app.py

import gradio as gr
from share_btn import community_icon_html, loading_icon_html
from tqdm.auto import tqdm

import argparse, os, sys, glob
import cv2
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import trange
from imwatermark import WatermarkEncoder
from itertools import islice
from einops import rearrange
from torchvision.utils import make_grid
import time
from pytorch_lightning import seed_everything
from torch import autocast
from contextlib import contextmanager, nullcontext

sys.path.append("./stable-diffusion/")
from ldm.util import instantiate_from_config
from ldm.models.diffusion.psld import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.models.diffusion.dpm_solver import DPMSolverSampler

# from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from transformers import AutoFeatureExtractor

## lr
import torchvision
import pdb
# os.environ['CUDA_VISIBLE_DEVICES']='1'
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
import subprocess
##

# load safety model
safety_model_id = "CompVis/stable-diffusion-safety-checker"
safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
# safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)

def chunk(it, size):
    it = iter(it)
    return iter(lambda: tuple(islice(it, size)), ())


def numpy_to_pil(images):
    """
    Convert a numpy image or a batch of images to a PIL image.
    """
    if images.ndim == 3:
        images = images[None, ...]
    images = (images * 255).round().astype("uint8")
    pil_images = [Image.fromarray(image) for image in images]

    return pil_images


def load_model_from_config(config, ckpt, verbose=False):
    print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    if "global_step" in pl_sd:
        print(f"Global Step: {pl_sd['global_step']}")
    sd = pl_sd["state_dict"]
    model = instantiate_from_config(config.model)
    m, u = model.load_state_dict(sd, strict=False)
    if len(m) > 0 and verbose:
        print("missing keys:")
        print(m)
    if len(u) > 0 and verbose:
        print("unexpected keys:")
        print(u)

    model = model.to(device)
    model.eval()
    return model


def put_watermark(img, wm_encoder=None):
    if wm_encoder is not None:
        img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
        img = wm_encoder.encode(img, 'dwtDct')
        img = Image.fromarray(img[:, :, ::-1])
    return img


def load_replacement(x):
    try:
        hwc = x.shape
        y = Image.open("assets/rick.jpeg").convert("RGB").resize((hwc[1], hwc[0]))
        y = (np.array(y)/255.0).astype(x.dtype)
        assert y.shape == x.shape
        return y
    except Exception:
        return x


def check_safety(x_image):
    safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
    x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
    assert x_checked_image.shape[0] == len(has_nsfw_concept)
    for i in range(len(has_nsfw_concept)):
        if has_nsfw_concept[i]:
            x_checked_image[i] = load_replacement(x_checked_image[i])
    return x_checked_image, has_nsfw_concept


parser = argparse.ArgumentParser()

