478 lines
16 KiB
Python
478 lines
16 KiB
Python
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import os
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import sys
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from .mz_prompt_utils import Utils
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from nodes import MAX_RESOLUTION
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import comfy.utils
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import shutil
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import comfy.samplers
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AUTHOR_NAME = u"[MinusZone]"
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CATEGORY_NAME = f"{AUTHOR_NAME} Utils"
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sys.path.append(os.path.join(os.path.dirname(__file__)))
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import importlib
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NODE_CLASS_MAPPINGS = {
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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}
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import mz_llama3
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import mz_phi3
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import mz_llama_cpp
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import mz_llava
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class MZ_LLamaCPPOptions:
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@classmethod
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def INPUT_TYPES(s):
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value = mz_llama_cpp.LlamaCppOptions()
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result = {}
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for key in value:
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if type(value[key]) == bool:
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result[key] = ([True, False], {"default": value[key]})
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elif type(value[key]) == int:
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result[key] = ("INT", {
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"default": value[key], "min": -0xffffffffffffffff, "max": 0xffffffffffffffff})
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elif type(value[key]) == float:
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result[key] = ("FLOAT", {
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"default": value[key], "min": -0xffffffffffffffff, "max": 0xffffffffffffffff})
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elif type(value[key]) == str:
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result[key] = ("STRING", {"default": value[key]})
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elif type(value[key]) == list:
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result[key] = (value[key], {"default": value[key][0]})
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else:
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raise Exception(f"Unknown type: {type(value[key])}")
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return {
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"required": result,
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}
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RETURN_TYPES = ("LLamaCPPOptions",)
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RETURN_NAMES = ("llama_cpp_options",)
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FUNCTION = "create"
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CATEGORY = CATEGORY_NAME
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def create(self, **kwargs):
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importlib.reload(mz_llama_cpp)
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opt = {}
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for key in kwargs:
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opt[key] = kwargs[key]
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return (opt,)
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NODE_CLASS_MAPPINGS["MZ_LLamaCPPOptions"] = MZ_LLamaCPPOptions
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NODE_DISPLAY_NAME_MAPPINGS["MZ_LLamaCPPOptions"] = f"{AUTHOR_NAME} - LLamaCPPOptions"
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class MZ_CustomizeInstruct:
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@classmethod
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def INPUT_TYPES(s):
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import mz_prompts
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return {
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"required": {
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"system": ("STRING", {"multiline": True, "default": mz_prompts.Long_prompt}),
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"instruct": ("STRING", {"multiline": True, "default": "Short: %text%"}),
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"start_str": ("STRING", {"default": "Long: "}),
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"end_str": ("STRING", {"default": ""}),
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},
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}
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RETURN_TYPES = ("CustomizeInstruct",)
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RETURN_NAMES = ("customize_instruct",)
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FUNCTION = "create"
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CATEGORY = CATEGORY_NAME
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def create(self, **kwargs):
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return (kwargs,)
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NODE_CLASS_MAPPINGS["MZ_CustomizeInstruct"] = MZ_CustomizeInstruct
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NODE_DISPLAY_NAME_MAPPINGS["MZ_CustomizeInstruct"] = f"{AUTHOR_NAME} - CustomizeInstruct"
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def getCommonCLIPTextEncodeInput():
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style_presets = mz_llama_cpp.get_style_presets()
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CommonCLIPTextEncodeInput = {
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"required": {
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"prompt_version": (["v1"], {"default": "v1"}),
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"style_presets": (
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style_presets, {"default": style_presets[1]}
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),
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"text": ("STRING", {"multiline": True, }),
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"keep_device": ([False, True], {"default": False}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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},
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"optional": {
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"clip": ("CLIP", ),
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"llama_cpp_options": ("LLamaCPPOptions", ),
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"customize_instruct": ("CustomizeInstruct", ),
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# "customize_json_schema": ("STRING", ),
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}
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}
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return CommonCLIPTextEncodeInput
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class MZ_LLama3CLIPTextEncode:
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@classmethod
