MagicQuill/comfyui_utils.py

import os
import folder_paths
import comfy.diffusers_load
import comfy.samplers
import comfy.sample
import comfy.sd
import comfy.utils
import comfy.controlnet
import comfy.clip_vision
import comfy.model_management
from comfy.cli_args import args
import torch
import torch.nn as nn
import numpy as np
import latent_preview
from PIL import Image
from einops import rearrange
import scipy.ndimage
import sys
import cv2
from magic_utils import HWC3, apply_color, common_input_validate, resize_image_with_pad
from pidi import pidinet


supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl'])
folder_names_and_paths = {}

base_path = os.path.dirname(os.path.realpath(__file__))
models_dir = os.path.join(base_path, "../models")

folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_pt_extensions)
folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"])

folder_names_and_paths["loras"] = ([os.path.join(models_dir, "loras")], supported_pt_extensions)
folder_names_and_paths["vae"] = ([os.path.join(models_dir, "vae")], supported_pt_extensions)
folder_names_and_paths["clip"] = ([os.path.join(models_dir, "clip")], supported_pt_extensions)
folder_names_and_paths["unet"] = ([os.path.join(models_dir, "unet")], supported_pt_extensions)
folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions)
folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions)
folder_names_and_paths["embeddings"] = ([os.path.join(models_dir, "embeddings")], supported_pt_extensions)
folder_names_and_paths["diffusers"] = ([os.path.join(models_dir, "diffusers")], ["folder"])
folder_names_and_paths["vae_approx"] = ([os.path.join(models_dir, "vae_approx")], supported_pt_extensions)

folder_names_and_paths["controlnet"] = ([os.path.join(models_dir, "controlnet"), os.path.join(models_dir, "t2i_adapter")], supported_pt_extensions)
folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], supported_pt_extensions)
folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions)
folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions)
folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")], supported_pt_extensions)
folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""})

def common_annotator_call(model, tensor_image, input_batch=False, show_pbar=True, **kwargs):
    if "detect_resolution" in kwargs:
        del kwargs["detect_resolution"] #Prevent weird case?

    if "resolution" in kwargs:
        detect_resolution = kwargs["resolution"] if type(kwargs["resolution"]) == int and kwargs["resolution"] >= 64 else 512
        del kwargs["resolution"]
    else:
        detect_resolution = 512

    if input_batch:
        np_images = np.asarray(tensor_image * 255., dtype=np.uint8)
        np_results = model(np_images, output_type="np", detect_resolution=detect_resolution, **kwargs)
        return torch.from_numpy(np_results.astype(np.float32) / 255.0)

    batch_size = tensor_image.shape[0]
    if show_pbar:
        pbar = comfy.utils.ProgressBar(batch_size)
    out_tensor = None
    for i, image in enumerate(tensor_image):
        np_image = np.asarray(image.cpu() * 255., dtype=np.uint8)
        np_result = model(np_image, output_type="np", detect_resolution=detect_resolution, **kwargs)
        out = torch.from_numpy(np_result.astype(np.float32) / 255.0)
        if out_tensor is None:
            out_tensor = torch.zeros(batch_size, *out.shape, dtype=torch.float32)
        out_tensor[i] = out
        if show_pbar:
            pbar.update(1)
    return out_tensor

class CheckpointLoaderSimple:
    def load_checkpoint(self, ckpt_name):
        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
        print("Loading checkpoint from:", ckpt_path)
        out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
        return out[:3]

class ControlNetLoader:
    def load_controlnet(self, control_net_name):
        controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
        controlnet = comfy.controlnet.load_controlnet(controlnet_path)
        return (controlnet, )

class ControlNetApplyAdvanced:
    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent):
        if strength == 0:
            return (positive, negative)

        control_hint = image.movedim(-1,1)
        cnets = {}

        out = []
        for conditioning in [positive, negative]:
            c = []
            for t in conditioning:
                d = t[1].copy()

                prev_cnet = d.get('control', None)
                if prev_cnet in cnets:
                    c_net = cnets[prev_cnet]
                else:
                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent))
                    c_net.set_previous_controlnet(prev_cnet)
                    cnets[prev_cnet] = c_net

                d['control'] = c_net
                d['control_apply_to_uncond'] = False
                n = [t[0], d]
                c.append(n)
            out.append(c)
        return (out[0], out[1])
    
