485 lines
20 KiB
Python
485 lines
20 KiB
Python
import torch
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import torchvision.transforms as T
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import os
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import folder_paths
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import sys
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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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import nodes
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import latent_preview
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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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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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torch_dtype = torch.float16
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sd15_scaling_factor = 0.18215
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sdxl_scaling_factor = 0.13025
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class BrushNetLoader:
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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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"brushnet": (inpaint_safetensors(), ),
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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):
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brushnet_file = os.path.join(folder_paths.models_dir, "inpaint", brushnet)
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is_SDXL = 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 = 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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print('BrushNet model type: SD1.5')
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is_SDXL = False
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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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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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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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print("BrushNet model file:", brushnet_file)
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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="auto",
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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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print("BrushNet model is loaded, SDXL:", is_SDXL)
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return ({"brushnet": brushnet_model, "SDXL": is_SDXL},)
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def inpaint_safetensors():
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inpaint_path = os.path.join(folder_paths.models_dir, 'inpaint')
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brushnet_path = os.path.join(inpaint_path, 'brushnet')
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abs_list = []
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for x in os.walk(inpaint_path):
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for name in x[2]:
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if 'safetensors' in name:
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abs_list.append(os.path.join(x[0], name))
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names = []
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for x in abs_list:
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remain = x
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y = ''
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while remain != inpaint_path:
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remain, folder = os.path.split(remain)
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if len(y) > 0:
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y = os.path.join(folder, y)
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else:
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y = folder
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names.append(y)
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return names
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def brushnet_blocks(sd):
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brushnet_down_block = 0
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brushnet_mid_block = 0
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brushnet_up_block = 0
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for key in sd:
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if 'brushnet_down_block' in key:
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brushnet_down_block += 1
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if 'brushnet_mid_block' in key:
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brushnet_mid_block += 1
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if 'brushnet_up_block' in key:
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brushnet_up_block += 1
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return (brushnet_down_block, brushnet_mid_block, brushnet_up_block, )
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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": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0}),
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"end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0}),
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},
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}
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CATEGORY = "inpaint"
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RETURN_TYPES = ("MODEL","LATENT",)
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RETURN_NAMES = ("model","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 = False
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if isinstance(model.model.model_config, comfy.supported_models.SD15):
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print('Base model type: SD1.5')
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is_SDXL = False
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if brushnet["SDXL"]:
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raise Exception("Base model is SD15, but BrushNet is SDXL type")
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elif isinstance(model.model.model_config, comfy.supported_models.SDXL):
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print('Base model type: SDXL')
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is_SDXL = True
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if not brushnet["SDXL"]:
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raise Exception("Base model is SDXL, but BrushNet is SD15 type")
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#raise Exception("SDXL is not implemented yet")
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else:
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print('Base model type: ', type(model.model.model_config))
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raise Exception("Unsupported model type: " + str(type(model.model.model_config)))
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# prepare image and mask
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# no batches for original image and mask
