custom_nodes/ComfyUI-Easy-Use/py/modules/brushnet/__init__.py

#credit to nullquant for this module
#from https://github.com/nullquant/ComfyUI-BrushNet

import os
import types

import torch
try:
    from accelerate import init_empty_weights, load_checkpoint_and_dispatch
except:
    init_empty_weights, load_checkpoint_and_dispatch = None, None

import comfy

try:
    from .model import BrushNetModel, PowerPaintModel
    from .model_patch import add_model_patch_option, patch_model_function_wrapper
    from .powerpaint_utils import TokenizerWrapper, add_tokens
except:
    BrushNetModel, PowerPaintModel = None, None
    add_model_patch_option, patch_model_function_wrapper = None, None
    TokenizerWrapper, add_tokens = None, None

cwd_path = os.path.dirname(os.path.realpath(__file__))
brushnet_config_file = os.path.join(cwd_path, 'config', 'brushnet.json')
brushnet_xl_config_file = os.path.join(cwd_path, 'config', 'brushnet_xl.json')
powerpaint_config_file = os.path.join(cwd_path, 'config', 'powerpaint.json')

sd15_scaling_factor = 0.18215
sdxl_scaling_factor = 0.13025

ModelsToUnload = [comfy.sd1_clip.SD1ClipModel, comfy.ldm.models.autoencoder.AutoencoderKL]

class BrushNet:

    # Check models compatibility
    def check_compatibilty(self, model, brushnet):
        is_SDXL = False
        is_PP = False
        if isinstance(model.model.model_config, comfy.supported_models.SD15):
            print('Base model type: SD1.5')
            is_SDXL = False
            if brushnet["SDXL"]:
                raise Exception("Base model is SD15, but BrushNet is SDXL type")
            if brushnet["PP"]:
                is_PP = True
        elif isinstance(model.model.model_config, comfy.supported_models.SDXL):
            print('Base model type: SDXL')
            is_SDXL = True
            if not brushnet["SDXL"]:
                raise Exception("Base model is SDXL, but BrushNet is SD15 type")
        else:
            print('Base model type: ', type(model.model.model_config))
            raise Exception("Unsupported model type: " + str(type(model.model.model_config)))

        return (is_SDXL, is_PP)

    def check_image_mask(self, image, mask, name):
        if len(image.shape) < 4:
            # image tensor shape should be [B, H, W, C], but batch somehow is missing
            image = image[None, :, :, :]

        if len(mask.shape) > 3:
            # mask tensor shape should be [B, H, W] but we get [B, H, W, C], image may be?
            # take first mask, red channel
            mask = (mask[:, :, :, 0])[:, :, :]
        elif len(mask.shape) < 3:
            # mask tensor shape should be [B, H, W] but batch somehow is missing
            mask = mask[None, :, :]

        if image.shape[0] > mask.shape[0]:
            print(name, "gets batch of images (%d) but only %d masks" % (image.shape[0], mask.shape[0]))
            if mask.shape[0] == 1:
                print(name, "will copy the mask to fill batch")
                mask = torch.cat([mask] * image.shape[0], dim=0)
            else:
                print(name, "will add empty masks to fill batch")
                empty_mask = torch.zeros([image.shape[0] - mask.shape[0], mask.shape[1], mask.shape[2]])
                mask = torch.cat([mask, empty_mask], dim=0)
        elif image.shape[0] < mask.shape[0]:
            print(name, "gets batch of images (%d) but too many (%d) masks" % (image.shape[0], mask.shape[0]))
            mask = mask[:image.shape[0], :, :]

        return (image, mask)

    # Prepare image and mask
    def prepare_image(self, image, mask):

        image, mask = self.check_image_mask(image, mask, 'BrushNet')

        print("BrushNet image.shape =", image.shape, "mask.shape =", mask.shape)

        if mask.shape[2] != image.shape[2] or mask.shape[1] != image.shape[1]:
            raise Exception("Image and mask should be the same size")

        # As a suggestion of inferno46n2 (https://github.com/nullquant/ComfyUI-BrushNet/issues/64)
        mask = mask.round()

        masked_image = image * (1.0 - mask[:, :, :, None])

        return (masked_image, mask)