class CLIPTextEncode:
    def encode(self, clip, text):
        tokens = clip.tokenize(text)
        cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
        return ([[cond, {"pooled_output": pooled}]], )

class KSampler:
    def common_ksampler(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
        latent_image = latent["samples"]
        latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)

        if disable_noise:
            noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
        else:
            batch_inds = latent["batch_index"] if "batch_index" in latent else None
            noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)

        noise_mask = None
        if "noise_mask" in latent:
            noise_mask = latent["noise_mask"]

        callback = latent_preview.prepare_callback(model, steps)
        disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
        samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
                                    denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
                                    force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
        out = latent.copy()
        out["samples"] = samples
        return (out, )

    def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):
        return self.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)

class VAEDecode:
    def decode(self, vae, samples):
        return (vae.decode(samples["samples"]), )

class ColorDetector:
    def __call__(self, input_image=None, detect_resolution=2048, output_type=None, **kwargs):
        input_image, output_type = common_input_validate(input_image, output_type, **kwargs)
        input_image = HWC3(input_image)
        detected_map = HWC3(apply_color(input_image, detect_resolution))
        
        if output_type == "pil":
            detected_map = Image.fromarray(detected_map)
            
        return detected_map

class Color_Preprocessor:
    def execute(self, image, resolution=512, **kwargs):
        return (common_annotator_call(ColorDetector(), image, resolution=resolution), )
    
norm_layer = nn.InstanceNorm2d
class ResidualBlock(nn.Module):
    def __init__(self, in_features):
        super(ResidualBlock, self).__init__()

        conv_block = [  nn.ReflectionPad2d(1),
                        nn.Conv2d(in_features, in_features, 3),
                        norm_layer(in_features),
                        nn.ReLU(inplace=True),
                        nn.ReflectionPad2d(1),
                        nn.Conv2d(in_features, in_features, 3),
                        norm_layer(in_features)
                        ]

        self.conv_block = nn.Sequential(*conv_block)

    def forward(self, x):
        return x + self.conv_block(x)

class Generator(nn.Module):
    def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
        super(Generator, self).__init__()

        # Initial convolution block
        model0 = [   nn.ReflectionPad2d(3),
                    nn.Conv2d(input_nc, 64, 7),
                    norm_layer(64),
                    nn.ReLU(inplace=True) ]
        self.model0 = nn.Sequential(*model0)

        # Downsampling
        model1 = []
        in_features = 64
        out_features = in_features*2
        for _ in range(2):
            model1 += [  nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
                        norm_layer(out_features),
                        nn.ReLU(inplace=True) ]
            in_features = out_features
            out_features = in_features*2
        self.model1 = nn.Sequential(*model1)

        model2 = []
        # Residual blocks
        for _ in range(n_residual_blocks):
            model2 += [ResidualBlock(in_features)]
        self.model2 = nn.Sequential(*model2)

        # Upsampling
        model3 = []
        out_features = in_features//2
        for _ in range(2):
            model3 += [  nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
                        norm_layer(out_features),
                        nn.ReLU(inplace=True) ]
            in_features = out_features
            out_features = in_features//2
        self.model3 = nn.Sequential(*model3)

        # Output layer
        model4 = [  nn.ReflectionPad2d(3),
                        nn.Conv2d(64, output_nc, 7)]
        if sigmoid:
            model4 += [nn.Sigmoid()]

        self.model4 = nn.Sequential(*model4)

    def forward(self, x, cond=None):
        out = self.model0(x)
        out = self.model1(out)
        out = self.model2(out)
        out = self.model3(out)
        out = self.model4(out)

        return out

class LineartDetector:
    def __init__(self, model, coarse_model):
        self.model = model
        self.model_coarse = coarse_model
        self.device = "cpu"

    @classmethod
    def from_pretrained(cls):
        current_dir = os.path.dirname(os.path.abspath(__file__))
        model_path = os.path.join(current_dir, "../models/preprocessor/sk_model.pth")
        coarse_model_path = os.path.join(current_dir, "../models/preprocessor/sk_model2.pth")