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def INPUT_TYPES(s):
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m_models = mz_llama3.llama3_models.copy()
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for i in range(len(m_models)):
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if mz_llama3.get_exist_model(m_models[i]) is not None:
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m_models[i] += "[downloaded]"
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importlib.reload(mz_llama_cpp)
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result = {
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"required": {
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"llama_cpp_model": (m_models, {"default": m_models[0]}),
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"download_source": (
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["none", "modelscope", "hf-mirror.com",],
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{"default": "none"}
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),
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},
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"optional": {},
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}
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common_input = getCommonCLIPTextEncodeInput()
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for key in common_input["required"]:
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result["required"][key] = common_input["required"][key]
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for key in common_input["optional"]:
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result["optional"][key] = common_input["optional"][key]
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return result
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RETURN_TYPES = ("STRING", "CONDITIONING",)
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RETURN_NAMES = ("text", "conditioning",)
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FUNCTION = "encode"
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CATEGORY = CATEGORY_NAME
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def encode(self, **kwargs):
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importlib.reload(mz_llama3)
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kwargs["llama_cpp_model"] = kwargs.get(
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"llama_cpp_model", "").replace("[downloaded]", "")
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text = mz_llama3.query_beautify_prompt_text(kwargs)
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conditionings = None
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clip = kwargs.get("clip", None)
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if clip is not None:
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tokens = clip.tokenize(text)
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True,)
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conditionings = [[cond, {"pooled_output": pooled}]]
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return (text, conditionings)
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NODE_CLASS_MAPPINGS["MZ_LLama3CLIPTextEncode"] = MZ_LLama3CLIPTextEncode
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NODE_DISPLAY_NAME_MAPPINGS[
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"MZ_LLama3CLIPTextEncode"] = f"{AUTHOR_NAME} - CLIPTextEncode(LLama3)"
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class MZ_Phi3CLIPTextEncode:
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@classmethod
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def INPUT_TYPES(s):
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m_models = mz_phi3.phi3_models.copy()
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for i in range(len(m_models)):
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if mz_llama3.get_exist_model(m_models[i]) is not None:
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m_models[i] += "[downloaded]"
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importlib.reload(mz_phi3)
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result = {
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"required": {
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"llama_cpp_model": (m_models, {"default": m_models[0]}),
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"download_source": (
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["none", "modelscope", "hf-mirror.com",],
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{"default": "none"}
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),
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},
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"optional": {},
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}
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common_input = getCommonCLIPTextEncodeInput()
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for key in common_input["required"]:
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result["required"][key] = common_input["required"][key]
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for key in common_input["optional"]:
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result["optional"][key] = common_input["optional"][key]
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return result
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RETURN_TYPES = ("STRING", "CONDITIONING",)
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RETURN_NAMES = ("text", "conditioning",)
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FUNCTION = "encode"
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CATEGORY = CATEGORY_NAME
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def encode(self, **kwargs):
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importlib.reload(mz_llama3)
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kwargs["llama_cpp_model"] = kwargs.get(
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"llama_cpp_model", "").replace("[downloaded]", "")
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text = mz_phi3.query_beautify_prompt_text(kwargs)
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conditionings = None
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clip = kwargs.get("clip", None)
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if clip is not None:
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tokens = clip.tokenize(text)
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True,)
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conditionings = [[cond, {"pooled_output": pooled}]]
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return (text, conditionings)
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NODE_CLASS_MAPPINGS["MZ_Phi3CLIPTextEncode"] = MZ_Phi3CLIPTextEncode
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NODE_DISPLAY_NAME_MAPPINGS[
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"MZ_Phi3CLIPTextEncode"] = f"{AUTHOR_NAME} - CLIPTextEncode(Phi3)"
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class MZ_BaseLLamaCPPCLIPTextEncode:
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@classmethod
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def INPUT_TYPES(s):
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importlib.reload(mz_llama_cpp)
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result = {
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"required": {
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"llama_cpp_model": ("STRING", {"default": "", "placeholder": "model_path"}),
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},
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"optional": {
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},
