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import argparse
import torch
from minigemini.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from minigemini.conversation import conv_templates, SeparatorStyle
from minigemini.model.builder import load_pretrained_model
from minigemini.utils import disable_torch_init
from minigemini.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
from PIL import Image
import requests
from PIL import Image
from io import BytesIO
from transformers import TextStreamer
try:
from diffusers import StableDiffusionXLPipeline
except:
print('please install diffusers==0.26.3')
try:
from paddleocr import PaddleOCR
except:
print('please install paddleocr following https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.7/README_en.md')
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()
if args.ocr and args.image_file is not None:
ocr = PaddleOCR(use_angle_cls=True, use_gpu=True, lang="ch")
result = ocr.ocr(args.image_file)
str_in_image = ''
if result[0] is not None:
result = [res[1][0] for res in result[0] if res[1][1] > 0.1]
if len(result) > 0:
str_in_image = ', '.join(result)
print('OCR Token: ' + str_in_image)
if args.gen:
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
).to("cuda")
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 '8x7b' in model_name.lower():
conv_mode = "mistral_instruct"
elif '34b' in model_name.lower():
conv_mode = "chatml_direct"
elif '2b' in model_name.lower():
conv_mode = "gemma"
else:
conv_mode = "vicuna_v1"
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
if args.image_file is not None:
images = []
if ',' in args.image_file:
images = args.image_file.split(',')
else:
images = [args.image_file]
image_convert = []
for _image in images:
image_convert.append(load_image(_image))
if hasattr(model.config, 'image_size_aux'):
if not hasattr(image_processor, 'image_size_raw'):
image_processor.image_size_raw = image_processor.crop_size.copy()
image_processor.crop_size['height'] = model.config.image_size_aux
image_processor.crop_size['width'] = model.config.image_size_aux
image_processor.size['shortest_edge'] = model.config.image_size_aux
# Similar operation in model_worker.py
image_tensor = process_images(image_convert, image_processor, model.config)
image_grid = getattr(model.config, 'image_grid', 1)
if hasattr(model.config, 'image_size_aux'):
raw_shape = [image_processor.image_size_raw['height'] * image_grid,
image_processor.image_size_raw['width'] * image_grid]
image_tensor_aux = image_tensor
image_tensor = torch.nn.functional.interpolate(image_tensor,
size=raw_shape,
mode='bilinear',
align_corners=False)
else:
image_tensor_aux = []
if image_grid >= 2:
raw_image = image_tensor.reshape(3,
image_grid,
image_processor.image_size_raw['height'],
image_grid,
image_processor.image_size_raw['width'])
raw_image = raw_image.permute(1, 3, 0, 2, 4)
raw_image = raw_image.reshape(-1, 3,
image_processor.image_size_raw['height'],
image_processor.image_size_raw['width'])
if getattr(model.config, 'image_global', False):
global_image = image_tensor
if len(global_image.shape) == 3:
global_image = global_image[None]
global_image = torch.nn.functional.interpolate(global_image,
size=[image_processor.image_size_raw['height'],
image_processor.image_size_raw['width']],
mode='bilinear',
align_corners=False)
# [image_crops, image_global]
raw_image = torch.cat([raw_image, global_image], dim=0)
image_tensor = raw_image.contiguous()
image_tensor = image_tensor.unsqueeze(0)
if type(image_tensor) is list:
image_tensor = [image.to(model.device, dtype=torch.float16) for image in image_tensor]
image_tensor_aux = [image.to(model.device, dtype=torch.float16) for image in image_tensor_aux]
else:
image_tensor = image_tensor.to(model.device, dtype=torch.float16)
image_tensor_aux = image_tensor_aux.to(model.device, dtype=torch.float16)
else:
images = None
image_tensor = None
image_tensor_aux = []
while True:
try:
inp = input(f"{roles[0]}: ")
except EOFError:
inp = ""
if not inp:
print("exit...")
break
print(f"{roles[1]}: ", end="")
if args.ocr and len(str_in_image) > 0:
inp = inp + '\nReference OCR Token: ' + str_in_image + '\n'
if args.gen:
inp = inp + ' <GEN>'
# print(inp, '====')
if images 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')*len(images) + inp
conv.append_message(conv.roles[0], inp)
images = None
else:
# later messages
conv.append_message(conv.roles[0], inp)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
# add image split string
if prompt.count(DEFAULT_IMAGE_TOKEN) >= 2:
final_str = ''
sent_split = prompt.split(DEFAULT_IMAGE_TOKEN)
for _idx, _sub_sent in enumerate(sent_split):
if _idx == len(sent_split) - 1:
final_str = final_str + _sub_sent
else:
final_str = final_str + _sub_sent + f'Image {_idx+1}:' + DEFAULT_IMAGE_TOKEN
prompt = final_str
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(model.device)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor,
images_aux=image_tensor_aux if len(image_tensor_aux)>0 else None,
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
max_new_tokens=args.max_new_tokens,
bos_token_id=tokenizer.bos_token_id, # Begin of sequence token
eos_token_id=tokenizer.eos_token_id, # End of sequence token
pad_token_id=tokenizer.pad_token_id, # Pad token
streamer=streamer,
use_cache=True)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
conv.messages[-1][-1] = outputs
if args.gen and '<h>' in outputs and '</h>' in outputs:
common_neg_prompt = "out of frame, lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"
prompt = outputs.split("</h>")[-2].split("<h>")[-1]
output_img = pipe(prompt, negative_prompt=common_neg_prompt).images[0]
output_img.save(args.output_file)
print(f'Generate an image, save at {args.output_file}')
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, default=None) # file_0.jpg,file_1.jpg for multi image
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("--ocr", action="store_true")
parser.add_argument("--gen", action="store_true")
parser.add_argument("--output-file", type=str, default='generate.png')
parser.add_argument("--debug", action="store_true")
args = parser.parse_args()
main(args) |