app.py

import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "False"
os.environ["TOKENIZERS_PARALLELISM"] = "true"
import numpy as np
import gradio as gr
import spaces
import torch
import torch.nn.functional as F
from PIL import Image
from omegaconf import OmegaConf
from transformers import AutoTokenizer

from prompting_utils import UniversalPrompting, create_attention_mask_predict_next, create_attention_mask_for_mmu
from training_utils import image_transform
from models import Showo, MAGVITv2, get_mask_chedule

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

config = OmegaConf.load("configs/showo_demo.yaml")
tokenizer = AutoTokenizer.from_pretrained(config.model.showo.llm_model_path, padding_side="left")

uni_prompting = UniversalPrompting(tokenizer, max_text_len=config.dataset.preprocessing.max_seq_length,
                                   special_tokens=("<|soi|>", "<|eoi|>", "<|sov|>", "<|eov|>", "<|t2i|>", "<|mmu|>",
                                                   "<|t2v|>", "<|v2v|>", "<|lvg|>"),
                                   ignore_id=-100, cond_dropout_prob=config.training.cond_dropout_prob)

vq_model = MAGVITv2()
vq_model = vq_model.from_pretrained(config.model.vq_model.vq_model_name).to(device)
vq_model.requires_grad_(False)
vq_model.eval()

model = Showo.from_pretrained(config.model.showo.pretrained_model_path).to(device)
model.eval()
mask_token_id = model.config.mask_token_id


@spaces.GPU
def text_to_image_generation(input_text, guidance_scale=1.75, generation_timesteps=18):
    prompts = [input_text]
    config.training.batch_size = config.batch_size = 1
    config.training.guidance_scale = config.guidance_scale = guidance_scale
    config.training.generation_timesteps = config.generation_timesteps = generation_timesteps

    image_tokens = torch.ones((len(prompts), config.model.showo.num_vq_tokens),
                              dtype=torch.long, device=device) * mask_token_id

    input_ids, _ = uni_prompting((prompts, image_tokens), 't2i_gen')

    if config.training.guidance_scale > 0:
        uncond_input_ids, _ = uni_prompting(([''] * len(prompts), image_tokens), 't2i_gen')
        attention_mask = create_attention_mask_predict_next(torch.cat([input_ids, uncond_input_ids], dim=0),
                                                            pad_id=int(uni_prompting.sptids_dict['<|pad|>']),
                                                            soi_id=int(uni_prompting.sptids_dict['<|soi|>']),
                                                            eoi_id=int(uni_prompting.sptids_dict['<|eoi|>']),
                                                            rm_pad_in_image=True)
    else:
        attention_mask = create_attention_mask_predict_next(input_ids,
                                                            pad_id=int(uni_prompting.sptids_dict['<|pad|>']),
                                                            soi_id=int(uni_prompting.sptids_dict['<|soi|>']),
                                                            eoi_id=int(uni_prompting.sptids_dict['<|eoi|>']),
                                                            rm_pad_in_image=True)
        uncond_input_ids = None

    if config.get("mask_schedule", None) is not None:
        schedule = config.mask_schedule.schedule
        args = config.mask_schedule.get("params", {})
        mask_schedule = get_mask_chedule(schedule, **args)
    else:
        mask_schedule = get_mask_chedule(config.training.get("mask_schedule", "cosine"))

    with torch.no_grad():
        gen_token_ids = model.t2i_generate(
            input_ids=input_ids,
            uncond_input_ids=uncond_input_ids,
            attention_mask=attention_mask,
            guidance_scale=config.training.guidance_scale,
            temperature=config.training.get("generation_temperature", 1.0),
            timesteps=config.training.generation_timesteps,
            noise_schedule=mask_schedule,
            noise_type=config.training.get("noise_type", "mask"),
            seq_len=config.model.showo.num_vq_tokens,
            uni_prompting=uni_prompting,
            config=config,
        )

        gen_token_ids = torch.clamp(gen_token_ids, max=config.model.showo.codebook_size - 1, min=0)
        images = vq_model.decode_code(gen_token_ids)
        images = torch.clamp((images + 1.0) / 2.0, min=0.0, max=1.0)
        images *= 255.0
        images = images.permute(0, 2, 3, 1).cpu().detach().numpy().astype(np.uint8)

    return images[0]


