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all_mgie.py

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+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(2000)):
+    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)