gradio_demo.py

import os

import gradio as gr
from gradio_imageslider import ImageSlider
import argparse
from SUPIR.util import HWC3, upscale_image, fix_resize, convert_dtype
import numpy as np
import torch
from SUPIR.util import create_SUPIR_model, load_QF_ckpt
from PIL import Image
from llava.llava_agent import LLavaAgent
from CKPT_PTH import LLAVA_MODEL_PATH
import einops
import copy
import time
import random
import spaces
from huggingface_hub import hf_hub_download

hf_hub_download(repo_id="laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", filename="open_clip_pytorch_model.bin", local_dir="laion_CLIP-ViT-bigG-14-laion2B-39B-b160k")
hf_hub_download(repo_id="camenduru/SUPIR", filename="sd_xl_base_1.0_0.9vae.safetensors", local_dir="yushan777_SUPIR")
hf_hub_download(repo_id="camenduru/SUPIR", filename="SUPIR-v0F.ckpt", local_dir="yushan777_SUPIR")
hf_hub_download(repo_id="camenduru/SUPIR", filename="SUPIR-v0Q.ckpt", local_dir="yushan777_SUPIR")
hf_hub_download(repo_id="RunDiffusion/Juggernaut-XL-Lightning", filename="Juggernaut_RunDiffusionPhoto2_Lightning_4Steps.safetensors", local_dir="RunDiffusion_Juggernaut-XL-Lightning")

parser = argparse.ArgumentParser()
parser.add_argument("--opt", type=str, default='options/SUPIR_v0.yaml')
parser.add_argument("--ip", type=str, default='127.0.0.1')
parser.add_argument("--port", type=int, default='6688')
parser.add_argument("--no_llava", action='store_true', default=True)#False
parser.add_argument("--use_image_slider", action='store_true', default=True)#False
parser.add_argument("--log_history", action='store_true', default=False)
parser.add_argument("--loading_half_params", action='store_true', default=False)#False
parser.add_argument("--use_tile_vae", action='store_true', default=True)#False
parser.add_argument("--encoder_tile_size", type=int, default=512)
parser.add_argument("--decoder_tile_size", type=int, default=64)
parser.add_argument("--load_8bit_llava", action='store_true', default=False)
args = parser.parse_args()
use_llava = not args.no_llava

if torch.cuda.device_count() > 0:
    if torch.cuda.device_count() >= 2:
        SUPIR_device = 'cuda:0'
        LLaVA_device = 'cuda:1'
    elif torch.cuda.device_count() == 1:
        SUPIR_device = 'cuda:0'
        LLaVA_device = 'cuda:0'
    else:
        SUPIR_device = 'cpu'
        LLaVA_device = 'cpu'

    # load SUPIR
    model, default_setting = create_SUPIR_model(args.opt, SUPIR_sign='Q', load_default_setting=True)
    if args.loading_half_params:
        model = model.half()
    if args.use_tile_vae:
        model.init_tile_vae(encoder_tile_size=args.encoder_tile_size, decoder_tile_size=args.decoder_tile_size)
    model = model.to(SUPIR_device)
    model.first_stage_model.denoise_encoder_s1 = copy.deepcopy(model.first_stage_model.denoise_encoder)
    model.current_model = 'v0-Q'
    ckpt_Q, ckpt_F = load_QF_ckpt(args.opt)

    # load LLaVA
    if use_llava:
        llava_agent = LLavaAgent(LLAVA_MODEL_PATH, device=LLaVA_device, load_8bit=args.load_8bit_llava, load_4bit=False)
    else:
        llava_agent = None

def update_seed(is_randomize_seed, seed):
    if is_randomize_seed:
        return random.randint(0, 2147483647)
    return seed

def check(input_image):
    if input_image is None:
        raise gr.Error("Please provide an image to restore.")

def reset_feedback():
    return 3, ''

@spaces.GPU(duration=240)
def stage1_process(input_image, gamma_correction):
    print('Start stage1_process')
    if torch.cuda.device_count() == 0:
        gr.Warning('Set this space to GPU config to make it work.')
        return None
    torch.cuda.set_device(SUPIR_device)
    LQ = HWC3(input_image)
    LQ = fix_resize(LQ, 512)
    # stage1
    LQ = np.array(LQ) / 255 * 2 - 1
    LQ = torch.tensor(LQ, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(SUPIR_device)[:, :3, :, :]
    LQ = model.batchify_denoise(LQ, is_stage1=True)
    LQ = (LQ[0].permute(1, 2, 0) * 127.5 + 127.5).cpu().numpy().round().clip(0, 255).astype(np.uint8)
    # gamma correction
    LQ = LQ / 255.0
    LQ = np.power(LQ, gamma_correction)
    LQ *= 255.0
    LQ = LQ.round().clip(0, 255).astype(np.uint8)
    print('End stage1_process')
    return LQ

