885 lines
36 KiB
Python
885 lines
36 KiB
Python
import torch
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import torchvision.transforms as T
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import math
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import os
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import folder_paths
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import sys
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from sys import platform
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# Get the parent directory of 'comfy' and add it to the Python path
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comfy_parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../'))
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sys.path.append(comfy_parent_dir)
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import comfy
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from accelerate import init_empty_weights, load_checkpoint_and_dispatch
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from .brushnet.brushnet import BrushNetModel
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from .brushnet.brushnet_ca import BrushNetModel as PowerPaintModel
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from .brushnet.powerpaint_utils import TokenizerWrapper, add_tokens
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import types
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from typing import Tuple
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import warnings
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warnings.filterwarnings("ignore", category=UserWarning, module="torch")
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warnings.filterwarnings("ignore", category=UserWarning, module="safetensors")
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current_directory = os.path.dirname(os.path.abspath(__file__))
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brushnet_config_file = os.path.join(current_directory, 'brushnet', 'brushnet.json')
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brushnet_xl_config_file = os.path.join(current_directory, 'brushnet', 'brushnet_xl.json')
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powerpaint_config_file = os.path.join(current_directory,'brushnet', 'powerpaint.json')
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sd15_scaling_factor = 0.18215
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sdxl_scaling_factor = 0.13025
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ComfySDLayers = [comfy.ops.disable_weight_init.Conv2d,
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comfy.ldm.modules.attention.SpatialTransformer,
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comfy.ldm.modules.diffusionmodules.openaimodel.Downsample,
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comfy.ldm.modules.diffusionmodules.openaimodel.ResBlock
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]
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class BrushNetLoader:
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@classmethod
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def INPUT_TYPES(s):
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files, inpaint_path = get_files_with_extension('inpaint')
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s.inpaint_path = inpaint_path
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return {"required":
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{
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"brushnet": (files, ),
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"dtype": (['float16', 'bfloat16', 'float32', 'float64'], ),
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},
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}
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CATEGORY = "inpaint"
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RETURN_TYPES = ("BRMODEL",)
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RETURN_NAMES = ("brushnet",)
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FUNCTION = "brushnet_loading"
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def brushnet_loading(self, brushnet, dtype):
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brushnet_file = os.path.join(self.inpaint_path, brushnet)
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is_SDXL = False
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is_PP = False
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sd = comfy.utils.load_torch_file(brushnet_file)
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brushnet_down_block, brushnet_mid_block, brushnet_up_block, keys = brushnet_blocks(sd)
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del sd
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if brushnet_down_block == 24 and brushnet_mid_block == 2 and brushnet_up_block == 30:
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is_SDXL = False
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if keys == 322:
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is_PP = False
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print('BrushNet model type: SD1.5')
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else:
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is_PP = True
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print('PowerPaint model type: SD1.5')
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elif brushnet_down_block == 18 and brushnet_mid_block == 2 and brushnet_up_block == 22:
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print('BrushNet model type: Loading SDXL')
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is_SDXL = True
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is_PP = False
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else:
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raise Exception("Unknown BrushNet model")
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with init_empty_weights():
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if is_SDXL:
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brushnet_config = BrushNetModel.load_config(brushnet_xl_config_file)
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brushnet_model = BrushNetModel.from_config(brushnet_config)
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elif is_PP:
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brushnet_config = PowerPaintModel.load_config(powerpaint_config_file)
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brushnet_model = PowerPaintModel.from_config(brushnet_config)
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else:
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brushnet_config = BrushNetModel.load_config(brushnet_config_file)
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brushnet_model = BrushNetModel.from_config(brushnet_config)
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if is_PP:
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print("PowerPaint model file:", brushnet_file)
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else:
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print("BrushNet model file:", brushnet_file)
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if dtype == 'float16':