@spaces.GPU
def text_guided_inpainting(input_text, inpainting_image, inpainting_mask, guidance_scale=1.75, generation_timesteps=16):
    prompt = [input_text]

    config.training.batch_size = config.batch_size = 1
    config.training.guidance_scale = config.guidance_scale = guidance_scale
    config.training.generation_timesteps = config.generation_timesteps = generation_timesteps

    inpainting_image = image_transform(inpainting_image, resolution=config.dataset.params.resolution).to(device)
    inpainting_mask = image_transform(inpainting_mask, resolution=config.dataset.params.resolution, normalize=False)

    inpainting_image = inpainting_image.unsqueeze(0).repeat(config.training.batch_size, 1, 1, 1)

    inpainting_mask = inpainting_mask.unsqueeze(0).to(device)
    inpainting_mask = F.interpolate(inpainting_mask, size=config.dataset.params.resolution // 16, mode='bicubic')
    inpainting_mask = inpainting_mask.repeat(config.training.batch_size, 1, 1, 1)

    inpainting_mask[inpainting_mask < 0.5] = 0
    inpainting_mask[inpainting_mask >= 0.5] = 1

    inpainting_mask = inpainting_mask.reshape(config.training.batch_size, -1)
    inpainting_mask = inpainting_mask.to(torch.bool)

    inpainting_image_tokens = vq_model.get_code(inpainting_image) + len(uni_prompting.text_tokenizer)
    inpainting_image_tokens[inpainting_mask] = mask_token_id

    input_ids, _ = uni_prompting((prompt, inpainting_image_tokens), 't2i_gen')

    if config.training.guidance_scale > 0:
        uncond_input_ids, _ = uni_prompting(([''] * len(prompt), inpainting_image_tokens), 't2i_gen')
        attention_mask = create_attention_mask_predict_next(torch.cat([input_ids, uncond_input_ids], dim=0),
                                                            pad_id=int(uni_prompting.sptids_dict['<|pad|>']),
                                                            soi_id=int(uni_prompting.sptids_dict['<|soi|>']),
                                                            eoi_id=int(uni_prompting.sptids_dict['<|eoi|>']),
                                                            rm_pad_in_image=True)
    else:
        attention_mask = create_attention_mask_predict_next(input_ids,
                                                            pad_id=int(uni_prompting.sptids_dict['<|pad|>']),
                                                            soi_id=int(uni_prompting.sptids_dict['<|soi|>']),
                                                            eoi_id=int(uni_prompting.sptids_dict['<|eoi|>']),
                                                            rm_pad_in_image=True)
        uncond_input_ids = None

    if config.get("mask_schedule", None) is not None:
        schedule = config.mask_schedule.schedule
        args = config.mask_schedule.get("params", {})
        mask_schedule = get_mask_chedule(schedule, **args)
    else:
        mask_schedule = get_mask_chedule(config.training.get("mask_schedule", "cosine"))

    with torch.no_grad():
        gen_token_ids = model.t2i_generate(
            input_ids=input_ids,
            uncond_input_ids=uncond_input_ids,
            attention_mask=attention_mask,
            guidance_scale=config.training.guidance_scale,
            temperature=config.training.get("generation_temperature", 1.0),
            timesteps=config.training.generation_timesteps,
            noise_schedule=mask_schedule,
            noise_type=config.training.get("noise_type", "mask"),
            seq_len=config.model.showo.num_vq_tokens,
            uni_prompting=uni_prompting,
            config=config,
        )

        gen_token_ids = torch.clamp(gen_token_ids, max=config.model.showo.codebook_size - 1, min=0)
        images = vq_model.decode_code(gen_token_ids)

        images = torch.clamp((images + 1.0) / 2.0, min=0.0, max=1.0)
        images *= 255.0
        images = images.permute(0, 2, 3, 1).cpu().detach().numpy().astype(np.uint8)

    return images[0]


