share_gpt4o.py

import os
import PIL.Image
import torch
import numpy as np
from transformers import AutoModelForCausalLM
from janus.models import MultiModalityCausalLM, VLChatProcessor
from dataclasses import dataclass


@dataclass
class VLChatProcessorOutput():
    sft_format: str
    input_ids: torch.Tensor
    pixel_values: torch.Tensor
    num_image_tokens: torch.IntTensor

    def __len__(self):
        return len(self.input_ids)


def process_image(image_paths, vl_chat_processor):
    images = [PIL.Image.open(image_path).convert("RGB") for image_path in image_paths]
    images_outputs = vl_chat_processor.image_processor(images, return_tensors="pt")
    return images_outputs['pixel_values']


# Load model and processor
model_path = "/data5/czh/bxh/test_2/slice_end"
vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)
tokenizer = vl_chat_processor.tokenizer
vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(
    model_path, trust_remote_code=True, torch_dtype=torch.bfloat16
)
vl_gpt = vl_gpt.cuda().eval()


# Define text+image-to-image generation function
def text_and_image_to_image_generate(input_prompt, input_image_path, output_path, vl_chat_processor, vl_gpt,
                                     temperature=1.0, parallel_size=2, cfg_weight=5, cfg_weight2=5):
    torch.cuda.empty_cache()

    input_img_tokens = vl_chat_processor.image_start_tag + vl_chat_processor.image_tag * vl_chat_processor.num_image_tokens + vl_chat_processor.image_end_tag + vl_chat_processor.image_start_tag + vl_chat_processor.pad_tag * vl_chat_processor.num_image_tokens + vl_chat_processor.image_end_tag
    output_img_tokens = vl_chat_processor.image_start_tag

    pre_data = []
    input_images = [input_image_path]
    img_len = len(input_images)
    prompts = input_img_tokens * img_len + input_prompt
    conversation = [
        {"role": "<|User|>", "content": prompts},
        {"role": "<|Assistant|>", "content": ""}
    ]
    sft_format = vl_chat_processor.apply_sft_template_for_multi_turn_prompts(
        conversations=conversation,
        sft_format=vl_chat_processor.sft_format,
        system_prompt="",
    )

    sft_format = sft_format + output_img_tokens
    print('sft_format: ', len(sft_format))

    mmgpt = vl_gpt

    image_token_num_per_image = 576
    img_size = 384
    patch_size = 16

    with torch.inference_mode():
        input_image_pixel_values = process_image(input_images, vl_chat_processor).to(torch.bfloat16).cuda()
        quant_input, emb_loss_input, info_input = mmgpt.gen_vision_model.encode(input_image_pixel_values)
        image_tokens_input = info_input[2].detach().reshape(input_image_pixel_values.shape[0], -1)
        image_embeds_input = mmgpt.prepare_gen_img_embeds(image_tokens_input)

        input_ids = torch.LongTensor(vl_chat_processor.tokenizer.encode(sft_format))
        print('input_ids.shape: ', input_ids.shape)
        encoder_pixel_values = process_image(input_images, vl_chat_processor).cuda()
        print('encoder: ', encoder_pixel_values[0][0][0][:2])
        tokens = torch.zeros((parallel_size * 3, len(input_ids)), dtype=torch.long)
        for i in range(parallel_size * 3):
            tokens[i, :] = input_ids
            if i % 3 == 2:
                tokens[i, 1:-1] = vl_chat_processor.pad_id
                print(vl_chat_processor.pad_id)
                pre_data.append(VLChatProcessorOutput(sft_format=sft_format, pixel_values=encoder_pixel_values,
                                                      input_ids=tokens[i - 2],
                                                      num_image_tokens=[vl_chat_processor.num_image_tokens] * img_len))
                pre_data.append(VLChatProcessorOutput(sft_format=sft_format, pixel_values=encoder_pixel_values,
                                                      input_ids=tokens[i - 1],
                                                      num_image_tokens=[vl_chat_processor.num_image_tokens] * img_len))
                pre_data.append(VLChatProcessorOutput(sft_format=sft_format, pixel_values=None, input_ids=tokens[i],
                                                      num_image_tokens=[]))

