f8ca04a406
closes https://github.com/lllyasviel/Fooocus/issues/2278, also removes comma at the end added before tokenizer
130 lines
4.6 KiB
Python
130 lines
4.6 KiB
Python
# Fooocus GPT2 Expansion
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# Algorithm created by Lvmin Zhang at 2023, Stanford
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# If used inside Fooocus, any use is permitted.
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# If used outside Fooocus, only non-commercial use is permitted (CC-By NC 4.0).
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# This applies to the word list, vocab, model, and algorithm.
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import os
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import torch
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import math
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import ldm_patched.modules.model_management as model_management
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from transformers.generation.logits_process import LogitsProcessorList
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from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
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from modules.config import path_fooocus_expansion
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from ldm_patched.modules.model_patcher import ModelPatcher
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# limitation of np.random.seed(), called from transformers.set_seed()
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SEED_LIMIT_NUMPY = 2**32
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neg_inf = - 8192.0
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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(path_fooocus_expansion)
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positive_words = open(os.path.join(path_fooocus_expansion, 'positive.txt'),
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encoding='utf-8').read().splitlines()
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positive_words = ['Ġ' + x.lower() for x in positive_words if x != '']
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self.logits_bias = torch.zeros((1, len(self.tokenizer.vocab)), dtype=torch.float32) + neg_inf
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debug_list = []
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for k, v in self.tokenizer.vocab.items():
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if k in positive_words:
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self.logits_bias[0, v] = 0
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debug_list.append(k[1:])
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print(f'Fooocus V2 Expansion: Vocab with {len(debug_list)} words.')
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# debug_list = '\n'.join(sorted(debug_list))
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# print(debug_list)
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# t11 = self.tokenizer(',', return_tensors="np")
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# t198 = self.tokenizer('\n', return_tensors="np")
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# eos = self.tokenizer.eos_token_id
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self.model = AutoModelForCausalLM.from_pretrained(path_fooocus_expansion)
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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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@torch.no_grad()
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@torch.inference_mode()
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def logits_processor(self, input_ids, scores):
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assert scores.ndim == 2 and scores.shape[0] == 1
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self.logits_bias = self.logits_bias.to(scores)
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bias = self.logits_bias.clone()
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bias[0, input_ids[0].to(bias.device).long()] = neg_inf
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bias[0, 11] = 0
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return scores + bias
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@torch.no_grad()
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@torch.inference_mode()
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def __call__(self, prompt, seed):
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if prompt == '':
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return ''
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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) % SEED_LIMIT_NUMPY
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set_seed(seed)
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prompt = safe_str(prompt) + ','
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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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current_token_length = int(tokenized_kwargs.data['input_ids'].shape[1])
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max_token_length = 75 * int(math.ceil(float(current_token_length) / 75.0))
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max_new_tokens = max_token_length - current_token_length
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if max_new_tokens == 0:
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return prompt[:-1]
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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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top_k=100,
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max_new_tokens=max_new_tokens,
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do_sample=True,
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logits_processor=LogitsProcessorList([self.logits_processor]))
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response = self.tokenizer.batch_decode(features, skip_special_tokens=True)
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result = safe_str(response[0])
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return result
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