    # Get origin of the mask
    def cut_with_mask(self, mask, width, height):
        iy, ix = (mask == 1).nonzero(as_tuple=True)

        h0, w0 = mask.shape

        if iy.numel() == 0:
            x_c = w0 / 2.0
            y_c = h0 / 2.0
        else:
            x_min = ix.min().item()
            x_max = ix.max().item()
            y_min = iy.min().item()
            y_max = iy.max().item()

            if x_max - x_min > width or y_max - y_min > height:
                raise Exception("Mask is bigger than provided dimensions")

            x_c = (x_min + x_max) / 2.0
            y_c = (y_min + y_max) / 2.0

        width2 = width / 2.0
        height2 = height / 2.0

        if w0 <= width:
            x0 = 0
            w = w0
        else:
            x0 = max(0, x_c - width2)
            w = width
            if x0 + width > w0:
                x0 = w0 - width

        if h0 <= height:
            y0 = 0
            h = h0
        else:
            y0 = max(0, y_c - height2)
            h = height
            if y0 + height > h0:
                y0 = h0 - height

        return (int(x0), int(y0), int(w), int(h))

    # Prepare conditioning_latents
    @torch.inference_mode()
    def get_image_latents(self, masked_image, mask, vae, scaling_factor):
        processed_image = masked_image.to(vae.device)
        image_latents = vae.encode(processed_image[:, :, :, :3]) * scaling_factor
        processed_mask = 1. - mask[:, None, :, :]
        interpolated_mask = torch.nn.functional.interpolate(
            processed_mask,
            size=(
                image_latents.shape[-2],
                image_latents.shape[-1]
            )
        )
        interpolated_mask = interpolated_mask.to(image_latents.device)

        conditioning_latents = [image_latents, interpolated_mask]

        print('BrushNet CL: image_latents shape =', image_latents.shape, 'interpolated_mask shape =',
              interpolated_mask.shape)

        return conditioning_latents

    def brushnet_blocks(self, sd):
        brushnet_down_block = 0
        brushnet_mid_block = 0
        brushnet_up_block = 0
        for key in sd:
            if 'brushnet_down_block' in key:
                brushnet_down_block += 1
            if 'brushnet_mid_block' in key:
                brushnet_mid_block += 1
            if 'brushnet_up_block' in key:
                brushnet_up_block += 1
        return (brushnet_down_block, brushnet_mid_block, brushnet_up_block, len(sd))

    def get_model_type(self, brushnet_file):
        sd = comfy.utils.load_torch_file(brushnet_file)
        brushnet_down_block, brushnet_mid_block, brushnet_up_block, keys = self.brushnet_blocks(sd)
        del sd
        if brushnet_down_block == 24 and brushnet_mid_block == 2 and brushnet_up_block == 30:
            is_SDXL = False
            if keys == 322:
                is_PP = False
                print('BrushNet model type: SD1.5')
            else:
                is_PP = True
                print('PowerPaint model type: SD1.5')
        elif brushnet_down_block == 18 and brushnet_mid_block == 2 and brushnet_up_block == 22:
            print('BrushNet model type: Loading SDXL')
            is_SDXL = True
            is_PP = False
        else:
            raise Exception("Unknown BrushNet model")
        return is_SDXL, is_PP

    def load_brushnet_model(self, brushnet_file, dtype='float16'):
        is_SDXL, is_PP = self.get_model_type(brushnet_file)
        with init_empty_weights():
            if is_SDXL:
                brushnet_config = BrushNetModel.load_config(brushnet_xl_config_file)
                brushnet_model = BrushNetModel.from_config(brushnet_config)
            elif is_PP:
                brushnet_config = PowerPaintModel.load_config(powerpaint_config_file)
                brushnet_model = PowerPaintModel.from_config(brushnet_config)
            else:
                brushnet_config = BrushNetModel.load_config(brushnet_config_file)
                brushnet_model = BrushNetModel.from_config(brushnet_config)
        if is_PP:
            print("PowerPaint model file:", brushnet_file)
        else:
            print("BrushNet model file:", brushnet_file)

        if dtype == 'float16':
            torch_dtype = torch.float16
        elif dtype == 'bfloat16':
            torch_dtype = torch.bfloat16
        elif dtype == 'float32':
            torch_dtype = torch.float32
        else:
            torch_dtype = torch.float64

