OmniGen/processor.py

import os
import re
from typing import Dict, List
import json

import torch
import numpy as np
import random
from PIL import Image
from torchvision import transforms
from transformers import AutoTokenizer
from huggingface_hub import snapshot_download

from OmniGen.utils import (
    create_logger,
    update_ema,
    requires_grad,
    center_crop_arr,
    crop_arr,
)




class OmniGenProcessor:
    def __init__(self, 
                text_tokenizer, 
                max_image_size: int=1024):
        self.text_tokenizer = text_tokenizer
        self.max_image_size = max_image_size

        self.image_transform = transforms.Compose([
            transforms.Lambda(lambda pil_image: crop_arr(pil_image, max_image_size)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
        ])

        self.collator = OmniGenCollator()
        self.separate_collator = OmniGenSeparateCollator()

    @classmethod
    def from_pretrained(cls, model_name):
        if not os.path.exists(model_name):
            cache_folder = os.getenv('HF_HUB_CACHE')
            model_name = snapshot_download(repo_id=model_name,
                                           cache_dir=cache_folder,
                                           allow_patterns="*.json")
        text_tokenizer = AutoTokenizer.from_pretrained(model_name)

        return cls(text_tokenizer)


    def process_image(self, image):
        image = Image.open(image).convert('RGB')
        return self.image_transform(image)
    
    def process_multi_modal_prompt(self, text, input_images):
        text = self.add_prefix_instruction(text)
        if input_images is None or len(input_images) == 0:
            model_inputs = self.text_tokenizer(text)
            return {"input_ids": model_inputs.input_ids, "pixel_values": None, "image_sizes": None}

        pattern = r"<\|image_\d+\|>"
        prompt_chunks = [self.text_tokenizer(chunk).input_ids for chunk in re.split(pattern, text)] 

        for i in range(1, len(prompt_chunks)):
            if prompt_chunks[i][0] == 1:
                prompt_chunks[i] = prompt_chunks[i][1:]

        image_tags = re.findall(pattern, text) 
        image_ids = [int(s.split("|")[1].split("_")[-1]) for s in image_tags]

        unique_image_ids = sorted(list(set(image_ids)))
        assert unique_image_ids == list(range(1, len(unique_image_ids)+1)), f"image_ids must start from 1, and must be continuous int, e.g. [1, 2, 3], cannot be {unique_image_ids}"
        # total images must be the same as the number of image tags
        assert len(unique_image_ids) == len(input_images), f"total images must be the same as the number of image tags, got {len(unique_image_ids)} image tags and {len(input_images)} images"
        
        input_images = [input_images[x-1] for x in image_ids]

        all_input_ids = []
        img_inx = []
        idx = 0
        for i in range(len(prompt_chunks)):
            all_input_ids.extend(prompt_chunks[i])
            if i != len(prompt_chunks) -1:
                start_inx = len(all_input_ids)
                size = input_images[i].size(-2) *  input_images[i].size(-1) // 16 // 16
                img_inx.append([start_inx, start_inx+size])
                all_input_ids.extend([0]*size)

        return {"input_ids": all_input_ids, "pixel_values": input_images, "image_sizes": img_inx}


    def add_prefix_instruction(self, prompt):
        user_prompt = '<|user|>\n'
        generation_prompt = 'Generate an image according to the following instructions\n'
        assistant_prompt = '<|assistant|>\n<|diffusion|>'
        prompt_suffix = "<|end|>\n"
        prompt = f"{user_prompt}{generation_prompt}{prompt}{prompt_suffix}{assistant_prompt}"
        return prompt


    def __call__(self, 
                instructions: List[str], 
                input_images: List[List[str]] = None,
                height: int = 1024,
                width: int = 1024,
                negative_prompt: str = "low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, 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.",
                use_img_cfg: bool = True,
                separate_cfg_input: bool = False,
                ) -> Dict:

        if input_images is None:
            use_img_cfg = False
        if isinstance(instructions, str):
            instructions = [instructions]
            input_images = [input_images]
        
        input_data = []
        for i in range(len(instructions)):
            cur_instruction = instructions[i]
            cur_input_images = None if input_images is None else input_images[i]
            if cur_input_images is not None and len(cur_input_images) > 0:
                cur_input_images = [self.process_image(x) for x in cur_input_images]
            else:
                cur_input_images = None
                assert "<img><|image_1|></img>" not in cur_instruction
            
