263 lines
9.0 KiB
Python
263 lines
9.0 KiB
Python
|
|
|
|
import os
|
|
import sys
|
|
from .mz_prompt_utils import Utils
|
|
from nodes import MAX_RESOLUTION
|
|
import comfy.utils
|
|
import shutil
|
|
import comfy.samplers
|
|
|
|
AUTHOR_NAME = u"[MinusZone]"
|
|
CATEGORY_NAME = f"{AUTHOR_NAME} Utils"
|
|
|
|
sys.path.append(os.path.join(os.path.dirname(__file__)))
|
|
|
|
import importlib
|
|
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
}
|
|
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
}
|
|
|
|
|
|
import mz_llama3
|
|
import mz_llama_cpp
|
|
import mz_llava
|
|
|
|
|
|
|
|
|
|
|
|
llama3_models = [
|
|
"Meta-Llama-3-8B-Instruct.Q4_K_M.gguf",
|
|
"Meta-Llama-3-8B-Instruct.Q2_K.gguf",
|
|
"Meta-Llama-3-8B-Instruct.Q3_K_L.gguf",
|
|
"Meta-Llama-3-8B-Instruct.Q3_K_M.gguf",
|
|
"Meta-Llama-3-8B-Instruct.Q3_K_S.gguf",
|
|
"Meta-Llama-3-8B-Instruct.Q4_0.gguf",
|
|
"Meta-Llama-3-8B-Instruct.Q4_1.gguf",
|
|
"Meta-Llama-3-8B-Instruct.Q4_K_S.gguf",
|
|
"Meta-Llama-3-8B-Instruct.Q5_0.gguf",
|
|
"Meta-Llama-3-8B-Instruct.Q5_1.gguf",
|
|
"Meta-Llama-3-8B-Instruct.Q5_K_M.gguf",
|
|
"Meta-Llama-3-8B-Instruct.Q5_K_S.gguf",
|
|
"Meta-Llama-3-8B-Instruct.Q6_K.gguf",
|
|
"Meta-Llama-3-8B-Instruct.Q8_0.gguf",
|
|
]
|
|
|
|
class MZ_LLamaCPPOptions:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
opt = mz_llama_cpp.LlamaCppOptions()
|
|
value = opt.value
|
|
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": 0, "max": 0xffffffffffffffff})
|
|
elif type(value[key]) == float:
|
|
result[key] = ("FLOAT", {"default": value[key], "min": 0, "max": 0xffffffffffffffff})
|
|
elif type(value[key]) == str:
|
|
result[key] = ("STRING", {"default": value[key]})
|
|
else:
|
|
raise Exception(f"Unknown type: {type(value[key])}")
|
|
|
|
return {
|
|
"required": result,
|
|
}
|
|
|
|
|
|
RETURN_TYPES = ("LLamaCPPOptions",)
|
|
FUNCTION = "create"
|
|
CATEGORY = CATEGORY_NAME
|
|
def create(self, **kwargs):
|
|
importlib.reload(mz_llama_cpp)
|
|
opt = mz_llama_cpp.LlamaCppOptions()
|
|
for key in kwargs:
|
|
opt.value[key] = kwargs[key]
|
|
return (opt,)
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_LLamaCPPOptions"] = MZ_LLamaCPPOptions
|
|
NODE_DISPLAY_NAME_MAPPINGS["MZ_LLamaCPPOptions"] = f"{AUTHOR_NAME} - LLamaCPPOptions"
|
|
|
|
class MZ_LLama3CLIPTextEncode:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
m_llama3_models = llama3_models.copy()
|
|
for i in range(len(m_llama3_models)):
|
|
if mz_llama3.get_exist_model(m_llama3_models[i]) is not None:
|
|
m_llama3_models[i] += "[downloaded]"
|
|
|
|
|
|
importlib.reload(mz_llama3)
|
|
style_presets = mz_llama3.get_style_presets()
|
|
|
|
return {
|
|
"required": {
|
|
"llama_cpp_model": (m_llama3_models, {"default": m_llama3_models[0]}),
|
|
"download_source": (
|