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if image.shape[0] > 1:
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image = image[0][None,:,:,:]
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if mask.shape[0] > 1:
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mask = mask[0][None,:,:]
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width = image.shape[2]
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height = image.shape[1]
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print("BrushNet image,", image.shape)
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if mask.shape[2] != width or mask.shape[1] != height:
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raise Exception("Image and mask should be the same size")
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masked_image = image * (1.0 - mask[:,:,:,None])
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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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# prepare conditioning latents
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with torch.no_grad():
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processed_image = masked_image.to(vae.device)
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processed_image2 = torch.cat([masked_image] * 2).to(vae.device)
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image_latents = vae.encode(processed_image[:,:,:,:3]) * scaling_factor
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image_latents2 = vae.encode(processed_image2[:,:,:,:3]) * scaling_factor
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processed_mask = 1. - mask[None,:,:,:]
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interpolated_mask = torch.nn.functional.interpolate(
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processed_mask,
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size=(
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image_latents.shape[-2],
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image_latents.shape[-1]
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)
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)
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interpolated_mask = interpolated_mask.to(image_latents.device)
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processed_mask2 = torch.cat([1. - mask[None,:,:,:]] * 2)
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interpolated_mask2 = torch.nn.functional.interpolate(
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processed_mask2,
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size=(
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image_latents2.shape[-2],
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image_latents2.shape[-1]
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)
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)
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interpolated_mask2 = interpolated_mask2.to(image_latents2.device)
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conditioning_latents = torch.concat([image_latents, interpolated_mask], 1).to(dtype=torch_dtype).to(brushnet['brushnet'].device)
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conditioning_latents2 = torch.concat([image_latents2, interpolated_mask2], 1).to(dtype=torch_dtype).to(brushnet['brushnet'].device)
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print('BrushNet CL: image_latents shape =', image_latents2.shape, 'interpolated_mask shape =', interpolated_mask2.shape)
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# prepare embeddings
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prompt_embeds = positive[0][0][0].to(brushnet['brushnet'].device)
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negative_prompt_embeds = negative[0][0][0].to(brushnet['brushnet'].device)
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add_text_embeds = positive[0][1]['pooled_output'].to(brushnet['brushnet'].device)
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negative_pooled_prompt_embeds = negative[0][1]['pooled_output'].to(brushnet['brushnet'].device)
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add_time_ids = torch.FloatTensor([[height, width, 0., 0., height, width]]).to(brushnet['brushnet'].device)
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negative_add_time_ids = add_time_ids
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prompt_embeds2 = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
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add_text_embeds2 = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
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add_time_ids2 = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
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# apply patch to code
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if nodes.common_ksampler.__doc__ is None or 'BrushNet' not in nodes.common_ksampler.__doc__:
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nodes.original_common_ksampler = nodes.common_ksampler
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nodes.common_ksampler = modified_common_ksampler
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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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cloned = model.clone()
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add_brushnet_patch(cloned,
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brushnet['brushnet'],
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conditioning_latents,
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conditioning_latents2,
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(brushnet_conditioning_scale, control_guidance_start, control_guidance_end),
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prompt_embeds, add_text_embeds, add_time_ids,
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prompt_embeds2, add_text_embeds2, add_time_ids2)
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latent = torch.zeros([1, 4, conditioning_latents.shape[2], conditioning_latents.shape[3]], device=brushnet['brushnet'].device)
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#latent = torch.randn([1, 4, conditioning_latents.shape[2], conditioning_latents.shape[3]], device=brushnet['brushnet'].device)
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return (cloned, {"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,
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size=(
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original.shape[1],
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original.shape[2],
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)
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)
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ret = []
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for result in inpaint:
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ret.append(original * (1.0 - blurred_mask[0][0][:,:,None]) + result.to(original.device) * blurred_mask[0][0][:,:,None])
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return (torch.stack(ret), blurred_mask[0],)
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# Model needs current step number at inference step. It is possible to write a custom KSampler but I'd like to use ComfyUI's one.
