all_mgie.py

import os

from tqdm.auto import tqdm

from PIL import Image

import torch as T
import transformers, diffusers
from mgie_llava import LlavaLlamaForCausalLM_
from llava.conversation import conv_templates
from llava.model import *
import json


def read_json(file_path): 
    with open(file_path, 'r', encoding='utf-8') as file:
        data = json.load(file)
    return data

def write_json(file_path, data):
    with open(file_path, 'w', encoding='utf-8') as file:
        json.dump(data, file, ensure_ascii=False, indent=4)

def crop_resize(f, sz=512):
    w, h = f.size
    if w>h:
        p = (w-h)//2
        f = f.crop([p, 0, p+h, h])
    elif h>w:
        p = (h-w)//2
        f = f.crop([0, p, w, p+w])
    f = f.resize([sz, sz])
    return f
def remove_alter(s):  # hack expressive instruction
    if 'ASSISTANT:' in s: s = s[s.index('ASSISTANT:')+10:].strip()
    if '</s>' in s: s = s[:s.index('</s>')].strip()
    if 'alternative' in s.lower(): s = s[:s.lower().index('alternative')]
    if '[IMG0]' in s: s = s[:s.index('[IMG0]')]
    s = '.'.join([s.strip() for s in s.split('.')[:2]])
    if s[-1]!='.': s += '.'
    return s.strip()


DEFAULT_IMAGE_TOKEN = '<image>'
DEFAULT_IMAGE_PATCH_TOKEN = '<im_patch>'
DEFAULT_IM_START_TOKEN = '<im_start>'
DEFAULT_IM_END_TOKEN = '<im_end>'
PATH_LLAVA = '/home/zbz5349/WorkSpace/aigeeks/ml-mgie/_ckpt/LLaVA-7B-v1'

tokenizer = transformers.AutoTokenizer.from_pretrained(PATH_LLAVA)
model = LlavaLlamaForCausalLM_.from_pretrained(PATH_LLAVA, low_cpu_mem_usage=True, torch_dtype=T.float16, use_cache=True).cuda()
image_processor = transformers.CLIPImageProcessor.from_pretrained(model.config.mm_vision_tower, torch_dtype=T.float16)

tokenizer.padding_side = 'left'
tokenizer.add_tokens(['[IMG0]', '[IMG1]', '[IMG2]', '[IMG3]', '[IMG4]', '[IMG5]', '[IMG6]', '[IMG7]'], special_tokens=True)
model.resize_token_embeddings(len(tokenizer))
ckpt = T.load('./_ckpt/mgie_7b/mllm.pt', map_location='cpu')
model.load_state_dict(ckpt, strict=False)

mm_use_im_start_end = getattr(model.config, 'mm_use_im_start_end', False)
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
if mm_use_im_start_end: tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)

vision_tower = model.get_model().vision_tower[0]
vision_tower = transformers.CLIPVisionModel.from_pretrained(vision_tower.config._name_or_path, torch_dtype=T.float16, low_cpu_mem_usage=True).cuda()
model.get_model().vision_tower[0] = vision_tower
vision_config = vision_tower.config
vision_config.im_patch_token = tokenizer.convert_tokens_to_ids([DEFAULT_IMAGE_PATCH_TOKEN])[0]
vision_config.use_im_start_end = mm_use_im_start_end
if mm_use_im_start_end: vision_config.im_start_token, vision_config.im_end_token = tokenizer.convert_tokens_to_ids([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN])
image_token_len = (vision_config.image_size//vision_config.patch_size)**2

_ = model.eval()
EMB = ckpt['emb'].cuda()
with T.inference_mode(): NULL = model.edit_head(T.zeros(1, 8, 4096).half().to('cuda'), EMB)
print('NULL:', NULL.shape)

pipe = diffusers.StableDiffusionInstructPix2PixPipeline.from_pretrained('timbrooks/instruct-pix2pix', torch_dtype=T.float16, safety_checker=None).to('cuda')
pipe.set_progress_bar_config(disable=True)
pipe.unet.load_state_dict(T.load('./_ckpt/mgie_7b/unet.pt', map_location='cpu'))


SEED = 13331

# ins = ['make the frame red', 'turn the day into night', 'give him a beard', 'make cottage a mansion', 
#        'remove yellow object from dogs paws', 'change the hair from red to blue', 'remove the text', 'increase the image contrast', 
#        'remove the people in the background', 'please make this photo professional looking', 'darken the image, sharpen it', 'photoshop the girl out', 
#        'make more brightness', 'take away the brown filter form the image', 'add more contrast to simulate more light', 'dark on rgb', 
#        'make the face happy', 'change view as ocean', 'replace basketball with soccer ball', 'let the floor be made of wood']

data_path = '/home/zbz5349/WorkSpace/aigeeks/Qwen2.5-VL/magicbrush_dataset/genp2_4_multi.json'
save_image = '/home/zbz5349/WorkSpace/aigeeks/ml-mgie/all'
os.makedirs(save_image,exist_ok=True)
# 若有x个指令那么生成x(single) + x(mix) + 1(all)张图片
data = read_json(data_path)
for i in tqdm(range(100)):
    img_path = data[i]["content"][0]["image"]
    g = img_path
    g = g.split('/')
    
    txt = data[i]["content"][1]["text"]
    save_img_path = f"{g[-1]}"

    img = Image.open(img_path)
    #img.save(os.path.join(save_image,f"ori_{i}{i}.png"))
    #img, txt = Image.open('_input/%d.jpg'%(i)).convert('RGB'), ins[i]
    
    img = image_processor.preprocess(img, return_tensors='pt')['pixel_values'][0]
    txt = "what will this image be like if '%s'"%(txt)
    txt = txt+'\n'+DEFAULT_IM_START_TOKEN+DEFAULT_IMAGE_PATCH_TOKEN*image_token_len+DEFAULT_IM_END_TOKEN
    conv = conv_templates['vicuna_v1'].copy()
    conv.append_message(conv.roles[0], txt), conv.append_message(conv.roles[1], None)
    txt = conv.get_prompt()
    txt = tokenizer(txt)
    txt, mask = T.as_tensor(txt['input_ids']), T.as_tensor(txt['attention_mask'])
    
    with T.inference_mode():
        out = model.generate(txt.unsqueeze(dim=0).cuda(), images=img.half().unsqueeze(dim=0).cuda(), attention_mask=mask.unsqueeze(dim=0).cuda(), 
                             do_sample=False, max_new_tokens=96, num_beams=1, no_repeat_ngram_size=3, 
                             return_dict_in_generate=True, output_hidden_states=True)
        out, hid = out['sequences'][0].tolist(), T.cat([x[-1] for x in out['hidden_states']], dim=1)[0]
        
        p = min(out.index(32003)-1 if 32003 in out else len(hid)-9, len(hid)-9)
        hid = hid[p:p+8]

        out = remove_alter(tokenizer.decode(out))
        emb = model.edit_head(hid.unsqueeze(dim=0), EMB)
        res = pipe(image=Image.open(img_path).convert('RGB'), prompt_embeds=emb, negative_prompt_embeds=NULL, generator=T.Generator(device='cuda').manual_seed(SEED)).images[0]
    save_img_path = os.path.join(save_image, save_img_path)
    res.save(save_img_path)