mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-22 16:17:57 +01:00
80c2f25131
* change multimodal projector to the correct one * remove reference to custom stopping strings from readme * fix stopping strings if tokenizer extension adds/removes tokens * add API example * LLaVA 7B just dropped, add to readme that there is no support for it currently
273 lines
11 KiB
Python
273 lines
11 KiB
Python
import base64
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import re
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import time
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from dataclasses import dataclass
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from functools import partial
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from io import BytesIO
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import gradio as gr
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import torch
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from huggingface_hub import hf_hub_download
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from PIL import Image
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from transformers import CLIPImageProcessor, CLIPVisionModel
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from modules import shared
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from modules.extensions import apply_extensions
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from modules.text_generation import encode, get_max_prompt_length
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params = {
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"add_all_images_to_prompt": False,
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# device to run CLIP on
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"clip_device": None,
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# bits to load clip in either 32 or 16 (it doesn't support 8-bit)
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"clip_bits": 32,
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# clip repository
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"clip_repo": "openai/clip-vit-large-patch14",
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# device to run projector on
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"projector_device": None,
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# projector bits, either 32 or 16
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"projector_bits": 32,
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# projector repository
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"projector_repo": "liuhaotian/LLaVA-13b-delta-v0",
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# file with the projector weights
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"projector_file": "mm_projector.bin"
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}
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# If 'state' is True, will hijack the next chat generation
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input_hijack = {
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'state': False,
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'value': ["", ""]
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}
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# initialized in ui, so that params are loaded from settings
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llava_embedder = None
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@dataclass
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class Token:
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token: str
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id: int
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class LLaVAEmbedder:
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IM_PATCH = Token("<im_patch>", 32000)
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IM_START = Token("<im_start>", 32001)
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IM_END = Token("<im_end>", 32002)
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def __init__(self):
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self.clip_device = self._get_device("clip_device")
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self.clip_dtype = self._get_dtype("clip_bits")
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self.projector_device = self._get_device("projector_device")
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self.projector_dtype = self._get_dtype("projector_bits")
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self.image_processor, self.vision_tower, self.mm_projector = self._load_models()
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def _get_device(self, setting_name):
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if params[setting_name] is None:
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return torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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return torch.device(params[setting_name])
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def _get_dtype(self, setting_name):
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return torch.float32 if int(params[setting_name]) == 32 else torch.float16
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def _load_models(self):
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start_ts = time.time()
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print(f"LLaVA - Loading CLIP from {params['clip_repo']} as {self.clip_dtype} on {self.clip_device}...")
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image_processor = CLIPImageProcessor.from_pretrained(params["clip_repo"], torch_dtype=self.clip_dtype)
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vision_tower = CLIPVisionModel.from_pretrained(params["clip_repo"], torch_dtype=self.clip_dtype).to(self.clip_device)
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print(f"LLaVA - Loading projector from {params['projector_repo']} as {self.projector_dtype} on {self.projector_device}...")
