImageInterrogatorCLIPTextEncode"] = MZ_ImageInterrogatorCLIPTextEncode
|
|
NODE_DISPLAY_NAME_MAPPINGS[
|
|
"MZ_ImageInterrogatorCLIPTextEncode"] = f"{AUTHOR_NAME} - CLIPTextEncode(ImageInterrogator)"
|
|
|
|
|
|
class MZ_ImageInterrogatorModelConfig_ManualSelect:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
gguf_files = Utils.get_gguf_files()
|
|
chat_format = mz_llama_cpp.get_llama_cpp_chat_handlers()
|
|
return {
|
|
"required": {
|
|
"llama_cpp_model": (gguf_files,),
|
|
"mmproj_model": (["auto"] + gguf_files,),
|
|
"chat_format": (["auto"] + chat_format, {"default": "auto"}),
|
|
},
|
|
"optional": {
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("ImageInterrogatorModelConfig",)
|
|
RETURN_NAMES = ("image_interrogator_model",)
|
|
|
|
FUNCTION = "create"
|
|
CATEGORY = f"{CATEGORY_NAME}/others"
|
|
|
|
def create(self, **kwargs):
|
|
kwargs = kwargs.copy()
|
|
|
|
llama_cpp_model = kwargs.get("llama_cpp_model", "")
|
|
if llama_cpp_model != "":
|
|
llama_cpp_model = os.path.join(
|
|
Utils.get_gguf_models_path(), llama_cpp_model)
|
|
|
|
mmproj_model = kwargs.get("mmproj_model", "")
|
|
if mmproj_model != "":
|
|
mmproj_model = os.path.join(
|
|
Utils.get_gguf_models_path(), mmproj_model)
|
|
|
|
chat_format = kwargs.get("chat_format", "auto")
|
|
if chat_format == "auto":
|
|
chat_format = None
|
|
return ({
|
|
"type": "ManualSelect",
|
|
"model_path": llama_cpp_model,
|
|
"mmproj_model": mmproj_model,
|
|
"chat_format": chat_format,
|
|
},)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_ImageInterrogatorModelConfig_ManualSelect"] = MZ_ImageInterrogatorModelConfig_ManualSelect
|
|
NODE_DISPLAY_NAME_MAPPINGS[
|
|
"MZ_ImageInterrogatorModelConfig_ManualSelect"] = f"{AUTHOR_NAME} - ModelConfigManualSelect(ImageInterrogator)"
|
|
|
|
|
|
class MZ_ImageInterrogatorModelConfig_DownloaderSelect:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
optional_models = Utils.get_model_zoo(tags_filter="llava")
|
|
model_names = [
|
|
model["model"] for model in optional_models
|
|
]
|
|
|
|
optional_models = Utils.get_model_zoo(tags_filter="mmproj")
|
|
mmproj_model_names = [
|
|
model["model"] for model in optional_models
|
|
]
|
|
|
|
chat_format = mz_llama_cpp.get_llama_cpp_chat_handlers()
|
|
return {
|
|
"required": {
|
|
"model_name": (model_names,),
|
|
"mmproj_model_name": (["auto"] + mmproj_model_names,),
|
|
"chat_format": (["auto"] + chat_format, {"default": "auto"}),
|
|
},
|
|
"optional": {
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("ImageInterrogatorModelConfig",)
|
|
RETURN_NAMES = ("image_interrogator_model",)
|
|
|
|
FUNCTION = "create"
|
|
CATEGORY = f"{CATEGORY_NAME}/others"
|
|
|
|
def create(self, **kwargs):
|
|
kwargs = kwargs.copy()
|
|
model_name = kwargs.get("model_name")
|
|
mmproj_model_name = kwargs.get("mmproj_model_name", "auto")
|
|
chat_format = kwargs.get("chat_format", "auto")
|
|
if chat_format == "auto":
|
|
chat_format = None
|
|
return ({
|
|
"type": "DownloaderSelect",
|
|
