import argparse import torch from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN from llava.conversation import conv_templates, SeparatorStyle from llava.model.builder import load_pretrained_model from llava.utils import disable_torch_init from llava.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path from PIL import Image import requests from PIL import Image from io import BytesIO from transformers import TextStreamer def load_image(image_file): if image_file.startswith('http://') or image_file.startswith('https://'): response = requests.get(image_file) image = Image.open(BytesIO(response.content)).convert('RGB') else: image = Image.open(image_file).convert('RGB') return image def main(args): # Model disable_torch_init() model_name = get_model_name_from_path(args.model_path) tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit, device=args.device) if "llama-2" in model_name.lower(): conv_mode = "llava_llama_2" elif "mistral" in model_name.lower(): conv_mode = "mistral_instruct" elif "v1.6-34b" in model_name.lower(): conv_mode = "chatml_direct" elif "v1" in model_name.lower(): conv_mode = "llava_v1" elif "mpt" in model_name.lower(): conv_mode = "mpt" else: conv_mode = "llava_v0" if args.conv_mode is not None and conv_mode != args.conv_mode: print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode)) else: args.conv_mode = conv_mode conv = conv_templates[args.conv_mode].copy() if "mpt" in model_name.lower(): roles = ('user', 'assistant') else: roles = conv.roles image = load_image(args.image_file) # Similar operation in model_worker.py print(model.config) image_tensor,image_size = process_images([image], image_processor, model.config) # image_size = image.size if type(image_tensor) is list: image_tensor = [image.to(model.device, dtype=torch.float16) for image in image_tensor] else: image_tensor = image_tensor.to(model.device, dtype=torch.float16) while True: try: inp = input(f"{roles[0]}: ") except EOFError: inp = "" if not inp: print("exit...") break print(f"{roles[1]}: ", end="") if image is not None: # first message if model.config.mm_use_im_start_end: inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + inp else: inp = DEFAULT_IMAGE_TOKEN + '\n' + inp conv.append_message(conv.roles[0], inp) image = None else: # later messages conv.append_message(conv.roles[0], inp) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(model.device) stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 keywords = [stop_str] streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) with torch.inference_mode(): output_ids = model.generate( input_ids, images=image_tensor, image_sizes=[image_size], do_sample=True if args.temperature > 0 else False, temperature=args.temperature, max_new_tokens=args.max_new_tokens, streamer=streamer, use_cache=True) outputs = tokenizer.decode(output_ids[0]).strip() conv.messages[-1][-1] = outputs if args.debug: print("\n", {"prompt": prompt, "outputs": outputs}, "\n") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--model-path", type=str, default="facebook/opt-350m") parser.add_argument("--model-base", type=str, default=None) parser.add_argument("--image-file", type=str, required=True) parser.add_argument("--device", type=str, default="cuda") parser.add_argument("--conv-mode", type=str, default=None) parser.add_argument("--temperature", type=float, default=0.2) parser.add_argument("--max-new-tokens", type=int, default=512) parser.add_argument("--load-8bit", action="store_true") parser.add_argument("--load-4bit", action="store_true") parser.add_argument("--debug", action="store_true") args = parser.parse_args() main(args)