        brushnet_model = load_checkpoint_and_dispatch(
            brushnet_model,
            brushnet_file,
            device_map="sequential",
            max_memory=None,
            offload_folder=None,
            offload_state_dict=False,
            dtype=torch_dtype,
            force_hooks=False,
        )

        if is_PP:
            print("PowerPaint model is loaded")
        elif is_SDXL:
            print("BrushNet SDXL model is loaded")
        else:
            print("BrushNet SD1.5 model is loaded")

        return ({"brushnet": brushnet_model, "SDXL": is_SDXL, "PP": is_PP, "dtype": torch_dtype},)

    def brushnet_model_update(self, model, vae, image, mask, brushnet, positive, negative, scale, start_at, end_at):

        is_SDXL, is_PP = self.check_compatibilty(model, brushnet)

        if is_PP:
            raise Exception("PowerPaint model was loaded, please use PowerPaint node")

            # Make a copy of the model so that we're not patching it everywhere in the workflow.
        model = model.clone()

        # prepare image and mask
        # no batches for original image and mask
        masked_image, mask = self.prepare_image(image, mask)

        batch = masked_image.shape[0]
        width = masked_image.shape[2]
        height = masked_image.shape[1]

        if hasattr(model.model.model_config, 'latent_format') and hasattr(model.model.model_config.latent_format,
                                                                          'scale_factor'):
            scaling_factor = model.model.model_config.latent_format.scale_factor
        elif is_SDXL:
            scaling_factor = sdxl_scaling_factor
        else:
            scaling_factor = sd15_scaling_factor

        torch_dtype = brushnet['dtype']

        # prepare conditioning latents
        conditioning_latents = self.get_image_latents(masked_image, mask, vae, scaling_factor)
        conditioning_latents[0] = conditioning_latents[0].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
        conditioning_latents[1] = conditioning_latents[1].to(dtype=torch_dtype).to(brushnet['brushnet'].device)

        # unload vae
        del vae
        # for loaded_model in comfy.model_management.current_loaded_models:
        #     if type(loaded_model.model.model) in ModelsToUnload:
        #         comfy.model_management.current_loaded_models.remove(loaded_model)
        #         loaded_model.model_unload()
        #         del loaded_model

        # prepare embeddings
        prompt_embeds = positive[0][0].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
        negative_prompt_embeds = negative[0][0].to(dtype=torch_dtype).to(brushnet['brushnet'].device)

        max_tokens = max(prompt_embeds.shape[1], negative_prompt_embeds.shape[1])
        if prompt_embeds.shape[1] < max_tokens:
            multiplier = max_tokens // 77 - prompt_embeds.shape[1] // 77
            prompt_embeds = torch.concat([prompt_embeds] + [prompt_embeds[:, -77:, :]] * multiplier, dim=1)
            print('BrushNet: negative prompt more than 75 tokens:', negative_prompt_embeds.shape,
                  'multiplying prompt_embeds')
        if negative_prompt_embeds.shape[1] < max_tokens:
            multiplier = max_tokens // 77 - negative_prompt_embeds.shape[1] // 77
            negative_prompt_embeds = torch.concat(
                [negative_prompt_embeds] + [negative_prompt_embeds[:, -77:, :]] * multiplier, dim=1)
            print('BrushNet: positive prompt more than 75 tokens:', prompt_embeds.shape,
                  'multiplying negative_prompt_embeds')

        if len(positive[0]) > 1 and 'pooled_output' in positive[0][1] and positive[0][1]['pooled_output'] is not None:
            pooled_prompt_embeds = positive[0][1]['pooled_output'].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
        else:
            print('BrushNet: positive conditioning has not pooled_output')
            if is_SDXL:
                print('BrushNet will not produce correct results')
            pooled_prompt_embeds = torch.empty([2, 1280], device=brushnet['brushnet'].device).to(dtype=torch_dtype)

        if len(negative[0]) > 1 and 'pooled_output' in negative[0][1] and negative[0][1]['pooled_output'] is not None:
            negative_pooled_prompt_embeds = negative[0][1]['pooled_output'].to(dtype=torch_dtype).to(
                brushnet['brushnet'].device)
        else:
            print('BrushNet: negative conditioning has not pooled_output')
            if is_SDXL:
                print('BrushNet will not produce correct results')
            negative_pooled_prompt_embeds = torch.empty([1, pooled_prompt_embeds.shape[1]],
                                                        device=brushnet['brushnet'].device).to(dtype=torch_dtype)

        time_ids = torch.FloatTensor([[height, width, 0., 0., height, width]]).to(dtype=torch_dtype).to(
            brushnet['brushnet'].device)

        if not is_SDXL:
            pooled_prompt_embeds = None
            negative_pooled_prompt_embeds = None
            time_ids = None

