nodes/meap158-ComfyUI-Prompt-Expansion
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HasegawaSkip
2023-09-16 19:05:03 +07:00
commit cc7532502d
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.gitignore
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# Ignore _pycache directories
__pycache__/
# Ignore all files with .bin extension
*.bin
__init__.py
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from .prompt_expansion import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
from .prompt_expansion import fooocus_expansion_path
from .model_loader import load_file_from_url
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
def download_models():
url = 'https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_expansion.bin'
model_dir = fooocus_expansion_path
file_name = 'pytorch_model.bin'
load_file_from_url(url=url, model_dir=model_dir, file_name=file_name)
download_models()
fooocus_expansion/config.json
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{
"_name_or_path": "gpt2",
"activation_function": "gelu_new",
"architectures": [
"GPT2LMHeadModel"
],
"attn_pdrop": 0.1,
"bos_token_id": 50256,
"embd_pdrop": 0.1,
"eos_token_id": 50256,
"pad_token_id": 50256,
"initializer_range": 0.02,
"layer_norm_epsilon": 1e-05,
"model_type": "gpt2",
"n_ctx": 1024,
"n_embd": 768,
"n_head": 12,
"n_inner": null,
"n_layer": 12,
"n_positions": 1024,
"reorder_and_upcast_attn": false,
"resid_pdrop": 0.1,
"scale_attn_by_inverse_layer_idx": false,
"scale_attn_weights": true,
"summary_activation": null,
"summary_first_dropout": 0.1,
"summary_proj_to_labels": true,
"summary_type": "cls_index",
"summary_use_proj": true,
"task_specific_params": {
"text-generation": {
"do_sample": true,
"max_length": 50
}
},
"torch_dtype": "float32",
"transformers_version": "4.23.0.dev0",
"use_cache": true,
"vocab_size": 50257
}
fooocus_expansion/merges.txt
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fooocus_expansion/special_tokens_map.json
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{
"bos_token": "<|endoftext|>",
"eos_token": "<|endoftext|>",
"unk_token": "<|endoftext|>"
}
fooocus_expansion/tokenizer.json
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fooocus_expansion/tokenizer_config.json
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{
"add_prefix_space": false,
"bos_token": "<|endoftext|>",
"eos_token": "<|endoftext|>",
"model_max_length": 1024,
"name_or_path": "gpt2",
"special_tokens_map_file": null,
"tokenizer_class": "GPT2Tokenizer",
"unk_token": "<|endoftext|>"
}
fooocus_expansion/vocab.json
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model_loader.py
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import os
from urllib.parse import urlparse
from typing import Optional
def load_file_from_url(
url: str,
*,
model_dir: str,
progress: bool = True,
file_name: Optional[str] = None,
) -> str:
"""Download a file from `url` into `model_dir`, using the file present if possible.
Returns the path to the downloaded file.
"""
os.makedirs(model_dir, exist_ok=True)
if not file_name:
parts = urlparse(url)
file_name = os.path.basename(parts.path)
cached_file = os.path.abspath(os.path.join(model_dir, file_name))
if not os.path.exists(cached_file):
print(f'Downloading: "{url}" to {cached_file}\n')
from torch.hub import download_url_to_file
download_url_to_file(url, cached_file, progress=progress)
return cached_file
prompt_expansion.py
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import os
import random
import sys
import torch
# Get the parent directory of 'comfy' and add it to the Python path
comfy_parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../'))
sys.path.append(comfy_parent_dir)
# Suppress console output
original_stdout = sys.stdout
sys.stdout = open(os.devnull, 'w')
# Import the required modules
import comfy.model_management as model_management
from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
from comfy.model_patcher import ModelPatcher
from .util import join_prompts, remove_empty_str
# Restore the original stdout
sys.stdout = original_stdout
fooocus_expansion_path = os.path.abspath(os.path.join(os.path.dirname(__file__),
'fooocus_expansion'))
fooocus_magic_split = [
', extremely',
', intricate,',
]
dangrous_patterns = '[]【】()（）|:：'
def safe_str(x):
x = str(x)
for _ in range(16):
x = x.replace(' ', ' ')
return x.strip(",. \r\n")
def remove_pattern(x, pattern):
for p in pattern:
x = x.replace(p, '')
return x
class FooocusExpansion:
def __init__(self):
self.tokenizer = AutoTokenizer.from_pretrained(fooocus_expansion_path)
self.model = AutoModelForCausalLM.from_pretrained(fooocus_expansion_path)
self.model.eval()
load_device = model_management.text_encoder_device()
if 'mps' in load_device.type:
load_device = torch.device('cpu')
if 'cpu' not in load_device.type and model_management.should_use_fp16():
self.model.half()
offload_device = model_management.text_encoder_offload_device()
self.patcher = ModelPatcher(self.model, load_device=load_device, offload_device=offload_device)
