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scripts/expansion.py

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+# Fooocus GPT2 Expansion
+# Algorithm created by Lvmin Zhang at 2023, Stanford 
+# modified by PlayDystinDB and GPT-4O for stable-diffusion-webui
+# If used inside Fooocus, any use is permitted.
+# If used outside Fooocus, only non-commercial use is permitted (CC-By NC 4.0).
+# This applies to the word list, vocab, model, and algorithm.
+
+
+import os
+import torch
+import math
+import gradio as gr
+import psutil
+
+from modules.scripts import basedir
+from transformers.generation.logits_process import LogitsProcessorList
+from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
+from modules import scripts, shared, script_callbacks
+from modules.ui_components import FormRow, FormColumn, FormGroup, ToolButton
+
+
+def text_encoder_device():
+    if torch.cuda.is_available():
+        return torch.device(torch.cuda.current_device())
+    else:
+        return torch.device("cpu")
+
+def text_encoder_offload_device():
+    if torch.cuda.is_available():
+        return torch.device(torch.cuda.current_device())
+    else:
+        return torch.device("cpu")
+
+def get_free_memory(dev=None, torch_free_too=False):
+    global directml_enabled
+    if dev is None:
+        dev = text_encoder_device()
+
+    if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):
+        mem_free_total = psutil.virtual_memory().available
+        mem_free_torch = mem_free_total
+    else:
+        if directml_enabled:
+            mem_free_total = 1024 * 1024 * 1024 #TODO
+            mem_free_torch = mem_free_total
+        else:
+            stats = torch.cuda.memory_stats(dev)
+            mem_active = stats['active_bytes.all.current']
+            mem_reserved = stats['reserved_bytes.all.current']
+            mem_free_cuda, _ = torch.cuda.mem_get_info(dev)
+            mem_free_torch = mem_reserved - mem_active
+            mem_free_total = mem_free_cuda + mem_free_torch
+
+
+
+# limitation of np.random.seed(), called from transformers.set_seed()
+SEED_LIMIT_NUMPY = 2**32
+neg_inf = - 8192.0
+ext_dir = basedir()
+path_fooocus_expansion = os.path.join('.', "models", "prompt_expansion")
+
+
+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
+
+def should_use_fp16(device=None, model_params=0, prioritize_performance=True):
+    if device is not None:
+        if hasattr(device, 'type'):
+            if device.type == 'cpu':
+                return False
+        return False
+    if torch.cuda.is_bf16_supported():
+        return True
+    props = torch.cuda.get_device_properties("cuda")
+    if props.major < 6:
+        return False
+
+    fp16_works = False
+    #FP16 is confirmed working on a 1080 (GP104) but it's a bit slower than FP32 so it should only be enabled
+    #when the model doesn't actually fit on the card
+    #TODO: actually test if GP106 and others have the same type of behavior
+    nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050"]
+    for x in nvidia_10_series:
+        if x in props.name.lower():
+            fp16_works = True
+
+    if fp16_works:
+        free_model_memory = (get_free_memory() * 0.9 - (1024 * 1024 * 1024))
+        if (not prioritize_performance) or model_params * 4 > free_model_memory:
+            return True
+
+    if props.major < 7:
+        return False
+
+    #FP16 is just broken on these cards
+    nvidia_16_series = ["1660", "1650", "1630", "T500", "T550", "T600", "MX550", "MX450", "CMP 30HX", "T2000", "T1000", "T1200"]
+    for x in nvidia_16_series:
+        if x in props.name:
+            return False
+
+    return True
+
+def is_device_mps(device):
+    if hasattr(device, 'type'):
+        if (device.type == 'mps'):
+            return True
+    return False 
+
+class FooocusExpansion:
+    def __init__(self):
+        global load_model_device
+        print(f'Loading models from {path_fooocus_expansion}')
+        self.tokenizer = AutoTokenizer.from_pretrained(path_fooocus_expansion)
+
+        positive_words = open(os.path.join(path_fooocus_expansion, 'positive.txt'),
+                              encoding='utf-8').read().splitlines()
+        positive_words = ['Ġ' + x.lower() for x in positive_words if x != '']
+
+        self.logits_bias = torch.zeros((1, len(self.tokenizer.vocab)), dtype=torch.float32) + neg_inf
+
+        debug_list = []
+        for k, v in self.tokenizer.vocab.items():
+            if k in positive_words:
+                self.logits_bias[0, v] = 0
+                debug_list.append(k[1:])
+
+        print(f'Fooocus V2 Expansion: Vocab with {len(debug_list)} words.')
