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#!/usr/bin/env python
import os
import random
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
if __package__ is None or __package__ == '':
# A1111 style (standalone script or direct module execution)
# Use absolute imports for compatibility with A1111 WebUI environment
from download_models import download_models
else:
# ComfyUI style (imported as a package)
# Use relative imports for proper integration with ComfyUI
from .download_models import download_models
global tokenizer, model
script_dir = os.path.dirname(os.path.abspath(__file__)) # Script directory
modelDir = os.path.join(script_dir, "./model_files/" )
def load_models():
if not all(os.path.exists(modelDir) for file in modelDir):
print("Model files not found. Downloading...\n")
download_models()
# else:
# print("Model files found. Skipping download.\n")
# print("Loading SuperPrompt-v1 model...\n")
global tokenizer, model
tokenizer = T5Tokenizer.from_pretrained(modelDir)
model = T5ForConditionalGeneration.from_pretrained(modelDir, torch_dtype=torch.float16)
# print("SuperPrompt-v1 model loaded successfully.\n")
def unload_models():
global tokenizer, model
del tokenizer
del model
for file in os.listdir(modelDir):
os.remove(os.path.join(modelDir, file))
os.rmdir(modelDir)
def answer(input_text="", max_new_tokens=512, repetition_penalty=1.2, temperature=0.5, top_p=1, top_k = 1 , seed=-1):
if seed == -1:
seed = random.randint(1, 1000000)
torch.manual_seed(seed)
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
if torch.cuda.is_available():
model.to('cuda')
outputs = model.generate(input_ids, max_new_tokens=max_new_tokens, repetition_penalty=repetition_penalty,
do_sample=True, temperature=temperature, top_p=top_p, top_k=top_k)
dirty_text = tokenizer.decode(outputs[0])
text = dirty_text.replace("<pad>", "").replace("</s>", "").strip()
return text
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