import base64 import re import time from dataclasses import dataclass from functools import partial from io import BytesIO import gradio as gr import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import CLIPImageProcessor, CLIPVisionModel from modules import shared from modules.extensions import apply_extensions from modules.text_generation import encode, get_max_prompt_length params = { "add_all_images_to_prompt": False, # device to run CLIP on "clip_device": None, # bits to load clip in either 32 or 16 (it doesn't support 8-bit) "clip_bits": 32, # device to run projector on "projector_device": None, # projector bits, either 32 or 16 "projector_bits": 32 } # If 'state' is True, will hijack the next chat generation input_hijack = { 'state': False, 'value': ["", ""] } # initialized in ui, so that params are loaded from settings llava_embedder = None @dataclass class Token: token: str id: int class LLaVAEmbedder: IM_PATCH = Token("", 32000) IM_START = Token("", 32001) IM_END = Token("", 32002) CLIP_VIT_HUB_NAME = 'openai/clip-vit-large-patch14' PROJECTOR_HUB_NAME = 'liuhaotian/LLaVA-13b-pretrain-projector-v0' PROJECTOR_FILE = 'LLaVA-13b-pretrain-projector-v0-CC3M-595K-original_caption.bin' def __init__(self): self.clip_device = self._get_device("clip_device") self.clip_dtype = self._get_dtype("clip_bits") self.projector_device = self._get_device("projector_device") self.projector_dtype = self._get_dtype("projector_bits") self.image_processor, self.vision_tower, self.mm_projector = self._load_models() def _get_device(self, setting_name): if params[setting_name] is None: return torch.device("cuda:0" if torch.cuda.is_available() else "cpu") return torch.device(params[setting_name]) def _get_dtype(self, setting_name): return torch.float32 if int(params[setting_name]) == 32 else torch.float16 def _load_models(self): start_ts = time.time() print(f"LLaVA - Loading {LLaVAEmbedder.CLIP_VIT_HUB_NAME} as {self.clip_dtype} on {self.clip_device}...") image_processor = CLIPImageProcessor.from_pretrained(LLaVAEmbedder.CLIP_VIT_HUB_NAME, torch_dtype=self.clip_dtype) vision_tower = CLIPVisionModel.from_pretrained(LLaVAEmbedder.CLIP_VIT_HUB_NAME, torch_dtype=self.clip_dtype).to(self.clip_device) print(f"LLaVA - Loading {LLaVAEmbedder.PROJECTOR_HUB_NAME} as {self.projector_dtype} on {self.projector_device}...") projector_path = hf_hub_download(LLaVAEmbedder.PROJECTOR_HUB_NAME, LLaVAEmbedder.PROJECTOR_FILE) mm_projector = torch.nn.Linear(1024, 5120) projector_data = torch.load(projector_path) mm_projector.weight = torch.nn.Parameter(projector_data['model.mm_projector.weight'].to(dtype=self.projector_dtype), False) mm_projector.bias = torch.nn.Parameter(projector_data['model.mm_projector.bias'].to(dtype=self.projector_dtype), False) mm_projector = mm_projector.to(self.projector_device) print(f"LLaVA supporting models loaded, took {time.time() - start_ts:.2f} seconds") return image_processor, vision_tower, mm_projector def _update_prompt(self, prompt, images): for _ in images: # replace the image token with the image patch token in the prompt (each occurrence) replace_token = LLaVAEmbedder.IM_PATCH.token * 256 replace_token = LLaVAEmbedder.IM_START.token + replace_token + LLaVAEmbedder.IM_END.token prompt = re.sub(r'', replace_token, prompt, 1) return prompt def _extract_image_features(self, images): images = self.image_processor(images, return_tensors='pt')['pixel_values'] images = images.to(self.clip_device, dtype=self.clip_dtype) with torch.no_grad(): image_forward_outs = self.vision_tower(images, output_hidden_states=True) select_hidden_state_layer = -2 select_hidden_state = image_forward_outs.hidden_states[select_hidden_state_layer] image_features = select_hidden_state[:, 1:].to(self.projector_device, dtype=self.projector_dtype) image_features = self.mm_projector(image_features) return image_features def forward(self, prompt, images, state): prompt = self._update_prompt(prompt, images) input_ids = encode(prompt, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state))[0] input_embeds = shared.model.model.embed_tokens(input_ids).to(self.projector_device) if input_ids[0] == LLaVAEmbedder.IM_PATCH.id: # prompt got truncated in the middle of an image, remove the image data im_end = torch.where(input_ids == LLaVAEmbedder.IM_END.id)[0][0] input_ids = input_ids[im_end+1:] input_embeds = input_embeds[im_end+1:] leftover_images = torch.where(input_ids == LLaVAEmbedder.IM_START.id)[0].shape[0] print(f"LLaVA - WARNING: removed {len(images) - leftover_images} image(s) from prompt. The generation might be broken, try decreasing max_new_tokens") images = images[-leftover_images:] if len(images) == 0: return prompt, input_ids, input_embeds, 0 total_embedded = 0 image_features = self._extract_image_features(images).to(self.projector_device) image_start_tokens = torch.where(input_ids == LLaVAEmbedder.IM_START.id)[0] if not