parser.add_argument(
    "--prompt",
    type=str,
    nargs="?",
    default="",
    help="the prompt to render"
)
parser.add_argument(
    "--outdir",
    type=str,
    nargs="?",
    help="dir to write results to",
    default="outputs/txt2img-samples"
)
parser.add_argument(
    "--skip_grid",
    action='store_false',
    help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
)
parser.add_argument(
    "--skip_save",
    action='store_true',
    help="do not save individual samples. For speed measurements.",
)
parser.add_argument(
    "--ddim_steps",
    type=int,
    default=200,
    help="number of ddim sampling steps",
)
parser.add_argument(
    "--plms",
    action='store_true',
    help="use plms sampling",
)
parser.add_argument(
    "--dpm_solver",
    action='store_true',
    help="use dpm_solver sampling",
)
parser.add_argument(
    "--laion400m",
    action='store_true',
    help="uses the LAION400M model",
)
parser.add_argument(
    "--fixed_code",
    action='store_true',
    help="if enabled, uses the same starting code across samples ",
)
parser.add_argument(
    "--ddim_eta",
    type=float,
    default=0.0,
    help="ddim eta (eta=0.0 corresponds to deterministic sampling",
)
parser.add_argument(
    "--n_iter",
    type=int,
    default=1,
    help="sample this often",
)
parser.add_argument(
    "--H",
    type=int,
    default=512,
    help="image height, in pixel space",
)
parser.add_argument(
    "--W",
    type=int,
    default=512,
    help="image width, in pixel space",
)
parser.add_argument(
    "--C",
    type=int,
    default=4,
    help="latent channels",
)
parser.add_argument(
    "--f",
    type=int,
    default=8,
    help="downsampling factor",
)
parser.add_argument(
    "--n_samples",
    type=int,
    default=1,
    help="how many samples to produce for each given prompt. A.k.a. batch size",
)
parser.add_argument(
    "--n_rows",
    type=int,
    default=0,
    help="rows in the grid (default: n_samples)",
)
parser.add_argument(
    "--scale",
    type=float,
    default=7.5,
    help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
)
parser.add_argument(
    "--from-file",
    type=str,
    help="if specified, load prompts from this file",
)
parser.add_argument(
    "--config",
    type=str,
    default="configs/stable-diffusion/v1-inference.yaml",
    help="path to config which constructs model",
)
parser.add_argument(
    "--ckpt",
    type=str,
    default="models/ldm/stable-diffusion-v1/model.ckpt",
    help="path to checkpoint of model",
)
parser.add_argument(
    "--seed",
    type=int,
    default=42,
    help="the seed (for reproducible sampling)",
)
parser.add_argument(
    "--precision",
    type=str,
    help="evaluate at this precision",
    choices=["full", "autocast"],
    default="autocast"
)
## 
parser.add_argument(
    "--dps_path",
    type=str,
    default='diffusion-posterior-sampling/',
    help="DPS codebase path",
)
parser.add_argument(
    "--task_config",
    type=str,
    default='configs/inpainting_config.yaml',
    help="task config yml file",
)
parser.add_argument(
    "--diffusion_config",
    type=str,
    default='configs/diffusion_config.yaml',
    help="diffusion config yml file",
)
parser.add_argument(
    "--model_config",
    type=str,
    default='configs/model_config.yaml',
    help="model config yml file",
)
parser.add_argument(
    "--gamma",
    type=float,
    default=1e-1,
    help="inpainting error",
)
parser.add_argument(
    "--omega",
    type=float,
    default=1.0,
    help="measurement error",
)
parser.add_argument(
    "--inpainting",
    type=int,
    default=1,
    help="inpainting",
)
parser.add_argument(
    "--general_inverse",
    type=int,
    default=0,
    help="general inverse",
)
parser.add_argument(
    "--file_id",
    type=str,
    default='00014.png',
    help='input image',
)
parser.add_argument(
    "--skip_low_res",
    action='store_true',
    help='downsample result to 256',
)
parser.add_argument(
    "--ffhq256",
    action='store_true',
    help='load SD weights trained on FFHQ',
)
parser.add_argument(
    "--sd_path",
    type=str,
    default='stable-diffusion/',
    help="SD codebase path",
)
##

opt,_ = parser.parse_known_args()
# pdb.set_trace()

if opt.laion400m:
    print("Falling back to LAION 400M model...")
    opt.config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml"
    opt.ckpt = "models/ldm/text2img-large/model.ckpt"
    
## 
if opt.ffhq256:
    print("Using FFHQ 256 finetuned model...")
    opt.config = "models/ldm/ffhq256/config.yaml"
    opt.ckpt = "models/ldm/ffhq256/model.ckpt"

sys.path.append(opt.sd_path)

opt.outdir = opt.sd_path+opt.outdir
opt.config = opt.sd_path+opt.config
opt.ckpt = opt.sd_path+opt.ckpt
##

seed_everything(opt.seed)

# pdb.set_trace()
print(f"Is CUDA available: {torch.cuda.is_available()}")
# True
print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
# Tesla T4


config = OmegaConf.load(f"{opt.config}")
if os.path.exists(opt.ckpt):
    model = load_model_from_config(config, f"{opt.ckpt}")
else:
    print('No pretrained weights found in ', opt.ckpt)
    # print('Falling back to random weights...')
    # model = instantiate_from_config(config.model)
    
    print('Downloading stable diffusion pretrained weights')
    subprocess.call(['sh', './download.sh'])
    model = load_model_from_config(config, "model.ckpt")

model = model.to(device)

if opt.dpm_solver:
    sampler = DPMSolverSampler(model)
elif opt.plms:
    sampler = PLMSSampler(model)
else:
    # pdb.set_trace()
    sampler = DDIMSampler(model)