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torch_dtype = torch.float16
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elif dtype == 'bfloat16':
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torch_dtype = torch.bfloat16
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elif dtype == 'float32':
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torch_dtype = torch.float32
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else:
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torch_dtype = torch.float64
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brushnet_model = load_checkpoint_and_dispatch(
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brushnet_model,
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brushnet_file,
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device_map="sequential",
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max_memory=None,
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offload_folder=None,
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offload_state_dict=False,
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dtype=torch_dtype,
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force_hooks=False,
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)
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if is_PP:
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print("PowerPaint model is loaded")
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elif is_SDXL:
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print("BrushNet SDXL model is loaded")
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else:
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print("BrushNet SD1.5 model is loaded")
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return ({"brushnet": brushnet_model, "SDXL": is_SDXL, "PP": is_PP, "dtype": torch_dtype}, )
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class PowerPaintCLIPLoader:
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@classmethod
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def INPUT_TYPES(s):
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inpaint_files, inpaint_path = get_files_with_extension('inpaint', ['bin'])
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s.inpaint_path = inpaint_path
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clip_files, clip_path = get_files_with_extension('clip')
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s.clip_path = clip_path
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return {"required":
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{
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"base": (clip_files, ),
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"powerpaint": (inpaint_files, ),
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},
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}
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CATEGORY = "inpaint"
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RETURN_TYPES = ("CLIP",)
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RETURN_NAMES = ("clip",)
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FUNCTION = "ppclip_loading"
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def ppclip_loading(self, base, powerpaint):
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base_CLIP_file = os.path.join(self.clip_path, base)
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pp_CLIP_file = os.path.join(self.inpaint_path, powerpaint)
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pp_clip = comfy.sd.load_clip(ckpt_paths=[base_CLIP_file])
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print('PowerPaint base CLIP file: ', base_CLIP_file)
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pp_tokenizer = TokenizerWrapper(pp_clip.tokenizer.clip_l.tokenizer)
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pp_text_encoder = pp_clip.patcher.model.clip_l.transformer
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add_tokens(
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tokenizer = pp_tokenizer,
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text_encoder = pp_text_encoder,
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placeholder_tokens = ["P_ctxt", "P_shape", "P_obj"],
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initialize_tokens = ["a", "a", "a"],
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num_vectors_per_token = 10,
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)
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pp_text_encoder.load_state_dict(torch.load(pp_CLIP_file), strict=False)
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print('PowerPaint CLIP file: ', pp_CLIP_file)
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pp_clip.tokenizer.clip_l.tokenizer = pp_tokenizer
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pp_clip.patcher.model.clip_l.transformer = pp_text_encoder
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return (pp_clip,)
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class PowerPaint:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{
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"model": ("MODEL",),
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"vae": ("VAE", ),
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"image": ("IMAGE",),
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"mask": ("MASK",),
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"powerpaint": ("BRMODEL", ),
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"clip": ("CLIP", ),
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"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"fitting" : ("FLOAT", {"default": 1.0, "min": 0.3, "max": 1.0}),
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"function": (['text guided', 'shape guided', 'object removal', 'context aware', 'image outpainting'], ),
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"scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}),
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"start_at": ("INT", {"default": 0, "min": 0, "max": 10000}),
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"end_at": ("INT", {"default": 10000, "min": 0, "max": 10000}),
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},
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}
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CATEGORY = "inpaint"
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RETURN_TYPES = ("MODEL","CONDITIONING","CONDITIONING","LATENT",)
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RETURN_NAMES = ("model","positive","negative","latent",)
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FUNCTION = "model_update"
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def model_update(self, model, vae, image, mask, powerpaint, clip, positive, negative, fitting, function, scale, start_at, end_at):
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is_SDXL, is_PP = check_compatibilty(model, powerpaint)
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if not is_PP:
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raise Exception("BrushNet model was loaded, please use BrushNet node")
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# Make a copy of the model so that we're not patching it everywhere in the workflow.