@spaces.GPU
def text_guided_extrapolation(input_img, input_text, left_ext, right_ext, guidance_scale=1.75, generation_timesteps=16):
    config.offset = 0
    config.training.batch_size = config.batch_size = 1
    config.training.guidance_scale = config.guidance_scale = guidance_scale
    config.training.generation_timesteps = config.generation_timesteps = generation_timesteps

    extra_direction = ['right'] * int(right_ext) + ['left'] * int(left_ext)
    prompt = [input_text] * len(extra_direction)
    W = config.dataset.params.resolution // 16
    for id, (prt, direction) in enumerate(zip(prompt, extra_direction)):
        prt = [prt] * config.training.batch_size
        if id == 0:
            # extrapolation_image = Image.open(config.image_path).convert("RGB")
            extrapolation_image = input_img
            extrapolation_image = image_transform(extrapolation_image,
                                                  resolution=config.dataset.params.resolution).to(device)

            B, _, _ = extrapolation_image.shape
            extrapolation_image = extrapolation_image.unsqueeze(0)
            extrapolation_image_tokens = vq_model.get_code(extrapolation_image) + len(uni_prompting.text_tokenizer)
            extrapolation_image_tokens = extrapolation_image_tokens.reshape(1,
                                                                            config.dataset.params.resolution // 16,
                                                                            config.dataset.params.resolution // 16)
            extrapolation_image_tokens = extrapolation_image_tokens.repeat(config.training.batch_size, 1, 1)
        else:

            extrapolation_image_tokens = gen_token_ids + len(uni_prompting.text_tokenizer)

        image_left_part = extrapolation_image_tokens[:, :, :-(W // 2 - config.offset)] - len(
            uni_prompting.text_tokenizer)
        image_right_part = extrapolation_image_tokens[:, :, W // 2 - config.offset:] - len(uni_prompting.text_tokenizer)
        image_up_part = extrapolation_image_tokens[:, :-(W // 2 - config.offset), :] - len(uni_prompting.text_tokenizer)
        image_down_part = extrapolation_image_tokens[:, W // 2 - config.offset:, :] - len(uni_prompting.text_tokenizer)

        if direction in ['left', 'right']:
            extrapolation_mask = torch.zeros((config.training.batch_size,
                                              config.dataset.params.resolution // 16,
                                              config.dataset.params.resolution // 16 // 2 + config.offset),
                                             dtype=torch.int64, device=device) + mask_token_id
        else:
            extrapolation_mask = torch.zeros((config.training.batch_size,
                                              config.dataset.params.resolution // 16 // 2 + config.offset,
                                              config.dataset.params.resolution // 16),
                                             dtype=torch.int64, device=device) + mask_token_id

        if direction == 'left':
            extrapolation_image_tokens = torch.cat(
                [extrapolation_mask, extrapolation_image_tokens[:, :, :W // 2 - config.offset]], dim=-1)
        elif direction == 'right':
            extrapolation_image_tokens = torch.cat(
                [extrapolation_image_tokens[:, :, -(W // 2 - config.offset):], extrapolation_mask], dim=-1)
        elif direction == 'up':
            extrapolation_image_tokens = torch.cat(
                [extrapolation_mask, extrapolation_image_tokens[:, :W // 2 - config.offset, :]], dim=-2)
        else:
            extrapolation_image_tokens = torch.cat(
                [extrapolation_image_tokens[:, -(W // 2 - config.offset):, :], extrapolation_mask], dim=-2)