os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir

print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
wm = "StableDiffusionV1"
wm_encoder = WatermarkEncoder()
wm_encoder.set_watermark('bytes', wm.encode('utf-8'))

batch_size = opt.n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
if not opt.from_file:
    prompt = opt.prompt
    assert prompt is not None
    data = [batch_size * [prompt]]

else:
    print(f"reading prompts from {opt.from_file}")
    with open(opt.from_file, "r") as f:
        data = f.read().splitlines()
        data = list(chunk(data, batch_size))

sample_path = os.path.join(outpath, "samples")
os.makedirs(sample_path, exist_ok=True)
base_count = len(os.listdir(sample_path))
grid_count = len(os.listdir(outpath)) - 1

def read_content(file_path: str) -> str:
    """read the content of target file
    """
    with open(file_path, 'r', encoding='utf-8') as f:
        content = f.read()

    return content

# def predict(dict, prompt=""):
#     init_image = dict["image"].convert("RGB").resize((512, 512))
#     mask = dict["mask"].convert("RGB").resize((512, 512))
#     output = pipe(prompt = prompt, image=init_image, mask_image=mask,guidance_scale=7.5)
#     return output.images[0], gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)

#########################################################
# Sampler
#########################################################

def predict(ddim_steps, gamma, gluing_kernel_size, gluing_kernel_sigma, omega, dict, prompt=""):
    opt.ddim_steps = ddim_steps
    opt.gamma = gamma
    opt.omega = omega
    if opt.gamma==0 and opt.omega==0:
        opt.inpainting = 0
        opt.general_inverse = 0

    opt.prompt = prompt
    init_image = dict["image"].convert("RGB").resize((512, 512))
    # pdb.set_trace()
    mask = dict["mask"].convert("RGB").resize((512, 512))

    # convert input image to array in [-1, 1]
    init_image = torch.tensor(2 * (np.asarray(init_image) / 255) - 1, device=device)
    mask = torch.tensor((np.asarray(mask) / 255), device=device)
    
    init_image = init_image.type(torch.float32)
    # mask = mask.type(torch.float32)

    # add one dimension for the batch and bring channels first
    init_image = init_image.permute(2, 0, 1).unsqueeze(0)
    mask = mask.permute(2, 0, 1).unsqueeze(0)
    mask[mask>=0.5] = 1.0
    mask[mask<0.5] = 0.0
    mask = 1-mask
    # check if the gadio takes the mask only or the masker image as arguments?



    #########################################################
    ## DPS configs
    #########################################################
    sys.path.append(opt.dps_path)

    import yaml
    from guided_diffusion.measurements import get_noise, get_operator
    from util.img_utils import clear_color, mask_generator
    import torch.nn.functional as f
    import matplotlib.pyplot as plt


    def load_yaml(file_path: str) -> dict:
        with open(file_path) as f:
            config = yaml.load(f, Loader=yaml.FullLoader)
        return config

    
    model_config=opt.dps_path+opt.model_config
    diffusion_config=opt.dps_path+opt.diffusion_config
    task_config=opt.dps_path+opt.task_config

    # pdb.set_trace()

    # Load configurations
    model_config = load_yaml(model_config)
    diffusion_config = load_yaml(diffusion_config)
    task_config = load_yaml(task_config)
    task_config['measurement']['mask_opt']['image_size']=opt.H
    
    # Prepare Operator and noise
    measure_config = task_config['measurement']
    operator = get_operator(device=device, **measure_config['operator'])
    noiser = get_noise(**measure_config['noise'])

    # Exception) In case of inpainting, we need to generate a mask 
    if measure_config['operator']['name'] == 'inpainting':
        mask_gen = mask_generator(
        **measure_config['mask_opt']
        )
    # print(init_image.shape)
    # Exception) In case of inpainging,
    if measure_config['operator'] ['name'] == 'inpainting':
        dps_mask = mask_gen(init_image) # dps mask
        # dps_mask = torch.ones_like(org_image) # no mask
        dps_mask[:,0,:,:] = mask[:,0,:,:]
        dps_mask = dps_mask[:, 0, :, :].unsqueeze(dim=0)
        # Forward measurement model (Ax + n)
        y = operator.forward(init_image, mask=dps_mask)
        y_n = noiser(y)