|
["none", "modelscope", "hf-mirror.com",],
|
|
{"default": "none"}
|
|
),
|
|
"style_presets": (
|
|
style_presets, {"default": style_presets[0]}
|
|
),
|
|
"text": ("STRING", {"multiline": True,}),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
"n_gpu_layers": ("INT", {"default": 40, "min": -1, "max": 0xffffffffffffffff}),
|
|
},
|
|
"optional": {
|
|
"clip": ("CLIP", ),
|
|
"llama_cpp_options": ("LLamaCPPOptions", ),
|
|
},
|
|
}
|
|
RETURN_TYPES = ("STRING", "CONDITIONING",)
|
|
FUNCTION = "encode"
|
|
CATEGORY = CATEGORY_NAME
|
|
def encode(self, text, llama_cpp_model, style_presets, clip=None, download_source=None, seed=0, n_gpu_layers=40, llama_cpp_options=None):
|
|
importlib.reload(mz_llama3)
|
|
|
|
llama_cpp_model = llama_cpp_model.replace("[downloaded]", "")
|
|
|
|
options = {}
|
|
if llama_cpp_options is not None:
|
|
options = llama_cpp_options.value
|
|
text = mz_llama3.query_beautify_prompt_text(llama_cpp_model, n_gpu_layers, text, style_presets, download_source, options)
|
|
conditionings = None
|
|
if clip is not None:
|
|
tokens = clip.tokenize(text)
|
|
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True,)
|
|
conditionings = [[cond, {"pooled_output": pooled}]]
|
|
|
|
return (text, conditionings)
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_LLama3CLIPTextEncode"] = MZ_LLama3CLIPTextEncode
|
|
NODE_DISPLAY_NAME_MAPPINGS["MZ_LLama3CLIPTextEncode"] = f"{AUTHOR_NAME} - LLama3CLIPTextEncode"
|
|
|
|
|
|
LLava_models = [
|
|
"ggml_bakllava-1/ggml-model-q4_k.gguf",
|
|
"ggml_bakllava-1/ggml-model-q5_k.gguf",
|
|
"ggml_bakllava-1/ggml-model-f16.gguf",
|
|
"ggml_llava-v1.5-7b/ggml-model-q4_k.gguf",
|
|
"ggml_llava-v1.5-7b/ggml-model-q5_k.gguf",
|
|
"ggml_llava-v1.5-7b/ggml-model-f16.gguf",
|
|
]
|
|
|
|
LLava_mmproj_models = [
|
|
# "llava-1.6-mistral-7b-gguf/mmproj-model-f16.gguf",
|
|
"ggml_bakllava-1/mmproj-model-f16.gguf",
|
|
"ggml_llava-v1.5-7b/mmproj-model-f16.gguf",
|
|
]
|
|
|
|
class MZ_LLavaImageInterrogator:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
|
|
m_llava_models = LLava_models.copy()
|
|
for i in range(len(m_llava_models)):
|
|
if mz_llava.get_exist_model(m_llava_models[i]) is not None:
|
|
m_llava_models[i] += "[downloaded]"
|
|
|
|
m_llava_mmproj_models = LLava_mmproj_models.copy()
|
|
for i in range(len(m_llava_mmproj_models)):
|
|
if mz_llava.get_exist_model(m_llava_mmproj_models[i]) is not None:
|
|
m_llava_mmproj_models[i] += "[downloaded]"
|
|
|
|
|
|
return {"required": {
|
|
"llama_cpp_model": (m_llava_models, {"default": m_llava_models[0]}),
|
|
"mmproj_model": (m_llava_mmproj_models, {"default": m_llava_mmproj_models[0]}),
|
|
"download_source": (
|
|
[
|
|
"none",
|
|
"modelscope",
|
|
"hf-mirror.com",
|
|
],
|
|
{"default": "none"}
|
|
),
|
|
"image": ("IMAGE",),