        extrapolation_image_tokens = extrapolation_image_tokens.reshape(config.training.batch_size, -1)

        input_ids, _ = uni_prompting((prt, extrapolation_image_tokens), 't2i_gen')

        if config.training.guidance_scale > 0:
            uncond_input_ids, _ = uni_prompting(([''] * len(prt), extrapolation_image_tokens), 't2i_gen')
            attention_mask = create_attention_mask_predict_next(torch.cat([input_ids, uncond_input_ids], dim=0),
                                                                pad_id=int(uni_prompting.sptids_dict['<|pad|>']),
                                                                soi_id=int(uni_prompting.sptids_dict['<|soi|>']),
                                                                eoi_id=int(uni_prompting.sptids_dict['<|eoi|>']),
                                                                rm_pad_in_image=True)
        else:
            attention_mask = create_attention_mask_predict_next(input_ids,
                                                                pad_id=int(uni_prompting.sptids_dict['<|pad|>']),
                                                                soi_id=int(uni_prompting.sptids_dict['<|soi|>']),
                                                                eoi_id=int(uni_prompting.sptids_dict['<|eoi|>']),
                                                                rm_pad_in_image=True)
            uncond_input_ids = None

        if config.get("mask_schedule", None) is not None:
            schedule = config.mask_schedule.schedule
            args = config.mask_schedule.get("params", {})
            mask_schedule = get_mask_chedule(schedule, **args)
        else:
            mask_schedule = get_mask_chedule(config.training.get("mask_schedule", "cosine"))

        with torch.no_grad():
            gen_token_ids = model.t2i_generate(
                input_ids=input_ids,
                uncond_input_ids=uncond_input_ids,
                attention_mask=attention_mask,
                guidance_scale=config.training.guidance_scale,
                temperature=config.training.get("generation_temperature", 1.0),
                timesteps=config.training.generation_timesteps,
                noise_schedule=mask_schedule,
                noise_type=config.training.get("noise_type", "mask"),
                seq_len=config.model.showo.num_vq_tokens,
                uni_prompting=uni_prompting,
                config=config,
            )

        gen_token_ids = torch.clamp(gen_token_ids, max=config.model.showo.codebook_size - 1, min=0)
        gen_token_ids = gen_token_ids.reshape(config.training.batch_size,
                                              config.dataset.params.resolution // 16,
                                              config.dataset.params.resolution // 16)
        if direction == 'left':
            gen_token_ids = torch.cat([gen_token_ids, image_right_part], dim=-1)
        elif direction == 'right':
            gen_token_ids = torch.cat([image_left_part, gen_token_ids], dim=-1)
        elif direction == 'up':
            gen_token_ids = torch.cat([gen_token_ids, image_down_part], dim=-2)
        else:
            gen_token_ids = torch.cat([image_left_part, gen_token_ids], dim=-2)

    _, h, w = gen_token_ids.shape
    gen_token_ids = gen_token_ids.reshape(config.training.batch_size, -1)

    with torch.no_grad():
        images = vq_model.decode_code(gen_token_ids, shape=(h, w))
        images = torch.clamp((images + 1.0) / 2.0, min=0.0, max=1.0)
        images *= 255.0
        images = images.permute(0, 2, 3, 1).cpu().detach().numpy().astype(np.uint8)

    return images[0]