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projector_path = hf_hub_download(params["projector_repo"], params["projector_file"])
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mm_projector = torch.nn.Linear(1024, 5120)
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projector_data = torch.load(projector_path)
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mm_projector.weight = torch.nn.Parameter(projector_data['model.mm_projector.weight'].to(dtype=self.projector_dtype), False)
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mm_projector.bias = torch.nn.Parameter(projector_data['model.mm_projector.bias'].to(dtype=self.projector_dtype), False)
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mm_projector = mm_projector.to(self.projector_device)
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print(f"LLaVA supporting models loaded, took {time.time() - start_ts:.2f} seconds")
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return image_processor, vision_tower, mm_projector
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def _update_prompt(self, prompt, images):
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for _ in images:
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# replace the image token with the image patch token in the prompt (each occurrence)
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replace_token = LLaVAEmbedder.IM_PATCH.token * 256
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replace_token = LLaVAEmbedder.IM_START.token + replace_token + LLaVAEmbedder.IM_END.token
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prompt = re.sub(r'<img src="data:image/jpeg;base64,([A-Za-z0-9+/=]+)">', replace_token, prompt, 1)
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return prompt
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def _extract_image_features(self, images):
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images = self.image_processor(images, return_tensors='pt')['pixel_values']
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images = images.to(self.clip_device, dtype=self.clip_dtype)
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with torch.no_grad():
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image_forward_outs = self.vision_tower(images, output_hidden_states=True)
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select_hidden_state_layer = -2
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select_hidden_state = image_forward_outs.hidden_states[select_hidden_state_layer]
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image_features = select_hidden_state[:, 1:].to(self.projector_device, dtype=self.projector_dtype)
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image_features = self.mm_projector(image_features)
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return image_features
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def forward(self, prompt, images, state):
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prompt = self._update_prompt(prompt, images)
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input_ids = encode(prompt, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state))[0]
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input_embeds = shared.model.model.embed_tokens(input_ids).to(self.projector_device)
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if input_ids[0] == LLaVAEmbedder.IM_PATCH.id:
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# prompt got truncated in the middle of an image, remove the image data
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im_end = torch.where(input_ids == LLaVAEmbedder.IM_END.id)[0][0]
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input_ids = input_ids[im_end+1:]
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input_embeds = input_embeds[im_end+1:]
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leftover_images = torch.where(input_ids == LLaVAEmbedder.IM_START.id)[0].shape[0]
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print(f"LLaVA - WARNING: removed {len(images) - leftover_images} image(s) from prompt. The generation might be broken, try decreasing max_new_tokens")
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images = images[-leftover_images:]
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if len(images) == 0:
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return prompt, input_ids, input_embeds, 0
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total_embedded = 0
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image_features = self._extract_image_features(images).to(self.projector_device)
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image_start_tokens = torch.where(input_ids == LLaVAEmbedder.IM_START.id)[0]
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if not torch.any(input_ids == LLaVAEmbedder.IM_PATCH.id) or len(image_start_tokens) == 0:
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# multimodal LLM, but the current prompt is not multimodal/truncated
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return prompt, input_ids, input_embeds, total_embedded
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cur_image_idx = 0
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if not params['add_all_images_to_prompt']:
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image_start_tokens = [image_start_tokens[-1]]
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cur_image_idx = -1
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for image_start_token_pos in image_start_tokens:
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cur_image_features = image_features[cur_image_idx]
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num_patches = cur_image_features.shape[0]
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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)
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cur_image_idx += 1
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total_embedded += 1
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return prompt, input_ids, input_embeds, total_embedded
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@staticmethod
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def len_in_tokens(text):
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images = re.findall(r'<img src="data:image/jpeg;base64,[A-Za-z0-9+/=]+">', text)
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image_tokens = 0
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for _ in images:
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image_tokens += 258
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return len(encode(re.sub(r'<img src="data:image/jpeg;base64,[A-Za-z0-9+/=]+">', '', text))[0]) + image_tokens
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def add_chat_picture(picture, text, visible_text):
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# resize the image, so that shortest edge is at least 224 (size for CLIP), and at most 300 (to keep history manageable)
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max_hw, min_hw = max(picture.size), min(picture.size)
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aspect_ratio = max_hw / min_hw
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shortest_edge = int(max(300 / aspect_ratio, 224))
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longest_edge = int(shortest_edge * aspect_ratio)
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w = shortest_edge if picture.width < picture.height else longest_edge
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h = shortest_edge if picture.width >= picture.height else longest_edge
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picture = picture.resize((w,h))
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buffer = BytesIO()