torch.any(input_ids == LLaVAEmbedder.IM_PATCH.id) or len(image_start_tokens) == 0: # multimodal LLM, but the current prompt is not multimodal/truncated return prompt, input_ids, input_embeds, total_embedded cur_image_idx = 0 if not params['add_all_images_to_prompt']: image_start_tokens = [image_start_tokens[-1]] cur_image_idx = -1 for image_start_token_pos in image_start_tokens: cur_image_features = image_features[cur_image_idx] num_patches = cur_image_features.shape[0] input_embeds = torch.cat((input_embeds[:image_start_token_pos+1], cur_image_features, input_embeds[image_start_token_pos + num_patches + 1:]), dim=0) cur_image_idx += 1 total_embedded += 1 return prompt, input_ids, input_embeds, total_embedded @staticmethod def len_in_tokens(text): images = re.findall(r'', text) image_tokens = 0 for _ in images: image_tokens += 258 return len(encode(re.sub(r'', '', text))[0]) + image_tokens def add_chat_picture(picture, text, visible_text): # resize the image, so that shortest edge is at least 224 (size for CLIP), and at most 300 (to keep history manageable) max_hw, min_hw = max(picture.size), min(picture.size) aspect_ratio = max_hw / min_hw shortest_edge = int(max(300 / aspect_ratio, 224)) longest_edge = int(shortest_edge * aspect_ratio) w = shortest_edge if picture.width < picture.height else longest_edge h = shortest_edge if picture.width >= picture.height else longest_edge picture = picture.resize((w,h)) buffer = BytesIO() picture.save(buffer, format="JPEG") img_str = base64.b64encode(buffer.getvalue()).decode('utf-8') image = f'' if '' in text: text = text.replace('', image) else: text = text + '\n' + image if visible_text == '' or visible_text is None: visible_text = text elif '' in visible_text: visible_text = visible_text.replace('', image) else: visible_text = visible_text + '\n' + image return text, visible_text def custom_generate_chat_prompt(user_input, state, **kwargs): impersonate = kwargs['impersonate'] if 'impersonate' in kwargs else False _continue = kwargs['_continue'] if '_continue' in kwargs else False also_return_rows = kwargs['also_return_rows'] if 'also_return_rows' in kwargs else False rows = [f"{state['context'].strip()}\n"] min_rows = 3 # Finding the maximum prompt size chat_prompt_size = state['chat_prompt_size'] if shared.soft_prompt: chat_prompt_size -= shared.soft_prompt_tensor.shape[1] max_length = min(get_max_prompt_length(state), chat_prompt_size) prefix1 = f"{state['name1']}: " prefix2 = f"{state['name2']}: " i = len(shared.history['internal']) - 1 while i >= 0 and LLaVAEmbedder.len_in_tokens(''.join(rows)) < max_length: if _continue and i == len(shared.history['internal']) - 1: rows.insert(1, f"{prefix2}{shared.history['internal'][i][1]}") else: rows.insert(1, f"{prefix2}{shared.history['internal'][i][1].strip()}{state['end_of_turn']}\n") string = shared.history['internal'][i][0] if string != '': rows.insert(1, f"{prefix1}{string.strip()}{state['end_of_turn']}\n") i -= 1 if impersonate: min_rows = 2 rows.append(f"{prefix1}") elif not _continue: # Adding the user message if len(user_input) > 0: rows.append(f"{prefix1}{user_input}{state['end_of_turn']}\n") # Adding the Character prefix rows.append(apply_extensions("bot_prefix", f"{prefix2}")) while len(rows) > min_rows and LLaVAEmbedder.len_in_tokens(''.join(rows)) >= max_length: rows.pop(1) prompt = ''.join(rows) if also_return_rows: return prompt, rows else: return prompt def tokenizer_modifier(state, prompt, input_ids, input_embeds): global params start_ts = time.time() image_matches = re.finditer(r'', prompt) images = [Image.open(BytesIO(base64.b64decode(match.group(1)))) for match in image_matches] if len(images) == 0: return prompt, input_ids, input_embeds prompt, input_ids, input_embeds, total_embedded = llava_embedder.forward(prompt, images, state) print(f'LLaVA - Embedded {total_embedded} image(s) in {time.time()-start_ts:.2f}s') return prompt, input_ids.unsqueeze(0).to(shared.model.device), input_embeds.unsqueeze(0).to(shared.model.device) def ui(): global llava_embedder llava_embedder = LLaVAEmbedder() with gr.Column(): picture_select = gr.Image(label='Send a picture', type='pil') # I found that it doesn't deal super well with multiple images, and demo ui had a bug where it included only the last image anyway single_image_checkbox = gr.Checkbox(False, label='Embed all images, not only the last one') # Prepare the input hijack picture_select.upload( lambda picture: input_hijack.update({"state": True, "value": partial(add_chat_picture, picture)}), [picture_select], None ) picture_select.clear(lambda: input_hijack.update({"state": False, "value": ["",""]}), None, None) single_image_checkbox.change(lambda x: params.update({"add_all_images_to_prompt": x}), single_image_checkbox, None) shared.gradio['Generate'].click(lambda: None, None, picture_select) shared.gradio['textbox'].submit(lambda: None, None, picture_select)