src/comfyui/custom_nodes/ComfyUI-Flowty-LDSR/ldsrlib/LDSR.py

import PIL.Image

import nodes
from .ldm.util import instantiate_from_config
from .ldm.models.diffusion.ddim import DDIMSampler
from .ldm.util import ismap

from PIL import Image
from einops import rearrange, repeat
import torch, torchvision
import time
from omegaconf import OmegaConf
import numpy as np
from os import path
import warnings
from comfy import model_management
import comfy
from PIL import ImageOps

warnings.filterwarnings("ignore", category=UserWarning)


class LDSR():
    def __init__(self, modelPath=None, model=None, torchdevice=model_management.get_torch_device(), on_progress=None, yamlPath=path.join(path.dirname(__file__), "config.yaml")):
        self.modelPath = modelPath
        self.model = model
        self.yamlPath = yamlPath
        self.torchdevice = torchdevice
        self.progress_hook = on_progress if on_progress else None

    @staticmethod
    def normalize_image(image):
        w, h = image.size

        # ensure (min length > 128)
        if h < w and h < 128:
            scale_ratio = 128 / h
            h = 128
            w = int(scale_ratio * w)
        elif w < 128:
            scale_ratio = 128 / w
            w = 128
            h = int(scale_ratio * h)

        resample = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
        image = image.resize((w, h), resample=resample)

        # ensure (multiply of 64)
        w_pad = 64 - w % 64
        h_pad = 64 - h % 64

        padded_image = Image.new("RGB", (w + w_pad, h + h_pad), color="black")
        padded_image.paste(image, (0, 0))

        return padded_image, w_pad, h_pad

    @staticmethod
    def remove_padding(prev_pil, image, w_pad, h_pad):
        if w_pad == 0 and h_pad == 0:
            return image

        w1, h1 = prev_pil.size
        h2, w2, _ = image.size()

        scale_ratio = h2 / h1
        w_pad = float.__ceil__(w_pad * scale_ratio)
        h_pad = float.__ceil__(h_pad * scale_ratio)

        return image[:h2-h_pad, :w2-w_pad, :]


    @staticmethod
    def load_model_from_path(modelPath, device=model_management.get_torch_device(), yamlPath=path.join(path.dirname(__file__), "config.yaml")):
        print(f"Loading model from {modelPath}")
        pl_sd = torch.load(modelPath, map_location="cpu")
        sd = pl_sd["state_dict"]
        config = OmegaConf.load(yamlPath)
        model = instantiate_from_config(config.model)
        model.load_state_dict(sd, strict=False)

        model.to(device)
        model.eval()

        return {"model": model}

    def load_model_from_config(self):
        if self.model is None:
            self.model = LDSR.load_model_from_path(self.modelPath, self.torchdevice)
        else:
            self.model['model'].to(self.torchdevice)

        return self.model

    def progress_callback(self, i):
        if self.progress_hook:
            self.progress_hook(i)

    def run(self, model, image, task, custom_steps, eta, resize_enabled=False, classifier_ckpt=None, global_step=None):
        def make_convolutional_sample(batch, model, mode="vanilla", custom_steps=None, eta=1.0, swap_mode=False,
                                      masked=False,
                                      invert_mask=True, quantize_x0=False, custom_schedule=None, decode_interval=1000,
                                      resize_enabled=False, custom_shape=None, temperature=1., noise_dropout=0.,
                                      corrector=None,
                                      corrector_kwargs=None, x_T=None, save_intermediate_vid=False, make_progrow=True,
                                      ddim_use_x0_pred=False):
            log = dict()

            z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
                                                return_first_stage_outputs=True,
                                                force_c_encode=not (hasattr(model, 'split_input_params')
                                                                    and model.cond_stage_key == 'coordinates_bbox'),
                                                return_original_cond=True)

            log_every_t = 1 if save_intermediate_vid else None

            if custom_shape is not None:
                z = torch.randn(custom_shape)
                # print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")

            z0 = None

            log["input"] = x
            log["reconstruction"] = xrec

            if ismap(xc):
                log["original_conditioning"] = model.to_rgb(xc)
                if hasattr(model, 'cond_stage_key'):
                    log[model.cond_stage_key] = model.to_rgb(xc)

            else:
                log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
                if model.cond_stage_model:
                    log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
                    if model.cond_stage_key == 'class_label':
                        log[model.cond_stage_key] = xc[model.cond_stage_key]

            with model.ema_scope("Plotting"):
                t0 = time.time()
                img_cb = None
                sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
                                                        eta=eta,
                                                        callback=self.progress_callback,
                                                        quantize_x0=quantize_x0, img_callback=img_cb, mask=None, x0=z0,
                                                        temperature=temperature, noise_dropout=noise_dropout,
                                                        score_corrector=corrector, corrector_kwargs=corrector_kwargs,
                                                        x_T=x_T, log_every_t=log_every_t)
                t1 = time.time()

                if ddim_use_x0_pred:
                    sample = intermediates['pred_x0'][-1]

            x_sample = model.decode_first_stage(sample)

            try:
                x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
                log["sample_noquant"] = x_sample_noquant
                log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
            except:
                pass

            log["sample"] = x_sample
            log["time"] = t1 - t0

            return log

        def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
                            mask=None, x0=None, quantize_x0=False, img_callback=None,
                            temperature=1., noise_dropout=0., score_corrector=None,
                            corrector_kwargs=None, x_T=None, log_every_t=None
                            ):
            ddim = DDIMSampler(model)
            bs = shape[0]  # dont know where this comes from but wayne
            shape = shape[1:]  # cut batch dim
            print(f"Sampling with eta = {eta}; steps: {steps}")
            samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond,
                                                 callback=callback,
                                                 normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
                                                 mask=mask, x0=x0, temperature=temperature, verbose=False,
                                                 score_corrector=score_corrector,
                                                 corrector_kwargs=corrector_kwargs, x_T=x_T)
            return samples, intermediates

