533 lines
21 KiB
Python
533 lines
21 KiB
Python
import os
|
|
import torch
|
|
import torch.nn.functional as F
|
|
|
|
try:
|
|
from diffusers import (
|
|
DPMSolverMultistepScheduler,
|
|
EulerDiscreteScheduler,
|
|
EulerAncestralDiscreteScheduler,
|
|
AutoencoderKL,
|
|
LCMScheduler,
|
|
DDPMScheduler,
|
|
DEISMultistepScheduler,
|
|
PNDMScheduler,
|
|
UniPCMultistepScheduler
|
|
)
|
|
from .scheduling_tcd import TCDScheduler
|
|
from diffusers.loaders.single_file_utils import (
|
|
convert_ldm_vae_checkpoint,
|
|
convert_ldm_unet_checkpoint,
|
|
create_vae_diffusers_config,
|
|
create_unet_diffusers_config,
|
|
create_text_encoder_from_ldm_clip_checkpoint
|
|
)
|
|
except:
|
|
raise ImportError("Diffusers version too old. Please update to 0.27.2 minimum.")
|
|
|
|
|
|
from .brushnet.pipeline_brushnet import StableDiffusionBrushNetPipeline
|
|
from .brushnet.brushnet import BrushNetModel
|
|
from .brushnet.unet_2d_condition import UNet2DConditionModel
|
|
|
|
from contextlib import nullcontext
|
|
from diffusers.utils import is_accelerate_available
|
|
if is_accelerate_available():
|
|
from accelerate import init_empty_weights
|
|
from accelerate.utils import set_module_tensor_to_device
|
|
|
|
import safetensors.torch
|
|
from omegaconf import OmegaConf
|
|
from transformers import CLIPTokenizer
|
|
|
|
import comfy.model_management as mm
|
|
import comfy.utils
|
|
import folder_paths
|
|
|
|
script_directory = os.path.dirname(os.path.abspath(__file__))
|
|
IS_MODEL_CPU_OFFLOAD_ENABLED = False
|
|
|
|
class brushnet_model_loader:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"model": ("MODEL",),
|
|
"clip": ("CLIP",),
|
|
"vae": ("VAE",),
|
|
"brushnet_model": (
|
|
[
|
|
"brushnet_segmentation_mask",
|
|
"brushnet_random_mask",
|
|
], {
|
|
"default": "brushnet_segmentation_mask"
|
|
}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("BRUSHNET",)
|
|
RETURN_NAMES = ("brushnet",)
|
|
FUNCTION = "loadmodel"
|
|
CATEGORY = "BrushNetWrapper"
|
|
|
|
def loadmodel(self, model, clip, vae, brushnet_model):
|
|
mm.soft_empty_cache()
|
|
dtype = mm.unet_dtype()
|
|
device = mm.get_torch_device()
|
|
|
|
custom_config = {
|
|
"model": model,
|
|
"vae": vae,
|
|
"clip": clip,
|
|
"brushnet_model": brushnet_model
|
|
}
|
|
if not hasattr(self, "model") or self.model == None or custom_config != self.current_config:
|
|
global IS_MODEL_CPU_OFFLOAD_ENABLED
|
|
IS_MODEL_CPU_OFFLOAD_ENABLED = False
|
|
pbar = comfy.utils.ProgressBar(5)
|
|
self.current_config = custom_config
|
|
|
|
original_config = OmegaConf.load(os.path.join(script_directory, f"configs/v1-inference.yaml"))
|
|
brushnet_config = OmegaConf.load(os.path.join(script_directory, f"configs/brushnet_config.json"))
|
|
|
|
brushnet_model_folder = os.path.join(folder_paths.models_dir,"brushnet")
|
|
checkpoint_path = os.path.join(brushnet_model_folder, f"{brushnet_model}_fp16.safetensors")
|
|
print(f"Loading BrushNet from {checkpoint_path}")
|
|
|
|
if not os.path.exists(checkpoint_path):
|
|
print(f"Selected model: {checkpoint_path} not found, downloading...")