@spaces.GPU
def multimodal_understanding(input_img, input_text, chat_history):
    top_k = 1  # retain only the top_k most likely tokens, clamp others to have 0 probability

    image_ori = input_img
    image = image_transform(image_ori, resolution=config.dataset.params.resolution).to(device)
    image = image.unsqueeze(0)
    image_tokens = vq_model.get_code(image) + len(uni_prompting.text_tokenizer)

    question = input_text
    input_ids = uni_prompting.text_tokenizer(['USER: \n' + question + ' ASSISTANT:'])[
        'input_ids']
    input_ids = torch.tensor(input_ids).to(device)

    input_ids = torch.cat([
        (torch.ones(input_ids.shape[0], 1) * uni_prompting.sptids_dict['<|mmu|>']).to(device),
        (torch.ones(input_ids.shape[0], 1) * uni_prompting.sptids_dict['<|soi|>']).to(device),
        image_tokens,
        (torch.ones(input_ids.shape[0], 1) * uni_prompting.sptids_dict['<|eoi|>']).to(device),
        (torch.ones(input_ids.shape[0], 1) * uni_prompting.sptids_dict['<|sot|>']).to(device),
        input_ids
    ], dim=1).long()

    attention_mask = create_attention_mask_for_mmu(input_ids.to(device),
                                                   eoi_id=int(uni_prompting.sptids_dict['<|eoi|>']))

    cont_toks_list = model.mmu_generate(input_ids, attention_mask=attention_mask,
                                        max_new_tokens=100, top_k=top_k,
                                        eot_token=uni_prompting.sptids_dict['<|eot|>'])

    cont_toks_list = torch.stack(cont_toks_list).squeeze()[None]

    output_text = uni_prompting.text_tokenizer.batch_decode(cont_toks_list, skip_special_tokens=True)

    output_text = output_text[0].strip()

    chat_history.append((input_text, output_text))

    return "", chat_history


with gr.Blocks() as demo:
    gr.HTML("""
            <h1 class="display-2 fw-bold title">
              <a style="color: #70a8dc;">S</a><a style="color: #6fb051;">h</a><a style="color: #e06766;">o</a><a style="color: #f7b26b;">w</a>-o
            </h1>
            <p>This is the official Gradio demo for the Show-o model, a unified model that can do multimodal understanding and generation.</p>

            <strong>Paper:</strong> <a href="https://arxiv.org/abs/2408.12528" target="_blank">Show-o: One Single Transformer To Unify Multimodal Understanding and Generation </a>
            <br/>
            <strong>Project Website:</strong> <a href="https://showlab.github.io/Show-o/" target="_blank">Show-o Website</a>
            <br/>
            <strong>Code and Models:</strong> <a href="https://github.com/showlab/Show-o" target="_blank">GitHub</a>
            <br/>
            <br/>
        """)

    with gr.Row():
        with gr.Column():
            text_prompt_t2i = gr.Textbox(
                label="Text prompt",
                lines=2,
                placeholder="Input the text prompt here for image generation."
            )
            guidance_scale_t2i = gr.Slider(
                label="guidance scale",
                minimum=0,
                maximum=5,
                step=0.05,
                value=1.75
            )
            generation_timesteps_t2i = gr.Slider(
                label="timesteps",
                minimum=1,
                maximum=30,
                step=1,
                value=18
            )
        generated_img_t2i = gr.Image(
            label="Output image"
        )
    examples_t2i = gr.Examples(
        label="Text to image generation examples",
        examples=[
            "A dynamic scene of a rally car race.",
            "Paper artwork, layered paper, colorful Chinese dragon surrounded by clouds.",
            "Pixel art character riding a dragon through the clouds.",
        ],
        inputs=text_prompt_t2i,
    )
    submit_btn_t2i = gr.Button("Generate: Text-to-image")
    submit_btn_t2i.click(text_to_image_generation,
                         [text_prompt_t2i, guidance_scale_t2i, generation_timesteps_t2i],
                         [generated_img_t2i])

    with gr.Row():
        inpainting_input_img = gr.Image(
            label="Input image",
            type="pil",
        )
        inpainting_input_mask = gr.Image(
            label="Inpainting mask",
            image_mode="L",
            type="pil",
        )