        # print("model_path:", model_path)
        model = Generator(3, 1, 3)
        model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
        model.eval()

        coarse_model = Generator(3, 1, 3)
        coarse_model.load_state_dict(torch.load(coarse_model_path, map_location=torch.device('cpu')))
        coarse_model.eval()

        return cls(model, coarse_model)
    
    def to(self, device):
        self.model.to(device)
        self.model_coarse.to(device)
        self.device = device
        return self
    
    def __call__(self, input_image, coarse=False, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", **kwargs):
        input_image, output_type = common_input_validate(input_image, output_type, **kwargs)
        detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method)

        model = self.model_coarse if coarse else self.model
        assert detected_map.ndim == 3
        with torch.no_grad():
            image = torch.from_numpy(detected_map).float().to(self.device)
            image = image / 255.0
            image = rearrange(image, 'h w c -> 1 c h w')
            line = model(image)[0][0]

            line = line.cpu().numpy()
            line = (line * 255.0).clip(0, 255).astype(np.uint8)

        detected_map = HWC3(line)
        detected_map = remove_pad(255 - detected_map)
        
        if output_type == "pil":
            detected_map = Image.fromarray(detected_map)
            
        return detected_map

class LineArt_Preprocessor:
    def execute(self, image, resolution=512, **kwargs):
        model = LineartDetector.from_pretrained().to(comfy.model_management.get_torch_device())
        print("model.device:", model.device)
        out = common_annotator_call(model, image, resolution=resolution, apply_filter=False, coarse = kwargs["coarse"] == "enable")
        del model
        return (out, )
    
def nms(x, t, s):
    x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s)

    f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
    f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
    f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
    f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)

    y = np.zeros_like(x)

    for f in [f1, f2, f3, f4]:
        np.putmask(y, cv2.dilate(x, kernel=f) == x, x)

    z = np.zeros_like(y, dtype=np.uint8)
    z[y > t] = 255
    return z

class PidiNetDetector:
    def __init__(self, netNetwork):
        self.netNetwork = netNetwork
        self.device = "cpu"

    @classmethod
    def from_pretrained(cls, filename="table5_pidinet.pth"):
        current_dir = os.path.dirname(os.path.abspath(__file__))
        model_path = os.path.join(current_dir, f"../models/preprocessor/{filename}")

        netNetwork = pidinet()
        netNetwork.load_state_dict({k.replace('module.', ''): v for k, v in torch.load(model_path)['state_dict'].items()})
        netNetwork.eval()

        return cls(netNetwork)

    def to(self, device):
        self.netNetwork.to(device)
        self.device = device
        return self
    
    def __call__(self, input_image, detect_resolution=512, safe=False, output_type="pil", scribble=False, apply_filter=True, upscale_method="INTER_CUBIC", **kwargs):
        input_image, output_type = common_input_validate(input_image, output_type, **kwargs)
        detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method)
        
        detected_map = detected_map[:, :, ::-1].copy()
        with torch.no_grad():
            image_pidi = torch.from_numpy(detected_map).float().to(self.device)
            image_pidi = image_pidi / 255.0
            image_pidi = rearrange(image_pidi, 'h w c -> 1 c h w')
            edge = self.netNetwork(image_pidi)[-1]
            edge = edge.cpu().numpy()
            if apply_filter:
                edge = edge > 0.5 
            edge = (edge * 255.0).clip(0, 255).astype(np.uint8)

        detected_map = edge[0, 0]

        if scribble:
            detected_map = nms(detected_map, 127, 3.0)
            detected_map = cv2.GaussianBlur(detected_map, (0, 0), 3.0)
            detected_map[detected_map > 4] = 255
            detected_map[detected_map < 255] = 0

        detected_map = HWC3(remove_pad(detected_map))

        if output_type == "pil":
            detected_map = Image.fromarray(detected_map)

        return detected_map

class GrowMask:
    def expand_mask(self, mask, expand, tapered_corners):
        c = 0 if tapered_corners else 1
        kernel = np.array([[c, 1, c],
                           [1, 1, 1],
                           [c, 1, c]])
        mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
        out = []
        for m in mask:
            output = m.numpy()
            for _ in range(abs(expand)):
                if expand < 0:
                    output = scipy.ndimage.grey_erosion(output, footprint=kernel)
                else:
                    output = scipy.ndimage.grey_dilation(output, footprint=kernel)
            output = torch.from_numpy(output)
            out.append(output)
        return (torch.stack(out, dim=0),)
    
class PIDINET_Preprocessor:
    def execute(self, image, resolution=512, **kwargs):
        model = PidiNetDetector.from_pretrained().to(comfy.model_management.get_torch_device())
        out = common_annotator_call(model, image, resolution=resolution, safe=True)
        del model
        return (out, )