            mllm_input = self.process_multi_modal_prompt(cur_instruction, cur_input_images)

        
            neg_mllm_input, img_cfg_mllm_input = None, None
            neg_mllm_input = self.process_multi_modal_prompt(negative_prompt, None)
            if use_img_cfg:
                if cur_input_images is not None and len(cur_input_images) >= 1:
                    img_cfg_prompt = [f"<img><|image_{i+1}|></img>" for i in range(len(cur_input_images))]
                    img_cfg_mllm_input = self.process_multi_modal_prompt(" ".join(img_cfg_prompt), cur_input_images)
                else:
                    img_cfg_mllm_input = neg_mllm_input

            input_data.append((mllm_input, neg_mllm_input, img_cfg_mllm_input, [height, width]))

        if separate_cfg_input:
            return self.separate_collator(input_data)
        return self.collator(input_data)




class OmniGenCollator:
    def __init__(self, pad_token_id=2, hidden_size=3072):
        self.pad_token_id = pad_token_id
        self.hidden_size = hidden_size
    
    def create_position(self, attention_mask, num_tokens_for_output_images):
        position_ids = []
        text_length = attention_mask.size(-1)
        img_length = max(num_tokens_for_output_images)  
        for mask in attention_mask:
            temp_l = torch.sum(mask)
            temp_position = [0]*(text_length-temp_l) + [i for i in range(temp_l+img_length+1)] # we add a time embedding into the sequence, so add one more token
            position_ids.append(temp_position)
        return torch.LongTensor(position_ids)
    
    def create_mask(self, attention_mask, num_tokens_for_output_images):
        extended_mask = []
        padding_images = []
        text_length = attention_mask.size(-1)
        img_length = max(num_tokens_for_output_images)
        seq_len = text_length + img_length + 1 # we add a time embedding into the sequence, so add one more token
        inx = 0
        for mask in attention_mask:
            temp_l = torch.sum(mask)
            pad_l = text_length - temp_l

            temp_mask = torch.tril(torch.ones(size=(temp_l+1, temp_l+1)))

            image_mask = torch.zeros(size=(temp_l+1, img_length))
            temp_mask = torch.cat([temp_mask, image_mask], dim=-1)

            image_mask = torch.ones(size=(img_length, temp_l+img_length+1))
            temp_mask = torch.cat([temp_mask, image_mask], dim=0)

            if pad_l > 0:
                pad_mask = torch.zeros(size=(temp_l+1+img_length, pad_l))
                temp_mask = torch.cat([pad_mask, temp_mask], dim=-1)

                pad_mask = torch.ones(size=(pad_l, seq_len))
                temp_mask = torch.cat([pad_mask, temp_mask], dim=0)

            true_img_length = num_tokens_for_output_images[inx]
            pad_img_length = img_length - true_img_length
            if pad_img_length > 0:
                temp_mask[:, -pad_img_length:] = 0
                temp_padding_imgs = torch.zeros(size=(1, pad_img_length, self.hidden_size))
            else:
                temp_padding_imgs = None
            
            extended_mask.append(temp_mask.unsqueeze(0))
            padding_images.append(temp_padding_imgs)
            inx += 1
        return torch.cat(extended_mask, dim=0), padding_images
    
    def adjust_attention_for_input_images(self, attention_mask, image_sizes):
        for b_inx in image_sizes.keys():
            for start_inx, end_inx in image_sizes[b_inx]:
                attention_mask[b_inx][start_inx:end_inx, start_inx:end_inx] = 1

        return attention_mask
    
    def pad_input_ids(self, input_ids, image_sizes):
        max_l = max([len(x) for x in input_ids])
        padded_ids = []
        attention_mask = []
        new_image_sizes = []

        for i in range(len(input_ids)):
            temp_ids = input_ids[i]
            temp_l = len(temp_ids)
            pad_l = max_l - temp_l
            if pad_l == 0:
                attention_mask.append([1]*max_l)
                padded_ids.append(temp_ids)
            else:
                attention_mask.append([0]*pad_l+[1]*temp_l)
                padded_ids.append([self.pad_token_id]*pad_l+temp_ids)
            
            if i in image_sizes:
                new_inx = []
                for old_inx in image_sizes[i]:
                    new_inx.append([x+pad_l for x in old_inx])
                image_sizes[i] = new_inx