        # apply patch to model
        brushnet_conditioning_scale = scale
        control_guidance_start = start_at
        control_guidance_end = end_at

        add_brushnet_patch(model,
                           brushnet['brushnet'],
                           torch_dtype,
                           conditioning_latents,
                           (brushnet_conditioning_scale, control_guidance_start, control_guidance_end),
                           prompt_embeds, negative_prompt_embeds,
                           pooled_prompt_embeds, negative_pooled_prompt_embeds, time_ids,
                           False)

        latent = torch.zeros([batch, 4, conditioning_latents[0].shape[2], conditioning_latents[0].shape[3]],
                             device=brushnet['brushnet'].device)

        return (model, positive, negative, {"samples": latent},)

    #powperpaint
    def load_powerpaint_clip(self, base_clip_file, pp_clip_file):
        pp_clip = comfy.sd.load_clip(ckpt_paths=[base_clip_file])

        print('PowerPaint base CLIP file: ', base_clip_file)

        pp_tokenizer = TokenizerWrapper(pp_clip.tokenizer.clip_l.tokenizer)
        pp_text_encoder = pp_clip.patcher.model.clip_l.transformer

        add_tokens(
            tokenizer=pp_tokenizer,
            text_encoder=pp_text_encoder,
            placeholder_tokens=["P_ctxt", "P_shape", "P_obj"],
            initialize_tokens=["a", "a", "a"],
            num_vectors_per_token=10,
        )

        pp_text_encoder.load_state_dict(comfy.utils.load_torch_file(pp_clip_file), strict=False)

        print('PowerPaint CLIP file: ', pp_clip_file)

        pp_clip.tokenizer.clip_l.tokenizer = pp_tokenizer
        pp_clip.patcher.model.clip_l.transformer = pp_text_encoder

        return (pp_clip,)

    def powerpaint_model_update(self, model, vae, image, mask, powerpaint, clip, positive, negative, fitting, function, scale, start_at, end_at, save_memory):
        is_SDXL, is_PP = self.check_compatibilty(model, powerpaint)
        if not is_PP:
            raise Exception("BrushNet model was loaded, please use BrushNet node")

            # Make a copy of the model so that we're not patching it everywhere in the workflow.
        model = model.clone()

        # prepare image and mask
        # no batches for original image and mask
        masked_image, mask = self.prepare_image(image, mask)

        batch = masked_image.shape[0]
        # width = masked_image.shape[2]
        # height = masked_image.shape[1]

        if hasattr(model.model.model_config, 'latent_format') and hasattr(model.model.model_config.latent_format,
                                                                          'scale_factor'):
            scaling_factor = model.model.model_config.latent_format.scale_factor
        else:
            scaling_factor = sd15_scaling_factor

        torch_dtype = powerpaint['dtype']

        # prepare conditioning latents
        conditioning_latents = self.get_image_latents(masked_image, mask, vae, scaling_factor)
        conditioning_latents[0] = conditioning_latents[0].to(dtype=torch_dtype).to(powerpaint['brushnet'].device)
        conditioning_latents[1] = conditioning_latents[1].to(dtype=torch_dtype).to(powerpaint['brushnet'].device)