@spaces.GPU(duration=240)
def llave_process(input_image, temperature, top_p, qs=None):
    print('Start llave_process')
    if torch.cuda.device_count() == 0:
        gr.Warning('Set this space to GPU config to make it work.')
        return 'Set this space to GPU config to make it work.'
    torch.cuda.set_device(LLaVA_device)
    if use_llava:
        LQ = HWC3(input_image)
        LQ = Image.fromarray(LQ.astype('uint8'))
        captions = llava_agent.gen_image_caption([LQ], temperature=temperature, top_p=top_p, qs=qs)
    else:
        captions = ['LLaVA is not available. Please add text manually.']
    print('End llave_process')
    return captions[0]

@spaces.GPU(duration=240)
def stage2_process(input_image, prompt, a_prompt, n_prompt, num_samples, upscale, edm_steps, s_stage1, s_stage2,
                   s_cfg, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction,
                   linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2, model_select):
    print('Start stage2_process')
    if torch.cuda.device_count() == 0:
        gr.Warning('Set this space to GPU config to make it work.')
        return None, None
    torch.cuda.set_device(SUPIR_device)
    event_id = str(time.time_ns())
    event_dict = {'event_id': event_id, 'localtime': time.ctime(), 'prompt': prompt, 'a_prompt': a_prompt,
                  'n_prompt': n_prompt, 'num_samples': num_samples, 'upscale': upscale, 'edm_steps': edm_steps,
                  's_stage1': s_stage1, 's_stage2': s_stage2, 's_cfg': s_cfg, 'seed': seed, 's_churn': s_churn,
                  's_noise': s_noise, 'color_fix_type': color_fix_type, 'diff_dtype': diff_dtype, 'ae_dtype': ae_dtype,
                  'gamma_correction': gamma_correction, 'linear_CFG': linear_CFG, 'linear_s_stage2': linear_s_stage2,
                  'spt_linear_CFG': spt_linear_CFG, 'spt_linear_s_stage2': spt_linear_s_stage2,
                  'model_select': model_select}

    if model_select != model.current_model:
        print('load ' + model_select)
        if model_select == 'v0-Q':
            model.load_state_dict(ckpt_Q, strict=False)
        elif model_select == 'v0-F':
            model.load_state_dict(ckpt_F, strict=False)
        model.current_model = model_select
    input_image = HWC3(input_image)
    input_image = upscale_image(input_image, upscale, unit_resolution=32,
                                min_size=1024)

    LQ = np.array(input_image) / 255.0
    LQ = np.power(LQ, gamma_correction)
    LQ *= 255.0
    LQ = LQ.round().clip(0, 255).astype(np.uint8)
    LQ = LQ / 255 * 2 - 1
    LQ = torch.tensor(LQ, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(SUPIR_device)[:, :3, :, :]
    if use_llava:
        captions = [prompt]
    else:
        captions = ['']

    model.ae_dtype = convert_dtype(ae_dtype)
    model.model.dtype = convert_dtype(diff_dtype)

    samples = model.batchify_sample(LQ, captions, num_steps=edm_steps, restoration_scale=s_stage1, s_churn=s_churn,
                                    s_noise=s_noise, cfg_scale=s_cfg, control_scale=s_stage2, seed=seed,
                                    num_samples=num_samples, p_p=a_prompt, n_p=n_prompt, color_fix_type=color_fix_type,
                                    use_linear_CFG=linear_CFG, use_linear_control_scale=linear_s_stage2,
                                    cfg_scale_start=spt_linear_CFG, control_scale_start=spt_linear_s_stage2)

    x_samples = (einops.rearrange(samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().round().clip(
        0, 255).astype(np.uint8)
    results = [x_samples[i] for i in range(num_samples)]