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model = model.clone()
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# prepare image and mask
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# no batches for original image and mask
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masked_image, mask = prepare_image(image, mask)
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batch = masked_image.shape[0]
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#width = masked_image.shape[2]
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#height = masked_image.shape[1]
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if hasattr(model.model.model_config, 'latent_format') and hasattr(model.model.model_config.latent_format, 'scale_factor'):
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scaling_factor = model.model.model_config.latent_format.scale_factor
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else:
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scaling_factor = sd15_scaling_factor
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torch_dtype = powerpaint['dtype']
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# prepare conditioning latents
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conditioning_latents = get_image_latents(masked_image, mask, vae, scaling_factor)
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conditioning_latents[0] = conditioning_latents[0].to(dtype=torch_dtype).to(powerpaint['brushnet'].device)
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conditioning_latents[1] = conditioning_latents[1].to(dtype=torch_dtype).to(powerpaint['brushnet'].device)
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# prepare embeddings
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if function == "object removal":
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promptA = "P_ctxt"
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promptB = "P_ctxt"
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negative_promptA = "P_obj"
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negative_promptB = "P_obj"
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print('You should add to positive prompt: "empty scene blur"')
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#positive = positive + " empty scene blur"
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elif function == "context aware":
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promptA = "P_ctxt"
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promptB = "P_ctxt"
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negative_promptA = ""
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negative_promptB = ""
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#positive = positive + " empty scene"
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print('You should add to positive prompt: "empty scene"')
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elif function == "shape guided":
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promptA = "P_shape"
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promptB = "P_ctxt"
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negative_promptA = "P_shape"
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negative_promptB = "P_ctxt"
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elif function == "image outpainting":
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promptA = "P_ctxt"
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promptB = "P_ctxt"
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negative_promptA = "P_obj"
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negative_promptB = "P_obj"
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#positive = positive + " empty scene"
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print('You should add to positive prompt: "empty scene"')
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else:
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promptA = "P_obj"
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promptB = "P_obj"
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negative_promptA = "P_obj"
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negative_promptB = "P_obj"
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tokens = clip.tokenize(promptA)
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prompt_embedsA = clip.encode_from_tokens(tokens, return_pooled=False)
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tokens = clip.tokenize(negative_promptA)
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negative_prompt_embedsA = clip.encode_from_tokens(tokens, return_pooled=False)
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tokens = clip.tokenize(promptB)
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prompt_embedsB = clip.encode_from_tokens(tokens, return_pooled=False)
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tokens = clip.tokenize(negative_promptB)
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negative_prompt_embedsB = clip.encode_from_tokens(tokens, return_pooled=False)
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prompt_embeds_pp = (prompt_embedsA * fitting + (1.0 - fitting) * prompt_embedsB).to(dtype=torch_dtype).to(powerpaint['brushnet'].device)
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negative_prompt_embeds_pp = (negative_prompt_embedsA * fitting + (1.0 - fitting) * negative_prompt_embedsB).to(dtype=torch_dtype).to(powerpaint['brushnet'].device)
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# apply patch to code
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if comfy.samplers.sample.__doc__ is None or 'BrushNet' not in comfy.samplers.sample.__doc__:
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comfy.samplers.original_sample = comfy.samplers.sample
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comfy.samplers.sample = modified_sample
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# apply patch to model
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brushnet_conditioning_scale = scale
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control_guidance_start = start_at
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control_guidance_end = end_at
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add_brushnet_patch(model,
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powerpaint['brushnet'],
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torch_dtype,
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conditioning_latents,
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(brushnet_conditioning_scale, control_guidance_start, control_guidance_end),
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negative_prompt_embeds_pp, prompt_embeds_pp,
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None, None, None)
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latent = torch.zeros([batch, 4, conditioning_latents[0].shape[2], conditioning_latents[0].shape[3]], device=powerpaint['brushnet'].device)
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return (model, positive, negative, {"samples":latent},)
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class BrushNet:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{
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"model": ("MODEL",),
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"vae": ("VAE", ),
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"image": ("IMAGE",),
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"mask": ("MASK",),
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"brushnet": ("BRMODEL", ),
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"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}),
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"start_at": ("INT", {"default": 0, "min": 0, "max": 10000}),
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"end_at": ("INT", {"default": 10000, "min": 0, "max": 10000}),
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},
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}
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CATEGORY = "inpaint"
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RETURN_TYPES = ("MODEL","CONDITIONING","CONDITIONING","LATENT",)
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RETURN_NAMES = ("model","positive","negative","latent",)
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FUNCTION = "model_update"
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def model_update(self, model, vae, image, mask, brushnet, positive, negative, scale, start_at, end_at):
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is_SDXL, is_PP = check_compatibilty(model, brushnet)
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if is_PP:
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raise Exception("PowerPaint model was loaded, please use PowerPaint node")
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# Make a copy of the model so that we're not patching it everywhere in the workflow.