        # global stride
        def get_cond(mode, img):
            example = dict()
            if mode == "superresolution":
                up_f = 4
                # visualize_cond_img(selected_path)

                c = img.convert('RGB')
                c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)

                c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]],
                                                                antialias=True)
                c_up = rearrange(c_up, '1 c h w -> 1 h w c')
                c = rearrange(c, '1 c h w -> 1 h w c')
                c = 2. * c - 1.
                c = c.to(self.torchdevice)
                example["LR_image"] = c
                example["image"] = c_up

            return example

        example = get_cond(task, image)

        save_intermediate_vid = False
        n_runs = 1
        masked = False
        guider = None
        ckwargs = None
        mode = 'ddim'
        ddim_use_x0_pred = False
        temperature = 1.
        eta = eta
        make_progrow = True
        custom_shape = None

        height, width = example["image"].shape[1:3]
        split_input = height >= 128 and width >= 128

        if split_input:
            ks = 128
            stride = 64
            vqf = 4  #
            model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
                                        "vqf": vqf,
                                        "patch_distributed_vq": True,
                                        "tie_braker": False,
                                        "clip_max_weight": 0.5,
                                        "clip_min_weight": 0.01,
                                        "clip_max_tie_weight": 0.5,
                                        "clip_min_tie_weight": 0.01}
        else:
            if hasattr(model, "split_input_params"):
                delattr(model, "split_input_params")

        invert_mask = False

        x_T = None
        for n in range(n_runs):
            if custom_shape is not None:
                x_T = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
                x_T = repeat(x_T, '1 c h w -> b c h w', b=custom_shape[0])

            logs = make_convolutional_sample(example, model,
                                             mode=mode, custom_steps=custom_steps,
                                             eta=eta, swap_mode=False, masked=masked,
                                             invert_mask=invert_mask, quantize_x0=False,
                                             custom_schedule=None, decode_interval=10,
                                             resize_enabled=resize_enabled, custom_shape=custom_shape,
                                             temperature=temperature, noise_dropout=0.,
                                             corrector=guider, corrector_kwargs=ckwargs, x_T=x_T,
                                             save_intermediate_vid=save_intermediate_vid,
                                             make_progrow=make_progrow, ddim_use_x0_pred=ddim_use_x0_pred
                                             )
        return logs

    @torch.no_grad()
    def superResolution(self, image, ddimSteps=100, preDownScale='None', postDownScale='None', downsampleMethod="Lanczos"):
        diffMode = 'superresolution'
        model = self.load_model_from_config()

        # Run settings

        diffusion_steps = int(ddimSteps)  # @param [25, 50, 100, 250, 500, 1000]
        eta = 1.0  # @param  {type: 'raw'}
        stride = 0  # not working atm

        # ####Scaling options:
        # Downsampling to 256px first will often improve the final image and runs faster.

        # You can improve sharpness without upscaling by upscaling and then downsampling to the original size (i.e. Super Resolution)
        pre_downsample = preDownScale  # @param ['None', '1/2', '1/4']

        post_downsample = postDownScale  # @param ['None', 'Original Size', '1/2', '1/4']

        # Nearest gives sharper results, but may look more pixellated. Lancoz is much higher quality, but result may be less crisp.
        downsample_method = downsampleMethod  # @param ['Nearest', 'Lanczos']

        i = 255. * image.cpu().numpy()
        im_og = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
        width_og, height_og = im_og.size

        # Downsample Pre
        if pre_downsample == '1/2':
            downsample_rate = 2
        elif pre_downsample == '1/4':
            downsample_rate = 4
        else:
            downsample_rate = 1

        width_downsampled_pre = width_og // downsample_rate
        height_downsampled_pre = height_og // downsample_rate
        if downsample_rate != 1:
            print(f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
            im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)

        im_og, w_pad, h_pad = LDSR.normalize_image(im_og)

        logs = self.run(model["model"], im_og, diffMode, diffusion_steps, eta)

        sample = logs["sample"]
        sample = sample.detach().cpu()
        sample = torch.clamp(sample, -1., 1.)
        sample = (sample + 1.) / 2. * 255
        sample = sample.numpy().astype(np.uint8)
        sample = np.transpose(sample, (0, 2, 3, 1))
        a = Image.fromarray(sample[0])

        # Downsample Post
        if post_downsample == '1/2':
            downsample_rate = 2
        elif post_downsample == '1/4':
            downsample_rate = 4
        else:
            downsample_rate = 1

        width, height = a.size
        width_downsampled_post = width // downsample_rate
        height_downsampled_post = height // downsample_rate

        if downsample_method == 'Lanczos':
            aliasing = Image.LANCZOS
        else:
            aliasing = Image.NEAREST

        if downsample_rate != 1:
            print(f'Downsampling from [{width}, {height}] to [{width_downsampled_post}, {height_downsampled_post}]')
            a = a.resize((width_downsampled_post, height_downsampled_post), aliasing)
        elif post_downsample == 'Original Size':
            print(f'Downsampling from [{width}, {height}] to Original Size [{width_og}, {height_og}]')
            a = a.resize((width_og+w_pad, height_og+h_pad), aliasing)

        out = np.array(a).astype(np.float32) / 255.0

        # Finalize
        result_image = torch.from_numpy(out)
        result_image = LDSR.remove_padding(im_og, result_image, w_pad, h_pad)

        model['model'].cpu()
        result_image.cpu()

        return result_image