"model_name": model_name,
|
|
"mmproj_model_name": mmproj_model_name,
|
|
"chat_format": chat_format,
|
|
},)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_ImageInterrogatorModelConfig_DownloaderSelect"] = MZ_ImageInterrogatorModelConfig_DownloaderSelect
|
|
NODE_DISPLAY_NAME_MAPPINGS[
|
|
"MZ_ImageInterrogatorModelConfig_DownloaderSelect"] = f"{AUTHOR_NAME} - ModelConfigDownloaderSelect(ImageInterrogator)"
|
|
|
|
|
|
class MZ_Florence2CLIPTextEncode:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"model_name": ([
|
|
"Florence-2-large-ft",
|
|
"Florence-2-large",
|
|
],),
|
|
"resolution": ("INT", {"default": 512, "min": 128, "max": 0xffffffffffffffff}),
|
|
"keep_device": ([False, True], {"default": False}),
|
|
# "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
},
|
|
"optional": {
|
|
"image": ("IMAGE",),
|
|
"clip": ("CLIP", ),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING", "CONDITIONING",)
|
|
RETURN_NAMES = ("text", "conditioning",)
|
|
OUTPUT_NODE = True
|
|
FUNCTION = "encode"
|
|
CATEGORY = CATEGORY_NAME
|
|
|
|
def encode(self, **kwargs):
|
|
kwargs = kwargs.copy()
|
|
from . import mz_transformers
|
|
importlib.reload(mz_transformers)
|
|
|
|
return mz_transformers.florence2_node_encode(kwargs)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_Florence2CLIPTextEncode"] = MZ_Florence2CLIPTextEncode
|
|
NODE_DISPLAY_NAME_MAPPINGS[
|
|
"MZ_Florence2CLIPTextEncode"] = f"{AUTHOR_NAME} - CLIPTextEncode(Florence-2)"
|
|
|
|
|
|
class MZ_Florence2Captioner:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"model_name": ([
|
|
"Florence-2-large-ft",
|
|
"Florence-2-large",
|
|
],),
|
|
"directory": ("STRING", {"default": "", "placeholder": "directory"}),
|
|
"resolution": ("INT", {"default": 512, "min": 128, "max": 0xffffffffffffffff}),
|
|
"batch_size": ("INT", {"default": 1, "min": 1, "max": 0xffffffffffffffff}),
|
|
"caption_suffix": ("STRING", {"default": ".caption"}),
|
|
"force_update": ([False, True], {"default": False}),
|
|
"prompt_fixed_beginning": ("STRING", {"default": "", }),
|
|
},
|
|
"optional": {
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING",)
|
|
RETURN_NAMES = ("debug",)
|
|
OUTPUT_NODE = True
|
|
FUNCTION = "encode"
|
|
CATEGORY = CATEGORY_NAME
|
|
|
|
def encode(self, **kwargs):
|
|
kwargs = kwargs.copy()
|
|
from . import mz_transformers
|
|
importlib.reload(mz_transformers)
|
|
|
|
kwargs["captioner_config"] = {
|
|
"directory": kwargs["directory"],
|
|
"resolution": kwargs["resolution"],
|
|
"batch_size": kwargs["batch_size"],
|
|
"caption_suffix": kwargs["caption_suffix"],
|
|
"force_update": kwargs["force_update"],
|
|
"prompt_fixed_beginning": kwargs["prompt_fixed_beginning"],
|
|
}
|
|
|
|
return mz_transformers.florence2_node_encode(kwargs)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_Florence2Captioner"] = MZ_Florence2Captioner
|
|
NODE_DISPLAY_NAME_MAPPINGS[
|
|
"MZ_Florence2Captioner"] = f"{AUTHOR_NAME} - Captioner(Florence-2)"
|
|
|
|
|
|