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}
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common_input = getCommonCLIPTextEncodeInput()
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for key in common_input["required"]:
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result["required"][key] = common_input["required"][key]
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for key in common_input["optional"]:
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result["optional"][key] = common_input["optional"][key]
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return result
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RETURN_TYPES = ("STRING", "CONDITIONING",)
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RETURN_NAMES = ("text", "conditioning",)
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FUNCTION = "encode"
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CATEGORY = CATEGORY_NAME
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def encode(self, **kwargs):
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importlib.reload(mz_llama3)
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kwargs["llama_cpp_model"] = kwargs.get(
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"llama_cpp_model", "").replace("[downloaded]", "")
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text = mz_llama_cpp.base_query_beautify_prompt_text(kwargs)
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conditionings = None
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clip = kwargs.get("clip", None)
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if clip is not None:
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tokens = clip.tokenize(text)
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True,)
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conditionings = [[cond, {"pooled_output": pooled}]]
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return (text, conditionings)
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NODE_CLASS_MAPPINGS["MZ_BaseLLamaCPPCLIPTextEncode"] = MZ_BaseLLamaCPPCLIPTextEncode
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NODE_DISPLAY_NAME_MAPPINGS[
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"MZ_BaseLLamaCPPCLIPTextEncode"] = f"{AUTHOR_NAME} - CLIPTextEncode(BaseLLamaCPP)"
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class MZ_LLavaImageInterrogator:
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@classmethod
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def INPUT_TYPES(s):
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importlib.reload(mz_llava)
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m_llava_models = mz_llava.LLava_models.copy()
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for i in range(len(m_llava_models)):
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if mz_llava.get_exist_model(m_llava_models[i]) is not None:
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m_llava_models[i] += "[downloaded]"
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m_llava_mmproj_models = mz_llava.LLava_mmproj_models.copy()
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for i in range(len(m_llava_mmproj_models)):
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if mz_llava.get_exist_model(m_llava_mmproj_models[i]) is not None:
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m_llava_mmproj_models[i] += "[downloaded]"
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return {"required": {
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"llama_cpp_model": (m_llava_models, {"default": m_llava_models[0]}),
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"mmproj_model": (m_llava_mmproj_models, {"default": m_llava_mmproj_models[0]}),
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"download_source": (
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[
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"none",
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"modelscope",
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"hf-mirror.com",
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],
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{"default": "none"}
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),
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"image": ("IMAGE",),
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"resolution": ("INT", {"default": 512, "min": 128, "max": 2048}),
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"sd_format": (["none", "v1"], {"default": "none"}),
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"keep_device": ([False, True], {"default": False}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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},
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"optional": {
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"clip": ("CLIP", ),
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"llama_cpp_options": ("LLamaCPPOptions", ),
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"customize_instruct": ("CustomizeInstruct", ),
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}}
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RETURN_TYPES = ("STRING", "CONDITIONING",)
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RETURN_NAMES = ("text", "conditioning",)
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FUNCTION = "interrogate"
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CATEGORY = CATEGORY_NAME
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def interrogate(self, **kwargs):
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importlib.reload(mz_llava)
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kwargs["llama_cpp_model"] = kwargs.get(
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"llama_cpp_model", "").replace("[downloaded]", "")
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kwargs["mmproj_model"] = kwargs.get(
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"mmproj_model", "").replace("[downloaded]", "")
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kwargs["image"] = Utils.tensor2pil(kwargs["image"])
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response = mz_llava.image_interrogator(kwargs)
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conditionings = None
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clip = kwargs.get("clip", None)
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if clip is not None:
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tokens = clip.tokenize(response)
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True,)
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conditionings = [[cond, {"pooled_output": pooled}]]
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return (response, conditionings)
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NODE_CLASS_MAPPINGS["MZ_LLavaImageInterrogator"] = MZ_LLavaImageInterrogator
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NODE_DISPLAY_NAME_MAPPINGS[
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"MZ_LLavaImageInterrogator"] = f"{AUTHOR_NAME} - ImageInterrogator(LLava)"
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class MZ_BaseLLavaImageInterrogator:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"llama_cpp_model": ("STRING", {"default": ""}),
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"mmproj_model": ("STRING", {"default": ""}),
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"image": ("IMAGE",),
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"resolution": ("INT", {"default": 512, "min": 128, "max": 2048}),
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"sd_format": (["none", "v1"], {"default": "none"}),
