nodes/MinusZoneAI-ComfyUI-Prompt-MZ
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wailovet
2024-04-24 13:13:15 +08:00
parent 62a7707a8e
commit 87fdc835e9
4 changed files with 195 additions and 19 deletions
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__init__.py
+49 -3
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@@ -96,8 +96,8 @@ class MZ_LLama3CLIPTextEncode:
m_llama3_models[i] += "[downloaded]"
importlib.reload(mz_llama3)
style_presets = mz_llama3.get_style_presets()
importlib.reload(mz_llama_cpp)
style_presets = mz_llama_cpp.get_style_presets()
return {
"required": {
@@ -260,4 +260,50 @@ class MZ_LLamaCPPInterrogator:
return (result,)
NODE_CLASS_MAPPINGS["MZ_LLamaCPPInterrogator"] = MZ_LLamaCPPInterrogator
NODE_DISPLAY_NAME_MAPPINGS["MZ_LLamaCPPInterrogator"] = f"{AUTHOR_NAME} - LLamaCPP simple interrogator"
NODE_DISPLAY_NAME_MAPPINGS["MZ_LLamaCPPInterrogator"] = f"{AUTHOR_NAME} - LLamaCPP simple interrogator"
class MZ_BaseLLamaCPPCLIPTextEncode:
@classmethod
def INPUT_TYPES(s):
importlib.reload(mz_llama_cpp)
style_presets = mz_llama_cpp.get_style_presets()
return {
"required": {
"llama_cpp_model": ("STRING", {"default": "", "placeholder": "model_path"}),
"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, seed=0, n_gpu_layers=40, llama_cpp_options=None):
importlib.reload(mz_llama3)
options = {}
if llama_cpp_options is not None:
options = llama_cpp_options.value
text = mz_llama_cpp.base_query_beautify_prompt_text(llama_cpp_model, n_gpu_layers, text, style_presets, 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_BaseLLamaCPPCLIPTextEncode"] = MZ_BaseLLamaCPPCLIPTextEncode
NODE_DISPLAY_NAME_MAPPINGS["MZ_BaseLLamaCPPCLIPTextEncode"] = f"{AUTHOR_NAME} - BaseLLamaCPPCLIPTextEncode"
mz_llama3.py
+7 -13
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@@ -23,18 +23,6 @@ def get_exist_model(model_name):
return None
high_quality_prompt = "((high quality:1.4), (best quality:1.4), (masterpiece:1.4), (8K resolution), (2k wallpaper))"
style_presets_prompt = {
"high_quality": high_quality_prompt,
"photography": f"{high_quality_prompt}, (RAW photo, best quality), (realistic, photo-realistic:1.2), (bokeh, cinematic shot, dynamic composition, incredibly detailed, sharpen, details, intricate detail, professional lighting, film lighting, 35mm, anamorphic, lightroom, cinematography, bokeh, lens flare, film grain, HDR10, 8K)",
"illustration": f"{high_quality_prompt}, ((detailed matte painting, intricate detail, splash screen, complementary colors), (detailed),(intricate details),illustration,an extremely delicate and beautiful,ultra-detailed,highres,extremely detailed)",
}
def get_style_presets():
return [
"high_quality",
"photography",
"illustration",
]
def query_beautify_prompt_text(model_name, n_gpu_layers, text, style_presets, download_source=None, options={}):
if options is None:
@@ -46,6 +34,12 @@ def query_beautify_prompt_text(model_name, n_gpu_layers, text, style_presets, do
try:
model_file = None
if options.get("customize_model_file", None) is not None:
model_file = options.get("customize_model_file")
model_file = get_exist_model(model_name)
if model_file is None:
@@ -151,7 +145,7 @@ def query_beautify_prompt_text(model_name, n_gpu_layers, text, style_presets, do
full_response = mz_prompt_utils.Utils.prompt_zh_to_en(full_response)
style_presets_prompt_text = style_presets_prompt.get(style_presets, "")
style_presets_prompt_text = mz_llama_cpp.style_presets_prompt.get(style_presets, "")
full_response = f"{style_presets_prompt_text}, {full_response}"
return full_response
mz_llama_cpp.py
+124 -1
View File
@@ -1,3 +1,5 @@
import importlib
import json
import os
import shutil
import subprocess
@@ -124,7 +126,7 @@ def llama_cpp_messages(model_file, n_gpu_layers, chat_handler=None, messages=[],
model = Llama(
model_path=model_file,
n_gpu_layers=n_gpu_layers,
n_ctx=options.get("n_ctx", 1024),
n_ctx=options.get("n_ctx", 2048),
logits_all=options.get("logits_all", False),
chat_handler=chat_handler,
chat_format=options.get("chat_format", None),
@@ -321,6 +323,127 @@ def llava_cpp_simple_interrogator(
high_quality_prompt = "((high quality:1.4), (best quality:1.4), (masterpiece:1.4), (8K resolution), (2k wallpaper))"
style_presets_prompt = {
"high_quality": high_quality_prompt,
"photography": f"{high_quality_prompt}, (RAW photo, best quality), (realistic, photo-realistic:1.2), (bokeh, cinematic shot, dynamic composition, incredibly detailed, sharpen, details, intricate detail, professional lighting, film lighting, 35mm, anamorphic, lightroom, cinematography, bokeh, lens flare, film grain, HDR10, 8K)",