    else: 
        # Forward measurement model (Ax + n)
        y = operator.forward(init_image)
        y_n = noiser(y)
        mask = None
    #########################################################
    # pdb.set_trace()
    start_code = None
    if opt.fixed_code:
        start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)

    precision_scope = autocast if opt.precision=="autocast" else nullcontext
    with precision_scope("cuda"):
        with model.ema_scope():
            uc = None
            if opt.ffhq256:
                shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
                samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
                                                batch_size=opt.n_samples,
                                                shape=shape,
                                                verbose=False,
                                                eta=opt.ddim_eta,
                                                x_T=start_code,
                                                ip_mask = mask,
                                                measurements = y_n,
                                                operator = operator,
                                                gamma = opt.gamma,
                                                inpainting = opt.inpainting,
                                                omega = opt.omega,
                                                general_inverse=opt.general_inverse,
                                                noiser=noiser,
                                                ffhq256=opt.ffhq256)
            else:
                # pdb.set_trace()
                if opt.scale != 1.0 :
                    uc = model.get_learned_conditioning(batch_size * [""])
                if isinstance(opt.prompt, tuple):
                    opt.prompt = list(opt.prompt)
                c = model.get_learned_conditioning(opt.prompt)
                shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
                samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
                                                conditioning=c,
                                                batch_size=opt.n_samples,
                                                shape=shape,
                                                verbose=False,
                                                unconditional_guidance_scale=opt.scale,
                                                unconditional_conditioning=uc,
                                                eta=opt.ddim_eta,
                                                x_T=start_code,
                                                ip_mask = mask,
                                                measurements = y_n,
                                                operator = operator,
                                                gamma = opt.gamma,
                                                inpainting = opt.inpainting,
                                                omega = opt.omega,
                                                general_inverse=opt.general_inverse,
                                                noiser=noiser)

                x_samples_ddim = model.decode_first_stage(samples_ddim)
                # pdb.set_trace()
                # final step
                if gluing_kernel_size > 0 and gluing_kernel_sigma > 0:
                    blur = torchvision.transforms.GaussianBlur(gluing_kernel_size, sigma=gluing_kernel_sigma)
                    mask = blur(mask)
                x_samples_ddim = mask * init_image + (1-mask) * x_samples_ddim

                x_samples_ddim1 = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
                x_samples_ddim1 = x_samples_ddim1.cpu().permute(0, 2, 3, 1).numpy()
                x_checked_image_torch = torch.from_numpy(x_samples_ddim1).permute(0, 3, 1, 2)
                x_sample1 = 255. * rearrange(x_checked_image_torch[0].cpu().numpy(), 'c h w -> h w c')
                
                
                ## no need to enc-dec again
                encoded_z_0 = model.encode_first_stage(x_samples_ddim.float())
                encoded_z_0 = model.get_first_stage_encoding(encoded_z_0)
                x_samples_ddim = model.decode_first_stage(encoded_z_0)
                
                x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
                x_samples_ddim = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy()

                # x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim)
                # x_checked_image_torch = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)

                # pdb.set_trace()
                x_checked_image_torch = torch.from_numpy(x_samples_ddim).permute(0, 3, 1, 2)
                
                x_sample2 = 255. * rearrange(x_checked_image_torch[0].cpu().numpy(), 'c h w -> h w c')
                # img = Image.fromarray(x_sample2.astype(np.uint8))
                # img = put_watermark(img, wm_encoder)

    image1 = x_sample1.astype("uint8")
    image2 = x_sample2.astype("uint8")
    # pdb.set_trace()
    return image1, image2, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)



css = '''
.container {max-width: 1150px;margin: auto;padding-top: 1.5rem}
#image_upload{min-height:400px}
#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px}
#mask_radio .gr-form{background:transparent; border: none}
#word_mask{margin-top: .75em !important}
#word_mask textarea:disabled{opacity: 0.3}
.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
.dark .footer {border-color: #303030}
.dark .footer>p {background: #0b0f19}
.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
#image_upload .touch-none{display: flex}
@keyframes spin {
    from {
        transform: rotate(0deg);
    }
    to {
        transform: rotate(360deg);
    }
}
#share-btn-container {
    display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
}
#share-btn {
    all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
}
#share-btn * {
    all: unset;
}
#share-btn-container div:nth-child(-n+2){
    width: auto !important;
    min-height: 0px !important;
}
#share-btn-container .wrap {
    display: none !important;
}
'''