        return torch.LongTensor(padded_ids), torch.LongTensor(attention_mask), image_sizes


    def process_mllm_input(self, mllm_inputs, target_img_size):
        num_tokens_for_output_images = []
        for img_size in target_img_size:
            num_tokens_for_output_images.append(img_size[0]*img_size[1]//16//16)

        pixel_values, image_sizes = [], {}
        b_inx = 0
        for x in mllm_inputs:
            if x['pixel_values'] is not None:
                pixel_values.extend(x['pixel_values'])
                for size in x['image_sizes']:
                    if b_inx not in image_sizes:
                        image_sizes[b_inx] = [size]
                    else:
                        image_sizes[b_inx].append(size)
            b_inx += 1     
        pixel_values = [x.unsqueeze(0) for x in pixel_values]

        
        input_ids = [x['input_ids'] for x in mllm_inputs]
        padded_input_ids, attention_mask, image_sizes = self.pad_input_ids(input_ids, image_sizes)
        position_ids = self.create_position(attention_mask, num_tokens_for_output_images)
        attention_mask, padding_images = self.create_mask(attention_mask, num_tokens_for_output_images)
        attention_mask = self.adjust_attention_for_input_images(attention_mask, image_sizes)

        return padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes
    
    
    def __call__(self, features):
        mllm_inputs = [f[0] for f in features]
        cfg_mllm_inputs = [f[1] for f in features]
        img_cfg_mllm_input = [f[2] for f in features]
        target_img_size = [f[3] for f in features]

        
        if img_cfg_mllm_input[0] is not None:
            mllm_inputs = mllm_inputs + cfg_mllm_inputs + img_cfg_mllm_input
            target_img_size = target_img_size + target_img_size + target_img_size
        else:
            mllm_inputs = mllm_inputs + cfg_mllm_inputs
            target_img_size = target_img_size + target_img_size


        all_padded_input_ids, all_position_ids, all_attention_mask, all_padding_images, all_pixel_values, all_image_sizes = self.process_mllm_input(mllm_inputs, target_img_size)

        data = {"input_ids": all_padded_input_ids,
        "attention_mask": all_attention_mask,
        "position_ids": all_position_ids,
        "input_pixel_values": all_pixel_values,
        "input_image_sizes": all_image_sizes,
        "padding_images": all_padding_images,
        }
        return data


class OmniGenSeparateCollator(OmniGenCollator):
    def __call__(self, features):
        mllm_inputs = [f[0] for f in features]
        cfg_mllm_inputs = [f[1] for f in features]
        img_cfg_mllm_input = [f[2] for f in features]
        target_img_size = [f[3] for f in features]

        
        all_padded_input_ids, all_attention_mask, all_position_ids, all_pixel_values, all_image_sizes, all_padding_images = [], [], [], [], [], []


        padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes = self.process_mllm_input(mllm_inputs, target_img_size)
        all_padded_input_ids.append(padded_input_ids)
        all_attention_mask.append(attention_mask)
        all_position_ids.append(position_ids)
        all_pixel_values.append(pixel_values)
        all_image_sizes.append(image_sizes)
        all_padding_images.append(padding_images)

        if cfg_mllm_inputs[0] is not None:
            padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes = self.process_mllm_input(cfg_mllm_inputs, target_img_size)
            all_padded_input_ids.append(padded_input_ids)
            all_attention_mask.append(attention_mask)
            all_position_ids.append(position_ids)
            all_pixel_values.append(pixel_values)
            all_image_sizes.append(image_sizes)
            all_padding_images.append(padding_images)
        if img_cfg_mllm_input[0] is not None:
            padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes = self.process_mllm_input(img_cfg_mllm_input, target_img_size)
            all_padded_input_ids.append(padded_input_ids)
            all_attention_mask.append(attention_mask)
            all_position_ids.append(position_ids)
            all_pixel_values.append(pixel_values)
            all_image_sizes.append(image_sizes)
            all_padding_images.append(padding_images)

        data = {"input_ids": all_padded_input_ids,
        "attention_mask": all_attention_mask,
        "position_ids": all_position_ids,
        "input_pixel_values": all_pixel_values,
        "input_image_sizes": all_image_sizes,
        "padding_images": all_padding_images,
        }
        return data