# print(f'Fooocus Expansion engine loaded for {load_device}.')
def __call__(self, prompt, seed):
model_management.load_model_gpu(self.patcher)
seed = int(seed)
set_seed(seed)
origin = safe_str(prompt)
prompt = origin + fooocus_magic_split[seed % len(fooocus_magic_split)]
tokenized_kwargs = self.tokenizer(prompt, return_tensors="pt")
tokenized_kwargs.data['input_ids'] = tokenized_kwargs.data['input_ids'].to(self.patcher.load_device)
tokenized_kwargs.data['attention_mask'] = tokenized_kwargs.data['attention_mask'].to(self.patcher.load_device)
# https://huggingface.co/blog/introducing-csearch
# https://huggingface.co/docs/transformers/generation_strategies
features = self.model.generate(**tokenized_kwargs,
num_beams=1,
max_new_tokens=256,
do_sample=True)
response = self.tokenizer.batch_decode(features, skip_special_tokens=True)
result = response[0][len(origin):]
result = safe_str(result)
result = remove_pattern(result, dangrous_patterns)
return result
class PromptExpansion:
# Define the expected input types for the node
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"text": ("STRING", {"multiline": True}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFF}),
"log_prompt": (["No", "Yes"], {"default": "No"}),
},
}
RETURN_TYPES = ("STRING", "INT",)
RETURN_NAMES = ("final_prompt", "seed",)
FUNCTION = "expand_prompt" # Function name
CATEGORY = "utils" # Category for organization
@staticmethod
@torch.no_grad()
def expand_prompt(text, seed, log_prompt):
expansion = FooocusExpansion()
prompt = remove_empty_str([safe_str(text)], default='')[0]
max_seed = int(1024 * 1024 * 1024)
if not isinstance(seed, int):
seed = random.randint(1, max_seed)
if seed < 0:
seed = - seed
seed = seed % max_seed
expansion_text = expansion(prompt, seed)
final_prompt = join_prompts(prompt, expansion_text)
if log_prompt == "Yes":
print(f"[Prompt Expansion] New suffix: {expansion_text}")
print(f"Final prompt: {final_prompt}")
return final_prompt, seed
# Define a mapping of node class names to their respective classes
NODE_CLASS_MAPPINGS = {
"PromptExpansion": PromptExpansion
}
# A dictionary that contains human-readable titles for the nodes
NODE_DISPLAY_NAME_MAPPINGS = {
"PromptExpansion": "Prompt Expansion"
}
util.py
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def remove_empty_str(items, default=None):
items = [x for x in items if x != ""]
if len(items) == 0 and default is not None:
return [default]
return items
def join_prompts(*args, **kwargs):
prompts = [str(x) for x in args if str(x) != ""]
if len(prompts) == 0:
return ""
if len(prompts) == 1:
return prompts[0]
return ', '.join(prompts)
workflow_example.json
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"last_node_id": 22,
"last_link_id": 41,
"nodes": [
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workflow_example.png
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