image_blocks = gr.Blocks(css=css)
with image_blocks as demo:
    gr.HTML(read_content("header.html"))
    with gr.Group():
        with gr.Box():
            with gr.Row():
                with gr.Column():
                    image = gr.Image(source='upload', tool='sketch', elem_id="image_upload", type="pil", label="Upload").style(height=400)
                    
                    ddim_steps = gr.Slider(minimum = 1, maximum = 1000, step = 1, label = 'Number of diffusion steps (e.g. 400)', value=400)
                    gamma = gr.Slider(minimum = 0, maximum = 1, step=0.01, label = 'Gluing factor (e.g. 1e-1)', value=1e-1)
                    gluing_kernel_size = gr.Slider(minimum = 0, maximum = 100, step=1, label = 'Gluing kernel size (e.g. 15)', value=15)
                    gluing_kernel_sigma = gr.Slider(minimum = 0, maximum = 25, step=1, label = 'Gluing kernel sigma (e.g. 7)', value=7)
                    omega = gr.Slider(minimum = 0, maximum = 2, step=0.1, label = 'Measurement factor (e.g. 1)', value=1)

                    with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
                        prompt = gr.Textbox(placeholder = 'Your prompt (leave empty for posterior sampling)', show_label=False, elem_id="input-text")
                        btn = gr.Button("Inpaint!").style(
                            margin=False,
                            rounded=(False, True, True, False),
                            full_width=False,
                        )
                    
                with gr.Column():
                    image_out1 = gr.Image(label="Output1", elem_id="output-img-1").style(height=400)
                    with gr.Group(elem_id="share-btn-container"):
                        community_icon = gr.HTML(community_icon_html, visible=False)
                        loading_icon = gr.HTML(loading_icon_html, visible=False)            
                    
                    image_out2 = gr.Image(label="Output2", elem_id="output-img-2").style(height=400)
                    with gr.Group(elem_id="share-btn-container"):
                        community_icon = gr.HTML(community_icon_html, visible=False)
                        loading_icon = gr.HTML(loading_icon_html, visible=False)  

            btn.click(fn=predict, inputs=[ddim_steps, gamma, gluing_kernel_size, gluing_kernel_sigma, omega, image, prompt], outputs=[image_out1, image_out2, community_icon, loading_icon])


            gr.HTML(
                """
                    <div class="footer">
                        <p>Image Inpainting by <a href="https://huggingface.co/spaces/PSLD/PSLD" style="text-decoration: underline;" target="_blank">PSLD</a> - Generative Foundation Model by <a href="https://huggingface.co/runwayml" style="text-decoration: underline;" target="_blank">RunwayML</a> - Gradio Demo by 🤗 Hugging Face
                        </p>
                    </div>
                    <div class="acknowledgments">
                        <p><h4>LICENSE</h4>
        The model is licensed with a <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" style="text-decoration: underline;" target="_blank">CreativeML Open RAIL-M</a> license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" target="_blank" style="text-decoration: underline;" target="_blank">read the license.</a></p>
                        <p><h4>Biases and content acknowledgment of Stable Diffusion</h4>
                        Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on the <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B dataset</a>, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. You can read more in the <a href="https://huggingface.co/CompVis/stable-diffusion-v1-4" style="text-decoration: underline;" target="_blank">model card.</a></p>
                        <p><h4>Limitations of PSLD</h4>
                        Our evaluation is based on <a href="https://huggingface.co/runwayml" style="text-decoration: underline;" target="_blank">Stable Diffusion v-1.5</a> which was trained on the <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B dataset</a>.
                        Biases in this dataset and the generative foundation model will be implicitly affecting our algorithm. Our method
                        can work with any latent diffusion model and we expect new foundation models trained on better datasets like <a href="https://www.datacomp.ai/" style="text-decoration: underline;" target="_blank">DataComp</a>
                        to mitigate these issues.                    
                        </p>
                        </div>
                """
            )
            
image_blocks.queue(max_size=100, api_open=False)
image_blocks.launch()