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model = model.clone()
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# prepare image and mask
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# no batches for original image and mask
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masked_image, mask = prepare_image(image, mask)
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batch = masked_image.shape[0]
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width = masked_image.shape[2]
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height = masked_image.shape[1]
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if hasattr(model.model.model_config, 'latent_format') and hasattr(model.model.model_config.latent_format, 'scale_factor'):
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scaling_factor = model.model.model_config.latent_format.scale_factor
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elif is_SDXL:
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scaling_factor = sdxl_scaling_factor
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else:
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scaling_factor = sd15_scaling_factor
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torch_dtype = brushnet['dtype']
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# prepare conditioning latents
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conditioning_latents = get_image_latents(masked_image, mask, vae, scaling_factor)
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conditioning_latents[0] = conditioning_latents[0].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
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conditioning_latents[1] = conditioning_latents[1].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
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# prepare embeddings
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prompt_embeds = positive[0][0].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
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negative_prompt_embeds = negative[0][0].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
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if len(positive[0]) > 1 and 'pooled_output' in positive[0][1] and positive[0][1]['pooled_output'] is not None:
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pooled_prompt_embeds = positive[0][1]['pooled_output'].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
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else:
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print('BrushNet: positive conditioning has not pooled_output')
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if is_SDXL:
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print('BrushNet will not produce correct results')
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pooled_prompt_embeds = torch.empty([2, 1280], device=brushnet['brushnet'].device).to(dtype=torch_dtype)
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if len(negative[0]) > 1 and 'pooled_output' in negative[0][1] and negative[0][1]['pooled_output'] is not None:
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negative_pooled_prompt_embeds = negative[0][1]['pooled_output'].to(dtype=torch_dtype).to(brushnet['brushnet'].device)
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else:
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print('BrushNet: negative conditioning has not pooled_output')
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if is_SDXL:
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print('BrushNet will not produce correct results')
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negative_pooled_prompt_embeds = torch.empty([1, pooled_prompt_embeds.shape[1]], device=brushnet['brushnet'].device).to(dtype=torch_dtype)
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time_ids = torch.FloatTensor([[height, width, 0., 0., height, width]]).to(dtype=torch_dtype).to(brushnet['brushnet'].device)
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if not is_SDXL:
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pooled_prompt_embeds = None
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negative_pooled_prompt_embeds = None
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time_ids = None
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# apply patch to code
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if comfy.samplers.sample.__doc__ is None or 'BrushNet' not in comfy.samplers.sample.__doc__:
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comfy.samplers.original_sample = comfy.samplers.sample
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comfy.samplers.sample = modified_sample
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# apply patch to model
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brushnet_conditioning_scale = scale
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control_guidance_start = start_at
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control_guidance_end = end_at
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add_brushnet_patch(model,
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brushnet['brushnet'],
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torch_dtype,
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conditioning_latents,
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(brushnet_conditioning_scale, control_guidance_start, control_guidance_end),
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prompt_embeds, negative_prompt_embeds,
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pooled_prompt_embeds, negative_pooled_prompt_embeds, time_ids)