|
|
"resolution": ("INT", {"default": 512, "min": 128, "max": 2048}),
|
|
# "prefix" : ("STRING", {"default": "(masterpiece)", "multiline": True, }),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
"n_gpu_layers": ("INT", {"default": 40, "min": -1, "max": 0xffffffffffffffff}),
|
|
},
|
|
"optional": {
|
|
"clip": ("CLIP", ),
|
|
"llama_cpp_options": ("LLamaCPPOptions", ),
|
|
}}
|
|
RETURN_TYPES = ("STRING", "CONDITIONING",)
|
|
FUNCTION = "interrogate"
|
|
CATEGORY = CATEGORY_NAME
|
|
def interrogate(self, llama_cpp_model, mmproj_model, image, resolution, download_source=None, seed=0, clip=None, n_gpu_layers=40, llama_cpp_options=None):
|
|
importlib.reload(mz_llava)
|
|
|
|
llama_cpp_model = llama_cpp_model.replace("[downloaded]", "")
|
|
mmproj_model = mmproj_model.replace("[downloaded]", "")
|
|
|
|
# prefix = Utils.prompt_zh_to_en(prefix)
|
|
# if not prefix.endswith(","):
|
|
# prefix += ","
|
|
|
|
image_pil = Utils.tensor2pil(image)
|
|
|
|
|
|
options = {}
|
|
if llama_cpp_options is not None:
|
|
options = llama_cpp_options.value
|
|
|
|
response = mz_llava.image_interrogator(
|
|
llama_cpp_model,
|
|
mmproj_model,
|
|
n_gpu_layers,
|
|
image_pil,
|
|
resolution,
|
|
download_source,
|
|
options,
|
|
)
|
|
conditionings = None
|
|
if clip is not None:
|
|
tokens = clip.tokenize(response)
|
|
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True,)
|
|
conditionings = [[cond, {"pooled_output": pooled}]]
|
|
return (response, conditionings)
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_LLavaImageInterrogator"] = MZ_LLavaImageInterrogator
|
|
NODE_DISPLAY_NAME_MAPPINGS["MZ_LLavaImageInterrogator"] = f"{AUTHOR_NAME} - LLavaImageInterrogator"
|
|
|
|
|
|
class MZ_LLamaCPPInterrogator:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"model_file": ("STRING", {"default": ""}),
|
|
"use_system": ([True, False], {"default": True}),
|
|
"system": ("STRING", {"multiline": True, "default": "You are a helpful assistant."}),
|
|
"prompt": ("STRING", {"multiline": True,}),
|
|
"n_gpu_layers": ("INT", {"default": 40, "min": -1, "max": 0xffffffffffffffff}),
|
|
},
|
|
}
|
|
RETURN_TYPES = ("STRING",)
|
|
FUNCTION = "simple_interrogator"
|
|
CATEGORY = CATEGORY_NAME
|
|
def simple_interrogator(self, model_file, prompt, use_system=True, system="You are a helpful assistant.", n_gpu_layers=40):
|
|
importlib.reload(mz_llama_cpp)
|
|
result = mz_llama_cpp.llama_cpp_simple_interrogator(
|
|
model_file,
|
|
n_gpu_layers=n_gpu_layers,
|
|
use_system=use_system,
|
|
system=system,
|
|
question=prompt,
|
|
)
|
|
return (result,)
|
|
|
|
NODE_CLASS_MAPPINGS["MZ_LLamaCPPInterrogator"] = MZ_LLamaCPPInterrogator
|
|
NODE_DISPLAY_NAME_MAPPINGS["MZ_LLamaCPPInterrogator"] = f"{AUTHOR_NAME} - LLamaCPP simple interrogator" |