class MZ_PaliGemmaCLIPTextEncode:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"model_name": ([
|
|
"paligemma-sd3-long-captioner-v2",
|
|
"paligemma-sd3-long-captioner",
|
|
],),
|
|
"resolution": ("INT", {"default": 512, "min": 128, "max": 0xffffffffffffffff}),
|
|
"keep_device": ([False, True], {"default": False}),
|
|
# "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
},
|
|
"optional": {
|
|
"image": ("IMAGE",),
|
|
"clip": ("CLIP", ),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING", "CONDITIONING",)
|
|
RETURN_NAMES = ("text", "conditioning",)
|
|
OUTPUT_NODE = True
|
|
FUNCTION = "encode"
|
|
CATEGORY = CATEGORY_NAME
|
|
|
|
def encode(self, **kwargs):
|
|
kwargs = kwargs.copy()
|
|
from . import mz_transformers
|
|
importlib.reload(mz_transformers)
|
|
|
|
return mz_transformers.paligemma_node_encode(kwargs)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_PaliGemmaCLIPTextEncode"] = MZ_PaliGemmaCLIPTextEncode
|
|
NODE_DISPLAY_NAME_MAPPINGS[
|
|
"MZ_PaliGemmaCLIPTextEncode"] = f"{AUTHOR_NAME} - CLIPTextEncode(PaliGemma)"
|
|
|
|
|
|
class MZ_PaliGemmaCaptioner:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"model_name": ([
|
|
"paligemma-sd3-long-captioner-v2",
|
|
"paligemma-sd3-long-captioner",
|
|
],),
|
|
"directory": ("STRING", {"default": "", "placeholder": "directory"}),
|
|
"resolution": ("INT", {"default": 512, "min": 128, "max": 0xffffffffffffffff}),
|
|
"caption_suffix": ("STRING", {"default": ".caption"}),
|
|
"force_update": ([False, True], {"default": False}),
|
|
"prompt_fixed_beginning": ("STRING", {"default": "", }),
|
|
},
|
|
"optional": {
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING",)
|
|
RETURN_NAMES = ("debug",)
|
|
OUTPUT_NODE = True
|
|
FUNCTION = "encode"
|
|
CATEGORY = CATEGORY_NAME
|
|
|
|
def encode(self, **kwargs):
|
|
kwargs = kwargs.copy()
|
|
from . import mz_transformers
|
|
importlib.reload(mz_transformers)
|
|
kwargs["captioner_config"] = {
|
|
"directory": kwargs["directory"],
|
|
"resolution": kwargs["resolution"],
|
|
"caption_suffix": kwargs["caption_suffix"],
|
|
"force_update": kwargs["force_update"],
|
|
"prompt_fixed_beginning": kwargs["prompt_fixed_beginning"],
|
|
}
|
|
return mz_transformers.paligemma_node_encode(kwargs)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_PaliGemmaCaptioner"] = MZ_PaliGemmaCaptioner
|
|
NODE_DISPLAY_NAME_MAPPINGS[
|
|
"MZ_PaliGemmaCaptioner"] = f"{AUTHOR_NAME} - Captioner(PaliGemma)"
|
|
|
|
try:
|
|
from . import mz_gen_translate
|
|
mz_gen_translate.gen_translate(
|
|
NODE_DISPLAY_NAME_MAPPINGS, NODE_CLASS_MAPPINGS)
|
|
except Exception as e:
|
|
print(f"Failed to generate translation: {e}")
|
|
|
|
|
|
from .v1.init import NODE_CLASS_MAPPINGS as DEPRECATED_NODE_CLASS_MAPPINGS
|
|
from .v1.init import NODE_DISPLAY_NAME_MAPPINGS as DEPRECATED_NODE_DISPLAY_NAME_MAPPINGS
|
|
|
|
NODE_CLASS_MAPPINGS.update(DEPRECATED_NODE_CLASS_MAPPINGS)
|
|
NODE_DISPLAY_NAME_MAPPINGS.update(DEPRECATED_NODE_DISPLAY_NAME_MAPPINGS)
|