"illustration": f"{high_quality_prompt}, ((detailed matte painting, intricate detail, splash screen, complementary colors), (detailed),(intricate details),illustration,an extremely delicate and beautiful,ultra-detailed,highres,extremely detailed)",
}
def get_style_presets():
return [
"high_quality",
"photography",
"illustration",
]
def base_query_beautify_prompt_text(model_file, n_gpu_layers, text, style_presets, options={}):
if options is None:
options = {}
import mz_prompts
importlib.reload(mz_prompts)
try:
schema = get_schema_obj(
keys_type={
"description": get_schema_base_type("string"),
"long_prompt": get_schema_base_type("string"),
"main_color_word": get_schema_base_type("string"),
"camera_angle_word": get_schema_base_type("string"),
"style_words": get_schema_array("string"),
"subject_words": get_schema_array("string"),
"light_words": get_schema_array("string"),
"environment_words": get_schema_array("string"),
},
required=[
"description",
"long_prompt",
"main_color_word",
"camera_angle_word",
"style_words",
"subject_words",
"light_words",
"environment_words",
]
)
options["max_tokens"] = options.get("max_tokens", 2048)
options["temperature"] = options.get("temperature", 1.6)
response_json = llama_cpp_simple_interrogator_to_json(
model_file=model_file,
n_gpu_layers=n_gpu_layers,
system=mz_prompts.Beautify_Prompt,
question=f"IDEA: {style_presets},{text}",
schema=schema,
options=options,
)
mz_prompt_utils.Utils.print_log(f"response_json: {response_json}")
response = json.loads(response_json)
freed_gpu_memory(model_file=model_file)
full_responses = []
if response["description"] != "":
full_responses.append(f"({response['description']})")
if response["long_prompt"] != "":
full_responses.append(f"({response['long_prompt']})")
if response["main_color_word"] != "":
full_responses.append(f"({response['main_color_word']})")
if response["camera_angle_word"] != "":
full_responses.append(f"({response['camera_angle_word']})")
response["style_words"] = [x for x in response["style_words"] if x != ""]
if len(response["style_words"]) > 0:
full_responses.append(f"({', '.join(response['style_words'])})")
response["subject_words"] = [x for x in response["subject_words"] if x != ""]
if len(response["subject_words"]) > 0:
full_responses.append(f"({', '.join(response['subject_words'])})")
response["light_words"] = [x for x in response["light_words"] if x != ""]
if len(response["light_words"]) > 0:
full_responses.append(f"({', '.join(response['light_words'])})")
response["environment_words"] = [x for x in response["environment_words"] if x != ""]
if len(response["environment_words"]) > 0:
full_responses.append(f"({', '.join(response['environment_words'])})")
full_response = ", ".join(full_responses)
# 去除换行
while full_response.find("\n") != -1:
full_response = full_response.replace("\n", " ")
# 句号换成逗号
while full_response.find(".") != -1:
full_response = full_response.replace(".", ",")
# 去除多余逗号
while full_response.find(",,") != -1:
full_response = full_response.replace(",,", ",")
while full_response.find(", ,") != -1:
full_response = full_response.replace(", ,", ",")
full_response = mz_prompt_utils.Utils.prompt_zh_to_en(full_response)
style_presets_prompt_text = style_presets_prompt.get(style_presets, "")
full_response = f"{style_presets_prompt_text}, {full_response}"
return full_response
except Exception as e:
freed_gpu_memory(model_file=model_file)
# mz_utils.Utils.print_log(f"Error in auto_prompt_text: {e}")
raise e
if __name__ == "__main__":
mz_llava.py
+15 -2
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@@ -49,8 +49,21 @@ def image_interrogator(model_name, mmproj_name, n_gpu_layers, image, resolution,
if options is None:
options = {}
image = mz_prompt_utils.Utils.resize_max(image, resolution, resolution)
model_file = get_exist_model(model_name)
mmproj_file = get_exist_model(mmproj_name)
model_file = None
mmproj_file = None
if options.get("customize_model_file", None) is not None:
model_file = options.get("customize_model_file")
if options.get("customize_mmproj_file", None) is not None:
mmproj_file = options.get("customize_mmproj_file")
if model_file is None:
model_file = get_exist_model(model_name)
if mmproj_file is None:
mmproj_file = get_exist_model(mmproj_name)
if model_file is None or mmproj_file is None:
if download_source == "modelscope":