        with gr.Column():
            text_prompt_inpainting = gr.Textbox(
                label="Text prompt",
                lines=2,
                placeholder="Input the text prompt here for image inpainting."
            )
            guidance_scale_inpainting = gr.Slider(
                label="guidance scale",
                minimum=0,
                maximum=5,
                step=0.05,
                value=1.75
            )
            generation_timesteps_inpainting = gr.Slider(
                label="timesteps",
                minimum=1,
                maximum=30,
                step=1,
                value=16
            )
        generated_img_inpainting = gr.Image(
            label="Output image"
        )
    examples_inpainting = gr.Examples(
        label="Text-guided inpainting examples",
        examples=[
            [
                "a blue sports car with sleek curves and tinted windows, parked on a bustling city street.",
                Image.open("./inpainting_validation/bus.jpg").convert("RGB"),
                Image.open("./inpainting_validation/bus_mask.webp").convert("L"),
            ],
            [
                "a clear, shallow river with some vibrant flowers in it.",
                Image.open("./inpainting_validation/train.jpg").convert("RGB"),
                Image.open("./inpainting_validation/train_mask.webp").convert("L"),
            ],
        ],
        inputs=[text_prompt_inpainting, inpainting_input_img, inpainting_input_mask],
    )
    submit_btn_inpainting = gr.Button("Generate: Text-guided Inpainting")
    submit_btn_inpainting.click(text_guided_inpainting,
                                [text_prompt_inpainting, inpainting_input_img, inpainting_input_mask,
                                 guidance_scale_inpainting, generation_timesteps_inpainting],
                                [generated_img_inpainting])

    with gr.Row():
        extra_input_img = gr.Image(
            label="Input image",
            type="pil",
            image_mode="RGB",
        )

        with gr.Column():
            text_prompt_extrapolation = gr.Textbox(
                label="Text prompt",
                lines=1,
                placeholder="Input the text prompt here for image extrapolation."
            )
            guidance_scale_extrapolation = gr.Slider(
                label="guidance scale",
                minimum=0,
                maximum=5,
                step=0.05,
                value=1.75
            )
            generation_timesteps_extrapolation = gr.Slider(
                label="timesteps",
                minimum=1,
                maximum=30,
                step=1,
                value=16
            )
            left_extrapolation = gr.Slider(
                label="left extrapolation",
                minimum=0,
                maximum=5,
                step=1,
                value=1
            )
            right_extrapolation = gr.Slider(
                label="right extrapolation",
                minimum=0,
                maximum=5,
                step=1,
                value=1
            )
        generated_img_extrapolation = gr.Image(
            label="Output image"
        )
    examples_extra = gr.Examples(
        label="Text-guided extrapolation examples",
        examples=[
            [
                Image.open("./inpainting_validation/wukong2.jpg").convert("RGB"),
                "the continuous mountain ranges and jungles, with meandering rivers occasionally appearing.",
                2,
                2,
            ],
            [
                Image.open("./inpainting_validation/alpine_lake.jpg").convert("RGB"),
                "a serene natural landscape featuring a clear, blue lake surrounded by lush green trees.",
                2,
                2,
            ],
        ],
        inputs=[extra_input_img, text_prompt_extrapolation, left_extrapolation, right_extrapolation],
    )
    submit_btn_inpainting = gr.Button("Generate: Text-guided Extrapolation")
    submit_btn_inpainting.click(text_guided_extrapolation,
                                [extra_input_img, text_prompt_extrapolation, left_extrapolation, right_extrapolation,
                                 guidance_scale_extrapolation, generation_timesteps_extrapolation],
                                [generated_img_extrapolation])
    with gr.Row():
        with gr.Row():
            chat_input_img = gr.Image(
                label="Input image",
                type="pil",
                image_mode="RGB",
            )
        with gr.Column():
            chatbot = gr.Chatbot()
            msg = gr.Textbox(label="Press Enter to send a message for chat")
            clear = gr.ClearButton([msg, chatbot])
    msg.submit(multimodal_understanding, [chat_input_img, msg, chatbot], [msg, chatbot])

demo.launch()