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latent = torch.zeros([batch, 4, conditioning_latents[0].shape[2], conditioning_latents[0].shape[3]], device=brushnet['brushnet'].device)
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return (model, positive, negative, {"samples":latent},)
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class BlendInpaint:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{
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"inpaint": ("IMAGE",),
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"original": ("IMAGE",),
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"mask": ("MASK",),
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"kernel": ("INT", {"default": 10, "min": 1, "max": 1000}),
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"sigma": ("FLOAT", {"default": 10.0, "min": 0.01, "max": 1000}),
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},
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}
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CATEGORY = "inpaint"
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RETURN_TYPES = ("IMAGE","MASK",)
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RETURN_NAMES = ("image","MASK",)
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FUNCTION = "blend_inpaint"
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def blend_inpaint(self, inpaint: torch.Tensor, original: torch.Tensor, mask: torch.Tensor, kernel: int, sigma:int) -> Tuple[torch.Tensor]:
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# no batches over mask and original image
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if len(mask.shape) > 2:
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mask = mask[0]
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if len(original.shape) > 3:
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original = original[0]
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if kernel % 2 == 0:
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kernel += 1
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transform = T.GaussianBlur(kernel_size=(kernel, kernel), sigma=(sigma, sigma))
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blurred_mask = transform(mask[None,None,:,:]).to(original.device).to(original.dtype)
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inpaint = torch.nn.functional.interpolate(
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inpaint.permute(0, 3, 1, 2),
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size=(
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original.shape[0],
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original.shape[1],
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)
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).to(original.device).to(original.dtype)
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ret = []
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for result in inpaint.permute(0, 2, 3, 1):
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ret.append(original * (1.0 - blurred_mask[0][0][:,:,None]) + result.to(original.device) * blurred_mask[0][0][:,:,None])
|
|
|
|
return (torch.stack(ret), blurred_mask[0],)
|
|
|
|
|
|
#### Utility function
|
|
|
|
def get_files_with_extension(folder_name, extension=['safetensors']):
|
|
|
|
try:
|
|
inpaint_path = folder_paths.get_folder_paths(folder_name)[0]
|
|
except:
|
|
inpaint_path = os.path.join(folder_paths.models_dir, folder_name)
|
|
|
|
if not os.path.isdir(inpaint_path):
|
|
inpaint_path = os.path.join(folder_paths.base_dir, inpaint_path)
|
|
if not os.path.isdir(inpaint_path):
|
|
raise Exception("Can't find", folder_name, " path")
|
|
|
|
abs_list = []
|
|
for x in os.walk(inpaint_path):
|
|
for name in x[2]:
|
|
for ext in extension:
|
|
if ext in name:
|
|
abs_list.append(os.path.join(x[0], name))
|
|
names = []
|
|
for x in abs_list:
|
|
remain = x
|
|
y = ''
|
|
while remain != inpaint_path:
|
|
remain, folder = os.path.split(remain)
|
|
if len(y) > 0:
|
|
y = os.path.join(folder, y)
|
|
else:
|
|
y = folder
|
|
names.append(y)
|
|
return names, inpaint_path
|
|
|
|
|
|
def brushnet_blocks(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))
|
|
|
|
|
|
# Check models compatibility
|
|
def check_compatibilty(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)
|
|
|
|
# Prepare image and mask
|
|
def prepare_image(image, mask):
|
|
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("BrushNet gets batch of images (%d) but only %d masks" % (image.shape[0], mask.shape[0]))
|
|
if mask.shape[0] == 1:
|
|
print("BrushNet will copy the mask to fill batch")
|
|
mask = torch.cat([mask] * image.shape[0], dim=0)
|
|
else:
|
|
print("BrushNet 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("BrushNet gets batch of images (%d) but too many (%d) masks" % (image.shape[0], mask.shape[0]))
|
|
mask = mask[:image.shape[0],:,:]
|
|
|
|
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)
|
|
|
|
|
|
# Prepare conditioning_latents
|
|
@torch.inference_mode()
|
|
def get_image_latents(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
|
|
|
|
|
|
# Model needs current step number and cfg at inference step. It is possible to write a custom KSampler but I'd like to use ComfyUI's one.
|
|
# The first versions had modified_common_ksampler, but it broke custom KSampler nodes
|
|
def modified_sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={},
|
|
latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
|
|
'''
|
|
Modified by BrushNet nodes
|
|
'''
|
|
cfg_guider = comfy.samplers.CFGGuider(model)
|
|
cfg_guider.set_conds(positive, negative)
|
|
cfg_guider.set_cfg(cfg)
|
|
|