    if args.log_history:
        os.makedirs(f'./history/{event_id[:5]}/{event_id[5:]}', exist_ok=True)
        with open(f'./history/{event_id[:5]}/{event_id[5:]}/logs.txt', 'w') as f:
            f.write(str(event_dict))
        f.close()
        Image.fromarray(input_image).save(f'./history/{event_id[:5]}/{event_id[5:]}/LQ.png')
        for i, result in enumerate(results):
            Image.fromarray(result).save(f'./history/{event_id[:5]}/{event_id[5:]}/HQ_{i}.png')
    print('End stage2_process')
    return [input_image] + results, [input_image] + results, event_id

def load_and_reset(param_setting):
    print('Start load_and_reset')
    if torch.cuda.device_count() == 0:
        gr.Warning('Set this space to GPU config to make it work.')
        return None, None, None, None, None, None, None, None, None, None, None, None, None
    edm_steps = default_setting.edm_steps
    s_stage2 = 1.0
    s_stage1 = -1.0
    s_churn = 5
    s_noise = 1.003
    a_prompt = 'Cinematic, High Contrast, highly detailed, taken using a Canon EOS R camera, hyper detailed photo - ' \
               'realistic maximum detail, 32k, Color Grading, ultra HD, extreme meticulous detailing, skin pore ' \
               'detailing, hyper sharpness, perfect without deformations.'
    n_prompt = 'painting, oil painting, illustration, drawing, art, sketch, anime, cartoon, CG Style, ' \
               '3D render, unreal engine, blurring, dirty, messy, worst quality, low quality, frames, watermark, ' \
               'signature, jpeg artifacts, deformed, lowres, over-smooth'
    color_fix_type = 'Wavelet'
    spt_linear_s_stage2 = 0.0
    linear_s_stage2 = False
    linear_CFG = True
    if param_setting == "Quality":
        s_cfg = default_setting.s_cfg_Quality
        spt_linear_CFG = default_setting.spt_linear_CFG_Quality
    elif param_setting == "Fidelity":
        s_cfg = default_setting.s_cfg_Fidelity
        spt_linear_CFG = default_setting.spt_linear_CFG_Fidelity
    else:
        raise NotImplementedError
    gr.Info('The parameters are reset.')
    print('End load_and_reset')
    return edm_steps, s_cfg, s_stage2, s_stage1, s_churn, s_noise, a_prompt, n_prompt, color_fix_type, linear_CFG, \
        linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2


def submit_feedback(event_id, fb_score, fb_text):
    if args.log_history:
        with open(f'./history/{event_id[:5]}/{event_id[5:]}/logs.txt', 'r') as f:
            event_dict = eval(f.read())
        f.close()
        event_dict['feedback'] = {'score': fb_score, 'text': fb_text}
        with open(f'./history/{event_id[:5]}/{event_id[5:]}/logs.txt', 'w') as f:
            f.write(str(event_dict))
        f.close()
        return 'Submit successfully, thank you for your comments!'
    else:
        return 'Submit failed, the server is not set to log history.'

title_html = """
    <h1><center>SUPIR</center></h1>
    <center>Upscale your images up to x8 freely, without account, without watermark and download it</center>
    <br/>
    
    <p>SUPIR is a practicing model scaling for photo-realistic image restoration. It is still a research project under tested and is not yet a stable commercial product. LLaVa is not available in this demo. If you want to auto-generate the description of your image, use another <a href="https://huggingface.co/spaces/MaziyarPanahi/llava-llama-3-8b">LLaVa space</a>.
    
    <p><center><a href="https://arxiv.org/abs/2401.13627">Paper</a> &emsp; <a href="http://supir.xpixel.group/">Project Page</a> &emsp; <a href="https://github.com/Fanghua-Yu/SUPIR/blob/master/assets/DemoGuide.png">How to play</a> &emsp; <a href="https://huggingface.co/blog/MonsterMMORPG/supir-sota-image-upscale-better-than-magnific-ai">Local Install Guide</a></center></p>
    """


claim_md = """
## **Terms of use**

By using this service, users are required to agree to the following terms: The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research. Please submit a feedback to us if you get any inappropriate answer! We will collect those to keep improving our models. For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality.