        # prepare embeddings

        if function == "object removal":
            promptA = "P_ctxt"
            promptB = "P_ctxt"
            negative_promptA = "P_obj"
            negative_promptB = "P_obj"
            print('You should add to positive prompt: "empty scene blur"')
            # positive = positive + " empty scene blur"
        elif function == "context aware":
            promptA = "P_ctxt"
            promptB = "P_ctxt"
            negative_promptA = ""
            negative_promptB = ""
            # positive = positive + " empty scene"
            print('You should add to positive prompt: "empty scene"')
        elif function == "shape guided":
            promptA = "P_shape"
            promptB = "P_ctxt"
            negative_promptA = "P_shape"
            negative_promptB = "P_ctxt"
        elif function == "image outpainting":
            promptA = "P_ctxt"
            promptB = "P_ctxt"
            negative_promptA = "P_obj"
            negative_promptB = "P_obj"
            # positive = positive + " empty scene"
            print('You should add to positive prompt: "empty scene"')
        else:
            promptA = "P_obj"
            promptB = "P_obj"
            negative_promptA = "P_obj"
            negative_promptB = "P_obj"

        tokens = clip.tokenize(promptA)
        prompt_embedsA = clip.encode_from_tokens(tokens, return_pooled=False)

        tokens = clip.tokenize(negative_promptA)
        negative_prompt_embedsA = clip.encode_from_tokens(tokens, return_pooled=False)

        tokens = clip.tokenize(promptB)
        prompt_embedsB = clip.encode_from_tokens(tokens, return_pooled=False)

        tokens = clip.tokenize(negative_promptB)
        negative_prompt_embedsB = clip.encode_from_tokens(tokens, return_pooled=False)

        prompt_embeds_pp = (prompt_embedsA * fitting + (1.0 - fitting) * prompt_embedsB).to(dtype=torch_dtype).to(
            powerpaint['brushnet'].device)
        negative_prompt_embeds_pp = (negative_prompt_embedsA * fitting + (1.0 - fitting) * negative_prompt_embedsB).to(
            dtype=torch_dtype).to(powerpaint['brushnet'].device)

        # unload vae and CLIPs
        del vae
        del clip
        # for loaded_model in comfy.model_management.current_loaded_models:
        #     if type(loaded_model.model.model) in ModelsToUnload:
        #         comfy.model_management.current_loaded_models.remove(loaded_model)
        #         loaded_model.model_unload()
        #         del loaded_model

        # apply patch to model

        brushnet_conditioning_scale = scale
        control_guidance_start = start_at
        control_guidance_end = end_at

        if save_memory != 'none':
            powerpaint['brushnet'].set_attention_slice(save_memory)

        add_brushnet_patch(model,
                           powerpaint['brushnet'],
                           torch_dtype,
                           conditioning_latents,
                           (brushnet_conditioning_scale, control_guidance_start, control_guidance_end),
                           negative_prompt_embeds_pp, prompt_embeds_pp,
                           None, None, None,
                           False)

        latent = torch.zeros([batch, 4, conditioning_latents[0].shape[2], conditioning_latents[0].shape[3]],
                             device=powerpaint['brushnet'].device)

        return (model, positive, negative, {"samples": latent},)
@torch.inference_mode()
def brushnet_inference(x, timesteps, transformer_options, debug):
    if 'model_patch' not in transformer_options:
        print('BrushNet inference: there is no model_patch key in transformer_options')
        return ([], 0, [])
    mp = transformer_options['model_patch']
    if 'brushnet' not in mp:
        print('BrushNet inference: there is no brushnet key in mdel_patch')
        return ([], 0, [])
    bo = mp['brushnet']
    if 'model' not in bo:
        print('BrushNet inference: there is no model key in brushnet')
        return ([], 0, [])
    brushnet = bo['model']
    if not (isinstance(brushnet, BrushNetModel) or isinstance(brushnet, PowerPaintModel)):
        print('BrushNet model is not a BrushNetModel class')
        return ([], 0, [])

    torch_dtype = bo['dtype']
    cl_list = bo['latents']
    brushnet_conditioning_scale, control_guidance_start, control_guidance_end = bo['controls']
    pe = bo['prompt_embeds']
    npe = bo['negative_prompt_embeds']
    ppe, nppe, time_ids = bo['add_embeds']

    #do_classifier_free_guidance = mp['free_guidance']
    do_classifier_free_guidance = len(transformer_options['cond_or_uncond']) > 1

    x = x.detach().clone()
    x = x.to(torch_dtype).to(brushnet.device)

    timesteps = timesteps.detach().clone()
    timesteps = timesteps.to(torch_dtype).to(brushnet.device)

    total_steps = mp['total_steps']
    step = mp['step']

    added_cond_kwargs = {}

    if do_classifier_free_guidance and step == 0:
        print('BrushNet inference: do_classifier_free_guidance is True')

    sub_idx = None
    if 'ad_params' in transformer_options and 'sub_idxs' in transformer_options['ad_params']:
        sub_idx = transformer_options['ad_params']['sub_idxs']