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def modified_common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0,
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disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
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'''
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Modified by BrushNet nodes
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'''
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latent_image = latent["samples"]
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if disable_noise:
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noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
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else:
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batch_inds = latent["batch_index"] if "batch_index" in latent else None
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noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
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noise_mask = None
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if "noise_mask" in latent:
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noise_mask = latent["noise_mask"]
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#######################################################################################
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#
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latent_preview_callback = latent_preview.prepare_callback(model, steps)
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to = add_model_patch_option(model)
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to['model_patch']['step'] = 0
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to['model_patch']['total_steps'] = steps
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def callback(step, x0, x, total_steps):
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to['model_patch']['step'] = step + 1
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latent_preview_callback(steps, x0, x, total_steps)
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#
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#######################################################################################
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disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
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samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
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denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
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force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback,
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disable_pbar=disable_pbar, seed=seed)
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out = latent.copy()
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out["samples"] = samples
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return (out, )
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def brushnet_inference(x, timesteps, transformer_options):
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if 'model_patch' not in transformer_options:
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print('BrushNet inference: there is no model_patch in transformer_options')
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return ([], 0, [])
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mp = transformer_options['model_patch']
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if 'brushnet_model' not in mp:
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print('BrushNet inference: there is no brushnet_model in transformer_options')
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return ([], 0, [])
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brushnet = mp['brushnet_model']
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if isinstance(brushnet, BrushNetModel):
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conditioning_latents = mp['brushnet_latents']
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conditioning_latents2 = mp['brushnet_latents2']
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brushnet_conditioning_scale, control_guidance_start, control_guidance_end = mp['brushnet_controls']
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prompt_embeds = mp['brushnet_prompt']
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prompt_embeds2 = mp['brushnet_prompt2']
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added_cond_kwargs = mp['brushnet_add_embeds']
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added_cond_kwargs2 = mp['brushnet_add_embeds2']
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step = mp['step']
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total_steps = mp['total_steps']
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brushnet_keep = []
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for i in range(total_steps):
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keeps = [
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1.0 - float(i / total_steps < s or (i + 1) / total_steps > e)
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for s, e in zip([control_guidance_start], [control_guidance_end])
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]
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brushnet_keep.append(keeps[0])
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cond_scale = brushnet_conditioning_scale * brushnet_keep[step]
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if x.shape[0] == 2:
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return brushnet(x,
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encoder_hidden_states=prompt_embeds2,
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brushnet_cond=conditioning_latents2,
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timestep = timesteps,
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conditioning_scale=cond_scale,
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guess_mode=False,
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added_cond_kwargs=added_cond_kwargs2,
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return_dict=False,
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)
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else:
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return brushnet(x,
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encoder_hidden_states=prompt_embeds,
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brushnet_cond=conditioning_latents,
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timestep = timesteps,
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conditioning_scale=cond_scale,
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guess_mode=False,
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added_cond_kwargs=added_cond_kwargs,
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return_dict=False,
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)
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else:
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print('BrushNet model is not a BrushNetModel class')
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return ([], 0, [])
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def add_model_patch_option(model):
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if 'transformer_options' not in model.model_options:
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model.model_options['transformer_options'] = {}
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to = model.model_options['transformer_options']
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if "model_patch" not in to:
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to["model_patch"] = {}
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return to
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def add_brushnet_patch(model, brushnet, conditioning_latents, conditioning_latents2,
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controls,
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prompt_embeds, add_text_embeds, add_time_ids,
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prompt_embeds2, add_text_embeds2, add_time_ids2):
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to = add_model_patch_option(model)
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to['model_patch']['brushnet_model'] = brushnet
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to['model_patch']['brushnet_latents'] = conditioning_latents
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to['model_patch']['brushnet_latents2'] = conditioning_latents2
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to['model_patch']['brushnet_controls'] = controls
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to['model_patch']['brushnet_prompt'] = prompt_embeds
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to['model_patch']['brushnet_prompt2'] = prompt_embeds2
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to['model_patch']['brushnet_add_embeds'] = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
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to['model_patch']['brushnet_add_embeds2'] = {"text_embeds": add_text_embeds2, "time_ids": add_time_ids2}
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is_SDXL = isinstance(model.model.model_config, comfy.supported_models.SDXL)
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if is_SDXL:
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input_blocks = [[0,0],[1,0],[2,0],[3,0],[4,1],[5,1],[6,0],[7,1],[8,1]]
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output_blocks = [[0,1],[1,1],[2,1],[2,2],[3,1],[4,1],[5,1],[5,2],[6,0],[7,0],[8,0]]
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else:
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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]]
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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]]
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# patch model `forward` so we can call BrushNet inference and setup additional samples to layers
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if not hasattr(model.model.diffusion_model, 'original_forward'):
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model.model.diffusion_model.original_forward = model.model.diffusion_model.forward
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def forward_patched_by_brushnet(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
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# brushnet inference
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input_samples, mid_sample, output_samples = brushnet_inference(x, timesteps, transformer_options)
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# give blocks additional samples
|
|
# no control of samples length
|
|
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'):
|
|
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)
|