## **License**

The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/Fanghua-Yu/SUPIR) of SUPIR.
"""

# Gradio interface
with gr.Blocks(title="SUPIR") as interface:
    if torch.cuda.device_count() == 0:
        with gr.Row():
            gr.HTML("""
    <p style="background-color: red;"><big><big><big><b>⚠️To use SUPIR, <a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/SUPIR?duplicate=true">Duplicate this space</a> and set a GPU with 30 GB VRAM.</b>
    
    You can't use SUPIR directly here because this space runs on a CPU, which is not enough for SUPIR. This is a template space. Please provide feedback if you have issues.
    </big></big></big></p>
    """)
    with gr.Row():
        gr.HTML(title_html)

    with gr.Row():
        with gr.Column():
            with gr.Row(equal_height=True):
                with gr.Column():
                    gr.Markdown("<center>Input</center>")
                    input_image = gr.Image(type="numpy", elem_id="image-input", height=400, width=400)
                with gr.Column():
                    gr.Markdown("<center>Pre-denoising Output</center>")
                    denoise_image = gr.Image(type="numpy", elem_id="image-s1", height=400, width=400)
            prompt = gr.Textbox(label="Image description", value="", placeholder="A person, walking, in a town, Summer, photorealistic", lines=3)

            with gr.Accordion("Pre-denoising options", open=False):
                gamma_correction = gr.Slider(label="Gamma Correction", minimum=0.1, maximum=2.0, value=1.0, step=0.1)

            with gr.Accordion("LLaVA options", open=False, visible=False):
                temperature = gr.Slider(label="Temperature", info = "lower=Always similar, higher=More creative", minimum=0., maximum=1.0, value=0.2, step=0.1)
                top_p = gr.Slider(label="Top P", info = "Percent of tokens shortlisted", minimum=0., maximum=1.0, value=0.7, step=0.1)
                qs = gr.Textbox(label="Question", info="Ask LLaVa what description you want", value="Describe the image and its style in a very detailed manner. The image is a realistic photography, not an art painting.", lines=3)

            with gr.Accordion("Restoring options", open=False):
                num_samples = gr.Slider(label="Num Samples", info="Number of generated results; I discourage to increase because the process is limited to 3 min", minimum=1, maximum=4 if not args.use_image_slider else 1
                                        , value=1, step=1)
                upscale = gr.Slider(label="Upscale factor", info="Resolution x1, x2, x3, x4, x5, x6, x7 or x8", minimum=1, maximum=8, value=2, step=1)
                edm_steps = gr.Slider(label="Steps", info="lower=faster, higher=more details", minimum=1, maximum=200, value=default_setting.edm_steps if torch.cuda.device_count() > 0 else 1, step=1)
                s_cfg = gr.Slider(label="Text Guidance Scale", info="lower=follow the image, higher=follow the prompt", minimum=1.0, maximum=15.0,
                                  value=default_setting.s_cfg_Quality if torch.cuda.device_count() > 0 else 1.0, step=0.1)
                s_stage2 = gr.Slider(label="Restoring Guidance Strength", minimum=0., maximum=1., value=1., step=0.05)
                s_stage1 = gr.Slider(label="Pre-denoising Guidance Strength", minimum=-1.0, maximum=6.0, value=-1.0, step=1.0)
                randomize_seed = gr.Checkbox(label = "\U0001F3B2 Randomize seed", value = True, info = "If checked, result is always different")
                seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True)
                s_churn = gr.Slider(label="S-Churn", minimum=0, maximum=40, value=5, step=1)
                s_noise = gr.Slider(label="S-Noise", minimum=1.0, maximum=1.1, value=1.003, step=0.001)
                a_prompt = gr.Textbox(label="Default Positive Prompt",
                                      info="Describe what the image represents",
                                      value='Cinematic, High Contrast, highly detailed, taken using a Canon EOS R '
                                            'camera, hyper detailed photo - realistic maximum detail, 32k, Color '
                                            'Grading, ultra HD, extreme meticulous detailing, skin pore detailing, '
                                            'hyper sharpness, perfect without deformations.',
                                      lines=3)
                n_prompt = gr.Textbox(label="Default Negative Prompt",
                                      info="List what the image does NOT represent",
                                      value='painting, oil painting, illustration, drawing, art, sketch, anime, '
                                            'cartoon, CG Style, 3D render, unreal engine, blurring, bokeh, ugly, dirty, messy, '
                                            'worst quality, low quality, frames, watermark, signature, jpeg artifacts, '
                                            'deformed, lowres, over-smooth',
                                      lines=3)
                with gr.Row():
                    with gr.Column():
                        linear_CFG = gr.Checkbox(label="Linear CFG", value=True)
                        spt_linear_CFG = gr.Slider(label="CFG Start", minimum=1.0,
                                                        maximum=9.0, value=default_setting.spt_linear_CFG_Quality if torch.cuda.device_count() > 0 else 1.0, step=0.5)
                    with gr.Column():
                        linear_s_stage2 = gr.Checkbox(label="Linear Restoring Guidance", value=False)
                        spt_linear_s_stage2 = gr.Slider(label="Guidance Start", minimum=0.,
                                                        maximum=1., value=0., step=0.05)
                with gr.Row():
                    with gr.Column():
                        diff_dtype = gr.Radio(['fp32', 'fp16', 'bf16'], label="Diffusion Data Type", value="fp16",
                                              interactive=True)
                    with gr.Column():
                        ae_dtype = gr.Radio(['fp32', 'bf16'], label="Auto-Encoder Data Type", value="bf16",
                                            interactive=True)
                    with gr.Column():
                        color_fix_type = gr.Radio(["None", "AdaIn", "Wavelet"], label="Color-Fix Type", info="AdaIn=Adaptive Instance Normalization, Wavelet=For JPEG artifacts", value="Wavelet",
                                                  interactive=True)
                    with gr.Column():
                        model_select = gr.Radio(["v0-Q", "v0-F"], label="Model Selection", info="Q=Quality, F=Fidelity", value="v0-Q",
                                                interactive=True)