    # we have batch input images
    batch = cl_list[0].shape[0]
    # we have incoming latents
    latents_incoming = x.shape[0]
    # and we already got some
    latents_got = bo['latent_id']
    if step == 0 or batch > 1:
        print('BrushNet inference, step = %d: image batch = %d, got %d latents, starting from %d' \
                % (step, batch, latents_incoming, latents_got))

    image_latents = []
    masks = []
    prompt_embeds = []
    negative_prompt_embeds = []
    pooled_prompt_embeds = []
    negative_pooled_prompt_embeds = []
    if sub_idx:
        # AnimateDiff indexes detected
        if step == 0:
            print('BrushNet inference: AnimateDiff indexes detected and applied')

        batch = len(sub_idx)

        if do_classifier_free_guidance:
            for i in sub_idx:
                image_latents.append(cl_list[0][i][None,:,:,:])
                masks.append(cl_list[1][i][None,:,:,:])
                prompt_embeds.append(pe)
                negative_prompt_embeds.append(npe)
                pooled_prompt_embeds.append(ppe)
                negative_pooled_prompt_embeds.append(nppe)
            for i in sub_idx:
                image_latents.append(cl_list[0][i][None,:,:,:])
                masks.append(cl_list[1][i][None,:,:,:])
        else:
            for i in sub_idx:
                image_latents.append(cl_list[0][i][None,:,:,:])
                masks.append(cl_list[1][i][None,:,:,:])
                prompt_embeds.append(pe)
                pooled_prompt_embeds.append(ppe)
    else:
        # do_classifier_free_guidance = 2 passes, 1st pass is cond, 2nd is uncond
        continue_batch = True
        for i in range(latents_incoming):
            number = latents_got + i
            if number < batch:
                # 1st pass, cond
                image_latents.append(cl_list[0][number][None,:,:,:])
                masks.append(cl_list[1][number][None,:,:,:])
                prompt_embeds.append(pe)
                pooled_prompt_embeds.append(ppe)
            elif do_classifier_free_guidance and number < batch * 2:
                # 2nd pass, uncond
                image_latents.append(cl_list[0][number-batch][None,:,:,:])
                masks.append(cl_list[1][number-batch][None,:,:,:])
                negative_prompt_embeds.append(npe)
                negative_pooled_prompt_embeds.append(nppe)
            else:
                # latent batch
                image_latents.append(cl_list[0][0][None,:,:,:])
                masks.append(cl_list[1][0][None,:,:,:])
                prompt_embeds.append(pe)
                pooled_prompt_embeds.append(ppe)
                latents_got = -i
                continue_batch = False

        if continue_batch:
            # we don't have full batch yet
            if do_classifier_free_guidance:
                if number < batch * 2 - 1:
                    bo['latent_id'] = number + 1
                else:
                    bo['latent_id'] = 0
            else:
                if number < batch - 1:
                    bo['latent_id'] = number + 1
                else:
                    bo['latent_id'] = 0
        else:
            bo['latent_id'] = 0

    cl = []
    for il, m in zip(image_latents, masks):
        cl.append(torch.concat([il, m], dim=1))
    cl2apply = torch.concat(cl, dim=0)

    conditioning_latents = cl2apply.to(torch_dtype).to(brushnet.device)

    prompt_embeds.extend(negative_prompt_embeds)
    prompt_embeds = torch.concat(prompt_embeds, dim=0).to(torch_dtype).to(brushnet.device)

    if ppe is not None:
        added_cond_kwargs = {}
        added_cond_kwargs['time_ids'] = torch.concat([time_ids] * latents_incoming, dim = 0).to(torch_dtype).to(brushnet.device)

        pooled_prompt_embeds.extend(negative_pooled_prompt_embeds)
        pooled_prompt_embeds = torch.concat(pooled_prompt_embeds, dim=0).to(torch_dtype).to(brushnet.device)
        added_cond_kwargs['text_embeds'] = pooled_prompt_embeds
    else:
        added_cond_kwargs = None