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"keep_device": ([False, True], {"default": False}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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},
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"optional": {
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"clip": ("CLIP", ),
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"llama_cpp_options": ("LLamaCPPOptions", ),
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"customize_instruct": ("CustomizeInstruct", ),
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}}
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RETURN_TYPES = ("STRING", "CONDITIONING",)
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RETURN_NAMES = ("text", "conditioning",)
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FUNCTION = "interrogate"
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CATEGORY = CATEGORY_NAME
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def interrogate(self, **kwargs):
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importlib.reload(mz_llava)
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kwargs["image"] = Utils.tensor2pil(kwargs["image"])
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response = mz_llava.base_image_interrogator(kwargs)
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conditionings = None
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clip = kwargs.get("clip", None)
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if clip is not None:
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tokens = clip.tokenize(response)
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True,)
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conditionings = [[cond, {"pooled_output": pooled}]]
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return (response, conditionings)
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NODE_CLASS_MAPPINGS["MZ_BaseLLavaImageInterrogator"] = MZ_BaseLLavaImageInterrogator
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NODE_DISPLAY_NAME_MAPPINGS[
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"MZ_BaseLLavaImageInterrogator"] = f"{AUTHOR_NAME} - ImageInterrogator(BaseLLava)"
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class MZ_LLamaCPPInterrogator:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"model_file": ("STRING", {"default": ""}),
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"use_system": ([True, False], {"default": True}),
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"system": ("STRING", {"multiline": True, "default": "You are a helpful assistant."}),
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"prompt": ("STRING", {"multiline": True, }),
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"n_gpu_layers": ("INT", {"default": -1, "min": -1, "max": 0xffffffffffffffff}),
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},
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("text",)
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FUNCTION = "simple_interrogator"
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CATEGORY = CATEGORY_NAME
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def simple_interrogator(self, model_file, prompt, use_system=True, system="You are a helpful assistant.", n_gpu_layers=-1):
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importlib.reload(mz_llama_cpp)
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result = mz_llama_cpp.llama_cpp_simple_interrogator(
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model_file,
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n_gpu_layers=n_gpu_layers,
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use_system=use_system,
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system=system,
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question=prompt,
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)
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return (result,)
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# NODE_CLASS_MAPPINGS["MZ_LLamaCPPInterrogator"] = MZ_LLamaCPPInterrogator
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# NODE_DISPLAY_NAME_MAPPINGS["MZ_LLamaCPPInterrogator"] = f"{AUTHOR_NAME} - LLamaCPP simple interrogator"
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class MZ_OpenAIApiCLIPTextEncode:
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@classmethod
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def INPUT_TYPES(s):
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importlib.reload(mz_llama_cpp)
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result = {
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"required": {
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"base_url": ("STRING", {"default": ""}),
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"api_key": ("STRING", {"default": "", "placeholder": ""}),
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"model_name": ("STRING", {"default": "gpt-3.5-turbo-1106"}),
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},
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"optional": {
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},
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}
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common_input = getCommonCLIPTextEncodeInput()
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for key in common_input["required"]:
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if key not in ["seed", "keep_device"]:
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result["required"][key] = common_input["required"][key]
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for key in common_input["optional"]:
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if key != "llama_cpp_options":
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result["optional"][key] = common_input["optional"][key]
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return result
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RETURN_TYPES = ("STRING", "CONDITIONING",)
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RETURN_NAMES = ("text", "conditioning",)
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FUNCTION = "encode"
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CATEGORY = CATEGORY_NAME
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def encode(self, **kwargs):
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import mz_openaiapi
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importlib.reload(mz_openaiapi)
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text = mz_openaiapi.query_beautify_prompt_text(kwargs)
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conditionings = None
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clip = kwargs.get("clip", None)
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if clip is not None:
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tokens = clip.tokenize(text)
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True,)
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conditionings = [[cond, {"pooled_output": pooled}]]
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return (text, conditionings)
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NODE_CLASS_MAPPINGS["MZ_OpenAIApiCLIPTextEncode"] = MZ_OpenAIApiCLIPTextEncode
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NODE_DISPLAY_NAME_MAPPINGS[
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"MZ_OpenAIApiCLIPTextEncode"] = f"{AUTHOR_NAME} - CLIPTextEncode(OpenAIApi)"
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import mz_gen_translate
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mz_gen_translate.gen_translate(NODE_DISPLAY_NAME_MAPPINGS, NODE_CLASS_MAPPINGS)
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