78 lines
2.6 KiB
Python
78 lines
2.6 KiB
Python
import torch
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import comfy.model_management as model_management
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from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
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from modules.path import fooocus_expansion_path
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from comfy.model_patcher import ModelPatcher
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fooocus_magic_split = [
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', extremely',
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', intricate,',
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]
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dangrous_patterns = '[]【】()（）|:：'
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def safe_str(x):
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x = str(x)
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for _ in range(16):
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x = x.replace(' ', ' ')
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return x.strip(",. \r\n")
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def remove_pattern(x, pattern):
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for p in pattern:
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x = x.replace(p, '')
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return x
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class FooocusExpansion:
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def __init__(self):
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self.tokenizer = AutoTokenizer.from_pretrained(fooocus_expansion_path)
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self.model = AutoModelForCausalLM.from_pretrained(fooocus_expansion_path)
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self.model.eval()
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load_device = model_management.text_encoder_device()
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offload_device = model_management.text_encoder_offload_device()
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# MPS hack
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if model_management.is_device_mps(load_device):
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load_device = torch.device('cpu')
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offload_device = torch.device('cpu')
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use_fp16 = model_management.should_use_fp16(device=load_device)
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if use_fp16:
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self.model.half()
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self.patcher = ModelPatcher(self.model, load_device=load_device, offload_device=offload_device)
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print(f'Fooocus Expansion engine loaded for {load_device}, use_fp16 = {use_fp16}.')
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def __call__(self, prompt, seed):
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if self.patcher.current_device != self.patcher.load_device:
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print('Fooocus Expansion loaded by itself.')
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model_management.load_model_gpu(self.patcher)
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seed = int(seed)
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set_seed(seed)
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origin = safe_str(prompt)
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prompt = origin + fooocus_magic_split[seed % len(fooocus_magic_split)]
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tokenized_kwargs = self.tokenizer(prompt, return_tensors="pt")
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tokenized_kwargs.data['input_ids'] = tokenized_kwargs.data['input_ids'].to(self.patcher.load_device)
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tokenized_kwargs.data['attention_mask'] = tokenized_kwargs.data['attention_mask'].to(self.patcher.load_device)
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# https://huggingface.co/blog/introducing-csearch
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# https://huggingface.co/docs/transformers/generation_strategies
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features = self.model.generate(**tokenized_kwargs,
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num_beams=1,
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max_new_tokens=256,
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do_sample=True)
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response = self.tokenizer.batch_decode(features, skip_special_tokens=True)
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result = response[0][len(origin):]
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result = safe_str(result)
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result = remove_pattern(result, dangrous_patterns)
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return result
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