    if x.shape[2] != conditioning_latents.shape[2] or x.shape[3] != conditioning_latents.shape[3]:
        if step == 0:
            print('BrushNet inference: image', conditioning_latents.shape, 'and latent', x.shape, 'have different size, resizing image')
        conditioning_latents = torch.nn.functional.interpolate(
            conditioning_latents, size=(
                x.shape[2],
                x.shape[3],
            ), mode='bicubic',
        ).to(torch_dtype).to(brushnet.device)

    if step == 0:
        print('BrushNet inference: sample', x.shape, ', CL', conditioning_latents.shape, 'dtype', torch_dtype)

    if debug: print('BrushNet: step =', step)

    if step < control_guidance_start or step > control_guidance_end:
        cond_scale = 0.0
    else:
        cond_scale = brushnet_conditioning_scale

    return brushnet(x,
        encoder_hidden_states=prompt_embeds,
        brushnet_cond=conditioning_latents,
        timestep = timesteps,
        conditioning_scale=cond_scale,
        guess_mode=False,
        added_cond_kwargs=added_cond_kwargs,
        return_dict=False,
        debug=debug,
    )

def add_brushnet_patch(model, brushnet, torch_dtype, conditioning_latents,
                       controls,
                       prompt_embeds, negative_prompt_embeds,
                       pooled_prompt_embeds, negative_pooled_prompt_embeds, time_ids,
                       debug):

    is_SDXL = isinstance(model.model.model_config, comfy.supported_models.SDXL)

    if model.model.model_config.custom_operations is None:
        fp8 = model.model.model_config.optimizations.get("fp8", model.model.model_config.scaled_fp8 is not None)
        operations = comfy.ops.pick_operations(model.model.model_config.unet_config.get("dtype", None), model.model.manual_cast_dtype,
                                               fp8_optimizations=fp8, scaled_fp8=model.model.model_config.scaled_fp8)
    else:
        # such as gguf
        operations = model.model.model_config.custom_operations

    if is_SDXL:
        input_blocks = [[0, operations.Conv2d],
                        [1, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
                        [2, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
                        [3, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample],
                        [4, comfy.ldm.modules.attention.SpatialTransformer],
                        [5, comfy.ldm.modules.attention.SpatialTransformer],
                        [6, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample],
                        [7, comfy.ldm.modules.attention.SpatialTransformer],
                        [8, comfy.ldm.modules.attention.SpatialTransformer]]
        middle_block = [0, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock]
        output_blocks = [[0, comfy.ldm.modules.attention.SpatialTransformer],
                         [1, comfy.ldm.modules.attention.SpatialTransformer],
                         [2, comfy.ldm.modules.attention.SpatialTransformer],
                         [2, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample],
                         [3, comfy.ldm.modules.attention.SpatialTransformer],
                         [4, comfy.ldm.modules.attention.SpatialTransformer],
                         [5, comfy.ldm.modules.attention.SpatialTransformer],
                         [5, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample],
                         [6, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
                         [7, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
                         [8, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock]]
    else:
        input_blocks = [[0, operations.Conv2d],
                        [1, comfy.ldm.modules.attention.SpatialTransformer],
                        [2, comfy.ldm.modules.attention.SpatialTransformer],
                        [3, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample],
                        [4, comfy.ldm.modules.attention.SpatialTransformer],
                        [5, comfy.ldm.modules.attention.SpatialTransformer],
                        [6, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample],
                        [7, comfy.ldm.modules.attention.SpatialTransformer],
                        [8, comfy.ldm.modules.attention.SpatialTransformer],
                        [9, comfy.ldm.modules.diffusionmodules.openaimodel.Downsample],
                        [10, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
                        [11, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock]]
        middle_block = [0, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock]
        output_blocks = [[0, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
                         [1, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
                         [2, comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock],
                         [2, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample],
                         [3, comfy.ldm.modules.attention.SpatialTransformer],
                         [4, comfy.ldm.modules.attention.SpatialTransformer],
                         [5, comfy.ldm.modules.attention.SpatialTransformer],
                         [5, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample],
                         [6, comfy.ldm.modules.attention.SpatialTransformer],
                         [7, comfy.ldm.modules.attention.SpatialTransformer],
                         [8, comfy.ldm.modules.attention.SpatialTransformer],
                         [8, comfy.ldm.modules.diffusionmodules.openaimodel.Upsample],
                         [9, comfy.ldm.modules.attention.SpatialTransformer],
                         [10, comfy.ldm.modules.attention.SpatialTransformer],
                         [11, comfy.ldm.modules.attention.SpatialTransformer]]