        prepare_inputs = vl_chat_processor.batchify(pre_data)

        inputs_embeds = mmgpt.prepare_inputs_embeds(
            input_ids=tokens.cuda(),
            pixel_values=prepare_inputs['pixel_values'].to(torch.bfloat16).cuda(),
            images_emb_mask=prepare_inputs['images_emb_mask'].cuda(),
            images_seq_mask=prepare_inputs['images_seq_mask'].cuda()
        )


        image_gen_indices = (tokens == vl_chat_processor.image_end_id).nonzero()
        print(inputs_embeds.shape)
        print(inputs_embeds[0][0][:2])
        print(image_embeds_input[0][0][:2])
        for ii, ind in enumerate(image_gen_indices):
            print('nmsl: ',ii, ind)
            if ii % 4 == 0:
                offset = ind[1] + 2
                inputs_embeds[ind[0], offset: offset + image_embeds_input.shape[1], :] = image_embeds_input[
                    (ii // 2) % img_len]

        generated_tokens = torch.zeros((parallel_size, image_token_num_per_image), dtype=torch.int).cuda()

        for i in range(image_token_num_per_image):
            outputs = mmgpt.language_model.model(inputs_embeds=inputs_embeds, use_cache=True,
                                                 past_key_values=outputs.past_key_values if i != 0 else None)
            hidden_states = outputs.last_hidden_state
            if i == 0:
                print('DAS', hidden_states.shape)
                # torch.save(inputs_embeds, '/data/bxh_data/unify_model/share.pt')


            logits = mmgpt.gen_head(hidden_states[:, -1, :])
            print('logits: ', logits.shape)
            logit_cond_full = logits[0::3, :]
            logit_cond_part = logits[1::3, :]
            logit_uncond = logits[2::3, :]

            logit_cond = (logit_cond_full + cfg_weight2 * (logit_cond_part)) / (1 + cfg_weight2)
            logits = logit_uncond + cfg_weight * (logit_cond - logit_uncond)
            probs = torch.softmax(logits / temperature, dim=-1)

            next_token = torch.multinomial(probs, num_samples=1)
            generated_tokens[:, i] = next_token.squeeze(dim=-1)

            next_token = torch.cat(
                [next_token.unsqueeze(dim=1), next_token.unsqueeze(dim=1), next_token.unsqueeze(dim=1)], dim=1).view(-1)
            img_embeds = mmgpt.prepare_gen_img_embeds(next_token)
            inputs_embeds = img_embeds.unsqueeze(dim=1)

        dec = mmgpt.gen_vision_model.decode_code(generated_tokens.to(dtype=torch.int),
                                                 shape=[parallel_size, 8, img_size // patch_size,
                                                        img_size // patch_size])
        dec = dec.to(torch.float32).cpu().numpy().transpose(0, 2, 3, 1)

        dec = np.clip((dec + 1) / 2 * 255, 0, 255)

        visual_img = np.zeros((parallel_size, img_size, img_size, 3), dtype=np.uint8)
        visual_img[:, :, :] = dec

        output_images = []
        for i in range(parallel_size):
            save_path = output_path.replace('.png', '') + f'_{i}.png'
            PIL.Image.fromarray(visual_img[i]).save(save_path)
            output_images.append(save_path)
        return output_images


# Run
prompt = "Place a potted plant on the step to the left of the bicycle."
input_image_path = "/data5/czh/bxh/SEED-Data-Edit-Part2-3/multi_turn_editing/images/data/20240318_278P_1069turns/Data/298/9945a25b0438494eb4cdb7a05574f16a.jpg"
image_output_path = "test_1.png"
text_and_image_to_image_generate(prompt, input_image_path, image_output_path, vl_chat_processor, vl_gpt,
                                 parallel_size=1)