launch/image_edition.py

import diffusers
import transformers
import gradio as gr
from ml_mgie.mgie_llava import *
from ml_mgie.conversation import conv_templates
import torch as T
import numpy as np
from PIL import Image
import huggingface_hub
import spaces

# Constants
DEFAULT_IMAGE_TOKEN = '<image>'
DEFAULT_IMAGE_PATCH_TOKEN = '<im_patch>'
DEFAULT_IM_START_TOKEN = '<im_start>'
DEFAULT_IM_END_TOKEN = '<im_end>'
PATH_LLAVA = '_ckpt/LLaVA-7B-v1'

# Download the model checkpoint
huggingface_hub.snapshot_download(
    repo_id='tsujuifu/ml-mgie', repo_type='model', local_dir='_ckpt', local_dir_use_symlinks=False)

# Load the model and tokenizer
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)

# Configure the tokenizer and model
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()

# Load the diffusion pipeline
pipe = diffusers.StableDiffusionInstructPix2PixPipeline.from_pretrained(
    'timbrooks/instruct-pix2pix', torch_dtype=T.float16).to('cuda')
pipe.set_progress_bar_config(disable=True)
pipe.unet.load_state_dict(T.load('_ckpt/mgie_7b/unet.pt', map_location='cpu'))
print('--init MGIE--')


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):
    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()

# Main MGIE function


@spaces.GPU(enable_queue=True)
def go_mgie(img, txt, seed, cfg_txt, cfg_img):
    EMB = ckpt['emb'].cuda()
    with T.inference_mode():
        NULL = model.edit_head(T.zeros(1, 8, 4096).half().to('cuda'), EMB)

    img, seed = crop_resize(Image.fromarray(img).convert('RGB')), int(seed)
    inp = img

    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_1'].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():
        _ = model.cuda()
        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]

        if 32003 in out:
            p = out.index(32003)-1
        else:
            p = len(hid)-9
        p = min(p, len(hid)-9)
        hid = hid[p:p+8]

        out = remove_alter(tokenizer.decode(out))
        _ = model.cuda()
        emb = model.edit_head(hid.unsqueeze(dim=0), EMB)
        res = pipe(image=inp, prompt_embeds=emb, negative_prompt_embeds=NULL,
                   generator=T.Generator(device='cuda').manual_seed(seed), guidance_scale=cfg_txt, image_guidance_scale=cfg_img).images[0]

    return res, out

# Example function


def go_example(seed, cfg_txt, cfg_img):
    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']
    i = T.randint(len(ins), (1, )).item()

    return './examples/_input/%d.jpg' % (i), ins[i], seed, cfg_txt, cfg_img


# Test MGIE
go_mgie(np.array(Image.open('./examples/_input/0.jpg').convert('RGB')),
        'make the frame red', 13331, 7.5, 1.5)
print('--init GO--')


def image_edition_ui():
    with gr.Row():
        inp, res = [gr.Image(height=384, width=384, label='Input Image', interactive=True),
                    gr.Image(height=384, width=384, label='Goal Image', interactive=True)]
    with gr.Row():
        txt, out = [gr.Textbox(label='Instruction', interactive=True),
                    gr.Textbox(label='Expressive Instruction', interactive=False)]
    with gr.Row():
        seed, cfg_txt, cfg_img = [gr.Number(value=13331, label='Seed', interactive=True),
                                  gr.Number(
                                      value=7.5, label='Text CFG', interactive=True),
                                  gr.Number(value=1.5, label='Image CFG', interactive=True)]
    with gr.Row():
        btn_exp, btn_sub = [gr.Button('More Example'), gr.Button('Submit')]
    btn_exp.click(fn=go_example, inputs=[seed, cfg_txt, cfg_img], outputs=[
                  inp, txt, seed, cfg_txt, cfg_img])
    btn_sub.click(fn=go_mgie, inputs=[
                  inp, txt, seed, cfg_txt, cfg_img], outputs=[res, out])

    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']
    gr.Examples(examples=[['./examples/_input/%d.jpg' % (i), ins[i]]
                for i in [1, 5, 8, 14, 16]], inputs=[inp, txt])