+
+        self.model = AutoModelForCausalLM.from_pretrained(path_fooocus_expansion)
+        self.model.eval()
+
+        load_model_device = text_encoder_device()
+        offload_device = text_encoder_offload_device()
+
+        # MPS hack
+        if is_device_mps(load_model_device):
+            load_model_device = torch.device('cpu')
+            offload_device = torch.device('cpu')
+
+        use_fp16 = should_use_fp16(device=load_model_device)
+
+        if use_fp16:
+            self.model.half()
+
+        self.model.to(load_model_device)  # Ensure model is on the correct device
+
+        print(f'Fooocus Expansion engine loaded for {load_model_device}, use_fp16 = {use_fp16}.')
+
+    @torch.no_grad()
+    @torch.inference_mode()
+    def logits_processor(self, input_ids, scores):
+        assert scores.ndim == 2 and scores.shape[0] == 1
+        self.logits_bias = self.logits_bias.to(load_model_device)
+
+        bias = self.logits_bias.clone().to(load_model_device)  # Ensure bias is on the correct device
+        bias[0, input_ids[0].to(load_model_device).long()] = neg_inf  # Ensure input_ids are on the correct device
+        bias[0, 11] = 0
+
+        return scores + bias.to(scores.device)  # Ensure bias is on the same device as scores
+
+    @torch.no_grad()
+    @torch.inference_mode()
+    def __call__(self, prompt, seed):
+        if prompt == '':
+            return ''
+
+        seed = int(seed) % SEED_LIMIT_NUMPY
+        set_seed(seed)
+        prompt = safe_str(prompt) + ','
+        tokenized_kwargs = self.tokenizer(prompt, return_tensors="pt")
+        tokenized_kwargs.data['input_ids'] = tokenized_kwargs.data['input_ids'].to(load_model_device)
+        tokenized_kwargs.data['attention_mask'] = tokenized_kwargs.data['attention_mask'].to(load_model_device)
+
+        current_token_length = int(tokenized_kwargs.data['input_ids'].shape[1])
+        max_token_length = 75 * int(math.ceil(float(current_token_length) / 75.0))
+        max_new_tokens = max_token_length - current_token_length
+
+        features = self.model.generate(**tokenized_kwargs,
+                                       top_k=100,
+                                       max_new_tokens=max_new_tokens,
+                                       do_sample=True,
+                                       logits_processor=LogitsProcessorList([self.logits_processor]))
+
+        response = self.tokenizer.batch_decode(features, skip_special_tokens=True)
+        result = safe_str(response[0])
+
+        return result
+
+def createPositive(positive, seed):
+    try:
+        expansion = FooocusExpansion()
+        positive = expansion(positive, seed=seed)
+        return positive
+    except Exception as e:
+        print(f"An error occurred: {str(e)}")
+
+class FooocusPromptExpansion(scripts.Script):
+    def __init__(self) -> None:
+        super().__init__()
+    
+    def title(self):
+        return 'Fooocus Expansion'
+    
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+    
+    def ui(self, is_img2img):
+        with gr.Group():
+            with gr.Accordion("Fooocus Expansion", open=True):
+                is_enabled = gr.Checkbox(
+                    value=True, label="Enable Expansion", info="Enable Or Disable Expansion ")
+                seed = gr.Number(
+                    value=0, maximum=63, label="Seed", info="Seed for random number generator")
+        return [is_enabled, seed]
+    
+    def process(self, p, is_enabled, seed):
+        if not is_enabled:
+            return
+
+        for i, prompt in enumerate(p.all_prompts):
+            positivePrompt = createPositive(prompt, seed)
+            p.all_prompts[i] = positivePrompt
+
+        
+    
+    def after_component(self, component, **kwargs):
+        # https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/7456#issuecomment-1414465888 helpfull link
+        # Find the text2img textbox component
+        if kwargs.get("elem_id") == "txt2img_prompt":  # postive prompt textbox
+            self.boxx = component
+        # Find the img2img textbox component
+        if kwargs.get("elem_id") == "img2img_prompt":  # postive prompt textbox
+            self.boxxIMG = component