    def last_layer_index(block, tp):
        layer_list = []
        for layer in block:
            layer_list.append(type(layer))
        layer_list.reverse()
        if tp not in layer_list:
            return -1, layer_list.reverse()
        return len(layer_list) - 1 - layer_list.index(tp), layer_list

    def brushnet_forward(model, x, timesteps, transformer_options, control):
        if 'brushnet' not in transformer_options['model_patch']:
            input_samples = []
            mid_sample = 0
            output_samples = []
        else:
            # brushnet inference
            input_samples, mid_sample, output_samples = brushnet_inference(x, timesteps, transformer_options, debug)

        # give additional samples to blocks
        for i, tp in input_blocks:
            idx, layer_list = last_layer_index(model.input_blocks[i], tp)
            if idx < 0:
                print("BrushNet can't find", tp, "layer in", i, "input block:", layer_list)
                continue
            model.input_blocks[i][idx].add_sample_after = input_samples.pop(0) if input_samples else 0

        idx, layer_list = last_layer_index(model.middle_block, middle_block[1])
        if idx < 0:
            print("BrushNet can't find", middle_block[1], "layer in middle block", layer_list)
        model.middle_block[idx].add_sample_after = mid_sample

        for i, tp in output_blocks:
            idx, layer_list = last_layer_index(model.output_blocks[i], tp)
            if idx < 0:
                print("BrushNet can't find", tp, "layer in", i, "outnput block:", layer_list)
                continue
            model.output_blocks[i][idx].add_sample_after = output_samples.pop(0) if output_samples else 0

    patch_model_function_wrapper(model, brushnet_forward)

    to = add_model_patch_option(model)
    mp = to['model_patch']
    if 'brushnet' not in mp:
        mp['brushnet'] = {}
    bo = mp['brushnet']

    bo['model'] = brushnet
    bo['dtype'] = torch_dtype
    bo['latents'] = conditioning_latents
    bo['controls'] = controls
    bo['prompt_embeds'] = prompt_embeds
    bo['negative_prompt_embeds'] = negative_prompt_embeds
    bo['add_embeds'] = (pooled_prompt_embeds, negative_pooled_prompt_embeds, time_ids)
    bo['latent_id'] = 0

    # patch layers `forward` so we can apply brushnet
    def forward_patched_by_brushnet(self, x, *args, **kwargs):
        h = self.original_forward(x, *args, **kwargs)
        if hasattr(self, 'add_sample_after') and type(self):
            to_add = self.add_sample_after
            if torch.is_tensor(to_add):
                # interpolate due to RAUNet
                if h.shape[2] != to_add.shape[2] or h.shape[3] != to_add.shape[3]:
                    to_add = torch.nn.functional.interpolate(to_add, size=(h.shape[2], h.shape[3]), mode='bicubic')
                h += to_add.to(h.dtype).to(h.device)
            else:
                h += self.add_sample_after
            self.add_sample_after = 0
        return h

    for i, block in enumerate(model.model.diffusion_model.input_blocks):
        for j, layer in enumerate(block):
            if not hasattr(layer, 'original_forward'):
                layer.original_forward = layer.forward
            layer.forward = types.MethodType(forward_patched_by_brushnet, layer)
            layer.add_sample_after = 0

    for j, layer in enumerate(model.model.diffusion_model.middle_block):
        if not hasattr(layer, 'original_forward'):
            layer.original_forward = layer.forward
        layer.forward = types.MethodType(forward_patched_by_brushnet, layer)
        layer.add_sample_after = 0

    for i, block in enumerate(model.model.diffusion_model.output_blocks):
        for j, layer in enumerate(block):
            if not hasattr(layer, 'original_forward'):
                layer.original_forward = layer.forward
            layer.forward = types.MethodType(forward_patched_by_brushnet, layer)
            layer.add_sample_after = 0