        with gr.Column():
            gr.Markdown("<center>Restoring Output</center>")
            result_slider = ImageSlider(label='Output', show_label=False, elem_id="slider1")
            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery1")
            with gr.Row():
                with gr.Column():
                    denoise_button = gr.Button(value="Pre-denoise")
                with gr.Column(visible=False):
                    llave_button = gr.Button(value="Generate description by LlaVa (disabled)")
                with gr.Column():
                    diffusion_button = gr.Button(value="🚀 Upscale/Restore", variant = "primary")
            with gr.Row():
                with gr.Column():
                    param_setting = gr.Radio(["Quality", "Fidelity"], interactive=True, label="Parameter setting", value="Quality")
                with gr.Column():
                    restart_button = gr.Button(value="Apply parameter setting", scale=2)

            with gr.Accordion("Feedback", open=True):
                fb_score = gr.Slider(label="Feedback Score", minimum=1, maximum=5, value=3, step=1,
                                     interactive=True)
                fb_text = gr.Textbox(label="Feedback Text", value="", placeholder='Please enter your feedback here.')
                submit_button = gr.Button(value="Submit Feedback")
    with gr.Row():
        gr.Markdown(claim_md)
        event_id = gr.Textbox(label="Event ID", value="", visible=False)
    
    denoise_button.click(fn = check, inputs = [
        input_image
    ], outputs = [], queue = False, show_progress = False).success(fn = stage1_process, inputs = [
        input_image,
        gamma_correction
    ], outputs=[
        denoise_image
    ])

    llave_button.click(fn = check, inputs = [
        denoise_image
    ], outputs = [], queue = False, show_progress = False).success(fn = llave_process, inputs = [
        denoise_image,
        temperature,
        top_p,
        qs
    ], outputs = [
        prompt
    ])

    diffusion_button.click(fn = update_seed, inputs = [
        randomize_seed,
        seed
    ], outputs = [
        seed
    ], queue = False, show_progress = False).then(fn = check, inputs = [
        input_image
    ], outputs = [], queue = False, show_progress = False).then(fn = reset_feedback, inputs = [], outputs = [
        fb_score,
        fb_text
    ], queue = False, show_progress = False).success(fn=stage2_process, inputs = [
        input_image,
        prompt,
        a_prompt,
        n_prompt,
        num_samples,
        upscale,
        edm_steps,
        s_stage1,
        s_stage2,
        s_cfg,
        seed,
        s_churn,
        s_noise,
        color_fix_type,
        diff_dtype,
        ae_dtype,
        gamma_correction,
        linear_CFG,
        linear_s_stage2,
        spt_linear_CFG,
        spt_linear_s_stage2,
        model_select
    ], outputs = [
        result_slider,
        result_gallery,
        event_id
    ])

    restart_button.click(fn = load_and_reset, inputs = [
        param_setting
    ], outputs = [
        edm_steps,
        s_cfg,
        s_stage2,
        s_stage1,
        s_churn,
        s_noise,
        a_prompt,
        n_prompt,
        color_fix_type,
        linear_CFG,
        linear_s_stage2,
        spt_linear_CFG,
        spt_linear_s_stage2
    ])

    submit_button.click(fn = submit_feedback, inputs = [
        event_id,
        fb_score,
        fb_text
    ], outputs = [
        fb_text
    ])
        
    interface.queue(10).launch()