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picture.save(buffer, format="JPEG")
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img_str = base64.b64encode(buffer.getvalue()).decode('utf-8')
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image = f'<img src="data:image/jpeg;base64,{img_str}">'
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if '<image>' in text:
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text = text.replace('<image>', image)
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else:
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text = text + '\n' + image
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if visible_text == '' or visible_text is None:
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visible_text = text
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elif '<image>' in visible_text:
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visible_text = visible_text.replace('<image>', image)
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else:
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visible_text = visible_text + '\n' + image
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return text, visible_text
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def custom_generate_chat_prompt(user_input, state, **kwargs):
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impersonate = kwargs['impersonate'] if 'impersonate' in kwargs else False
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_continue = kwargs['_continue'] if '_continue' in kwargs else False
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also_return_rows = kwargs['also_return_rows'] if 'also_return_rows' in kwargs else False
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rows = [f"{state['context'].strip()}\n"]
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min_rows = 3
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# Finding the maximum prompt size
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chat_prompt_size = state['chat_prompt_size']
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if shared.soft_prompt:
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chat_prompt_size -= shared.soft_prompt_tensor.shape[1]
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max_length = min(get_max_prompt_length(state), chat_prompt_size)
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prefix1 = f"{state['name1']}: "
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prefix2 = f"{state['name2']}: "
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i = len(shared.history['internal']) - 1
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while i >= 0 and LLaVAEmbedder.len_in_tokens(''.join(rows)) < max_length:
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if _continue and i == len(shared.history['internal']) - 1:
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rows.insert(1, f"{prefix2}{shared.history['internal'][i][1]}")
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else:
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rows.insert(1, f"{prefix2}{shared.history['internal'][i][1].strip()}\n")
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string = shared.history['internal'][i][0]
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if string != '':
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rows.insert(1, f"{prefix1}{string.strip()}\n")
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i -= 1
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if impersonate:
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min_rows = 2
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rows.append(f"{prefix1}")
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elif not _continue:
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# Adding the user message
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if len(user_input) > 0:
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rows.append(f"{prefix1}{user_input}\n")
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# Adding the Character prefix
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rows.append(apply_extensions("bot_prefix", f"{prefix2}"))
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while len(rows) > min_rows and LLaVAEmbedder.len_in_tokens(''.join(rows)) >= max_length:
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rows.pop(1)
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prompt = ''.join(rows)
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if also_return_rows:
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return prompt, rows
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else:
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return prompt
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def tokenizer_modifier(state, prompt, input_ids, input_embeds):
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global params
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start_ts = time.time()
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image_matches = re.finditer(r'<img src="data:image/jpeg;base64,([A-Za-z0-9+/=]+)">', prompt)
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images = [Image.open(BytesIO(base64.b64decode(match.group(1)))) for match in image_matches]
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if len(images) == 0:
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return prompt, input_ids, input_embeds
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prompt, input_ids, input_embeds, total_embedded = llava_embedder.forward(prompt, images, state)
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print(f'LLaVA - Embedded {total_embedded} image(s) in {time.time()-start_ts:.2f}s')
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return (prompt,
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input_ids.unsqueeze(0).to(shared.model.device, dtype=torch.int64),
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input_embeds.unsqueeze(0).to(shared.model.device, dtype=shared.model.dtype))
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def ui():
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global llava_embedder
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llava_embedder = LLaVAEmbedder()
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with gr.Column():
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picture_select = gr.Image(label='Send a picture', type='pil')
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# 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
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single_image_checkbox = gr.Checkbox(False, label='Embed all images, not only the last one')
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# Prepare the input hijack
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picture_select.upload(
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lambda picture: input_hijack.update({"state": True, "value": partial(add_chat_picture, picture)}),
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[picture_select],
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None
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)
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picture_select.clear(lambda: input_hijack.update({"state": False, "value": ["",""]}), None, None)
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single_image_checkbox.change(lambda x: params.update({"add_all_images_to_prompt": x}), single_image_checkbox, None)
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shared.gradio['Generate'].click(lambda: None, None, picture_select)
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shared.gradio['textbox'].submit(lambda: None, None, picture_select)
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