|
### Modified part ######################################################################
|
|
#
|
|
to = add_model_patch_option(model)
|
|
to['model_patch']['all_sigmas'] = sigmas
|
|
|
|
if math.isclose(cfg, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
|
|
to['model_patch']['free_guidance'] = False
|
|
else:
|
|
to['model_patch']['free_guidance'] = True
|
|
#
|
|
#######################################################################################
|
|
|
|
return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
|
|
|
|
|
|
# Main function where magic happens
|
|
@torch.inference_mode()
|
|
def brushnet_inference(x, timesteps, transformer_options):
|
|
if 'model_patch' not in transformer_options:
|
|
print('BrushNet inference: there is no model_patch in transformer_options')
|
|
return ([], 0, [])
|
|
mp = transformer_options['model_patch']
|
|
if 'brushnet_model' not in mp:
|
|
print('BrushNet inference: there is no brushnet_model in transformer_options')
|
|
return ([], 0, [])
|
|
brushnet = mp['brushnet_model']
|
|
if isinstance(brushnet, BrushNetModel) or isinstance(brushnet, PowerPaintModel):
|
|
torch_dtype = mp['brushnet_dtype']
|
|
cl_list = mp['brushnet_latents']
|
|
brushnet_conditioning_scale, control_guidance_start, control_guidance_end = mp['brushnet_controls']
|
|
pe = mp['brushnet_prompt']
|
|
npe = mp['brushnet_negative_prompt']
|
|
ppe, nppe, time_ids = mp['brushnet_add_embeds']
|
|
|
|
do_classifier_free_guidance = mp['free_guidance']
|
|
|
|
x = x.detach().clone()
|
|
x = x.to(torch_dtype).to(brushnet.device)
|
|
|
|
timesteps = timesteps.detach().clone()
|
|
timesteps = timesteps.to(torch_dtype).to(brushnet.device)
|
|
|
|
all_sigmas = mp['all_sigmas']
|
|
sigma = transformer_options['sigmas'][0].item()
|
|
total_steps = all_sigmas.shape[0]
|
|
step = torch.argmin((all_sigmas - sigma).abs()).item()
|
|
|
|
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 = mp['brushnet_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:
|
|
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:
|
|
mp['brushnet_latent_id'] = number + 1
|
|
else:
|
|
mp['brushnet_latent_id'] = 0
|
|
else:
|
|
if number < batch - 1:
|
|
mp['brushnet_latent_id'] = number + 1
|
|
else:
|
|
mp['brushnet_latent_id'] = 0
|
|
else:
|
|
mp['brushnet_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)
|
|
|
|
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,
|
|
)
|
|
else:
|
|
print('BrushNet model is not a BrushNetModel class')
|
|
return ([], 0, [])
|
|
|
|
|
|
# Check and add 'model_patch' to model.model_options['transformer_options']
|
|
def add_model_patch_option(model):
|
|
if 'transformer_options' not in model.model_options:
|
|
model.model_options['transformer_options'] = {}
|
|
to = model.model_options['transformer_options']
|
|
if "model_patch" not in to:
|
|
to["model_patch"] = {}
|
|
return to
|
|
|
|
|
|
# This is main patch function
|
|
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):
|
|
to = add_model_patch_option(model)
|
|
to['model_patch']['brushnet_model'] = brushnet
|
|
to['model_patch']['brushnet_dtype'] = torch_dtype
|
|
to['model_patch']['brushnet_latents'] = conditioning_latents
|
|
to['model_patch']['brushnet_controls'] = controls
|
|
to['model_patch']['brushnet_prompt'] = prompt_embeds
|
|
to['model_patch']['brushnet_negative_prompt'] = negative_prompt_embeds
|
|
to['model_patch']['brushnet_add_embeds'] = (pooled_prompt_embeds, negative_pooled_prompt_embeds, time_ids)
|
|
to['model_patch']['brushnet_latent_id'] = 0
|
|
|
|
is_SDXL = isinstance(model.model.model_config, comfy.supported_models.SDXL)
|
|
|
|
if is_SDXL:
|
|
input_blocks = [[0,0],[1,0],[2,0],[3,0],[4,1],[5,1],[6,0],[7,1],[8,1]]
|
|
output_blocks = [[0,1],[1,1],[2,1],[2,2],[3,1],[4,1],[5,1],[5,2],[6,0],[7,0],[8,0]]
|
|
else:
|
|
input_blocks = [[0,0],[1,1],[2,1],[3,0],[4,1],[5,1],[6,0],[7,1],[8,1],[9,0],[10,0],[11,0]]
|
|
output_blocks = [[0,0],[1,0],[2,0],[2,1],[3,1],[4,1],[5,1],[5,2],[6,1],[7,1],[8,1],[8,2],[9,1],[10,1],[11,1]]
|
|
|
|
# patch model `forward` so we can call BrushNet inference and add additional samples to layers
|
|
if not hasattr(model.model.diffusion_model, 'original_forward'):
|
|
model.model.diffusion_model.original_forward = model.model.diffusion_model.forward
|
|
def forward_patched_by_brushnet(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
|
|
# check if this is brushnet patched model
|
|
if 'model_patch' not in transformer_options or 'brushnet_model' 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)
|
|
# give additional samples to blocks
|
|
for i, block in enumerate(self.input_blocks):
|
|
for j, layer in enumerate(block):
|
|
if [i,j] in input_blocks:
|
|
layer.brushnet_sample = input_samples.pop(0) if input_samples else 0
|
|
self.middle_block[-1].brushnet_sample = mid_sample
|
|
for i, block in enumerate(self.output_blocks):
|
|
for j, layer in enumerate(block):
|
|
if [i,j] in output_blocks:
|
|
layer.brushnet_sample = output_samples.pop(0) if output_samples else 0
|
|
return self.original_forward(x, timesteps, context, y, control, transformer_options, **kwargs)
|
|
model.model.diffusion_model.forward = types.MethodType(forward_patched_by_brushnet, model.model.diffusion_model)
|
|
|
|
# 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, 'brushnet_sample') and type(self) in ComfySDLayers:
|
|
if torch.is_tensor(self.brushnet_sample):
|
|
#print(type(self), h.shape, self.brushnet_sample.shape)
|
|
h += self.brushnet_sample.to(h.dtype).to(h.device)
|
|
else:
|
|
h += self.brushnet_sample
|
|
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)
|
|
|
|
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)
|
|
|
|
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)
|