|
|
from huggingface_hub import snapshot_download
|
|
snapshot_download(repo_id="Kijai/BrushNet-fp16",
|
|
allow_patterns=[f"*{brushnet_model}*"],
|
|
local_dir=brushnet_model_folder,
|
|
local_dir_use_symlinks=False
|
|
)
|
|
|
|
#create models
|
|
with (init_empty_weights() if is_accelerate_available() else nullcontext()):
|
|
brushnet = BrushNetModel(**brushnet_config)
|
|
|
|
converted_vae_config = create_vae_diffusers_config(original_config, image_size=512)
|
|
new_vae = AutoencoderKL(**converted_vae_config)
|
|
|
|
converted_unet_config = create_unet_diffusers_config(original_config, image_size=512)
|
|
new_unet = UNet2DConditionModel(**converted_unet_config)
|
|
|
|
pbar.update(1)
|
|
|
|
#load weights
|
|
brushnet_sd = comfy.utils.load_torch_file(checkpoint_path)
|
|
for key in brushnet_sd:
|
|
set_module_tensor_to_device(brushnet, key, device=device, dtype=dtype, value=brushnet_sd[key])
|
|
del brushnet_sd
|
|
|
|
clip_sd = None
|
|
load_models = [model]
|
|
load_models.append(clip.load_model())
|
|
clip_sd = clip.get_sd()
|
|
comfy.model_management.load_models_gpu(load_models)
|
|
sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), None)
|
|
|
|
converted_vae = convert_ldm_vae_checkpoint(sd, converted_vae_config)
|
|
for key in converted_vae:
|
|
set_module_tensor_to_device(new_vae, key, device=device, dtype=dtype, value=converted_vae[key])
|
|
del converted_vae
|
|
pbar.update(1)
|
|
|
|
converted_unet = convert_ldm_unet_checkpoint(sd, converted_unet_config)
|
|
for key in converted_unet:
|
|
set_module_tensor_to_device(new_unet, key, device=device, dtype=dtype, value=converted_unet[key])
|
|
del converted_unet
|
|
|
|
pbar.update(1)
|
|
|
|
# 3. text_model
|
|
print("loading text model")
|
|
text_encoder = create_text_encoder_from_ldm_clip_checkpoint("openai/clip-vit-large-patch14",sd)
|
|
text_encoder.to(dtype)
|
|
|
|
# 4. tokenizer
|
|
tokenizer_path = os.path.join(script_directory, "configs/tokenizer")
|
|
tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
|
|
|
|
pbar.update(1)
|
|
del sd
|
|
|
|
self.pipe = StableDiffusionBrushNetPipeline(
|
|
unet=new_unet,
|
|
vae=new_vae,
|
|
text_encoder=text_encoder,
|
|
tokenizer=tokenizer,
|
|
scheduler=None,
|
|
brushnet=brushnet,
|
|
requires_safety_checker=False,
|
|
safety_checker=None,
|
|
feature_extractor=None
|
|
)
|
|
#self.pipe.enable_model_cpu_offload()
|
|
pbar.update(1)
|
|
brushnet = {
|
|
"pipe": self.pipe,
|
|
}
|
|
|
|
return (brushnet,)
|
|
|
|
class brushnet_sampler:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"brushnet": ("BRUSHNET",),
|
|
"image": ("IMAGE",),
|
|
"mask": ("MASK",),
|
|
"steps": ("INT", {"default": 25, "min": 1, "max": 200, "step": 1}),
|
|
"cfg": ("FLOAT", {"default": 7.5, "min": 0.0, "max": 20.0, "step": 0.01}),
|
|
"cfg_brushnet": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 20.0, "step": 0.01}),
|
|
"control_guidance_start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"control_guidance_end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"guess_mode": ("BOOLEAN", {"default": False}),
|
|
"clip_skip": ("INT", {"default": 0, "min": 0, "max": 20, "step": 1}),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
"scheduler": (
|
|
[
|
|
"DPMSolverMultistepScheduler",
|
|
"DPMSolverMultistepScheduler_SDE_karras",
|
|
"DDPMScheduler",
|
|
"LCMScheduler",
|
|
"PNDMScheduler",
|
|
"DEISMultistepScheduler",
|
|
"EulerDiscreteScheduler",
|
|
"EulerAncestralDiscreteScheduler",
|
|
"UniPCMultistepScheduler",
|
|
"TCDScheduler"
|
|
], {
|
|
"default": "UniPCMultistepScheduler"
|
|
}),
|
|
"prompt": ("STRING", {"multiline": True, "default": "caption",}),
|
|
"n_prompt": ("STRING", {"multiline": True, "default": "caption",}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
RETURN_NAMES = ("images",)
|
|
FUNCTION = "process"
|
|
CATEGORY = "BrushNetWrapper"
|
|
|
|
def process(self, brushnet, image, mask, prompt, n_prompt, steps, cfg, guess_mode, clip_skip,
|
|
cfg_brushnet, control_guidance_start, control_guidance_end, seed, scheduler):
|
|
device = mm.get_torch_device()
|
|
mm.soft_empty_cache()
|
|
pipe=brushnet["pipe"]
|
|
|
|
global IS_MODEL_CPU_OFFLOAD_ENABLED
|
|
if not IS_MODEL_CPU_OFFLOAD_ENABLED:
|
|
pipe.enable_model_cpu_offload()
|
|
IS_MODEL_CPU_OFFLOAD_ENABLED = True
|
|
|
|
scheduler_config = {
|
|
"num_train_timesteps": 1000,
|
|
"beta_start": 0.00085,
|
|
"beta_end": 0.012,
|
|
"beta_schedule": "scaled_linear",
|
|
"steps_offset": 1,
|
|
}
|
|
if scheduler == "DPMSolverMultistepScheduler":
|
|
noise_scheduler = DPMSolverMultistepScheduler(**scheduler_config)
|
|
elif scheduler == "DPMSolverMultistepScheduler_SDE_karras":
|
|
scheduler_config.update({"algorithm_type": "sde-dpmsolver++"})
|
|
scheduler_config.update({"use_karras_sigmas": True})
|
|
noise_scheduler = DPMSolverMultistepScheduler(**scheduler_config)
|
|
elif scheduler == "DDPMScheduler":
|
|
noise_scheduler = DDPMScheduler(**scheduler_config)
|
|
elif scheduler == "LCMScheduler":
|
|
noise_scheduler = LCMScheduler(**scheduler_config)
|
|
elif scheduler == "PNDMScheduler":
|
|
scheduler_config.update({"set_alpha_to_one": False})
|
|
scheduler_config.update({"trained_betas": None})
|
|
noise_scheduler = PNDMScheduler(**scheduler_config)
|
|
elif scheduler == "DEISMultistepScheduler":
|
|
noise_scheduler = DEISMultistepScheduler(**scheduler_config)
|
|
elif scheduler == "EulerDiscreteScheduler":
|
|
noise_scheduler = EulerDiscreteScheduler(**scheduler_config)
|
|
elif scheduler == "EulerAncestralDiscreteScheduler":
|
|
noise_scheduler = EulerAncestralDiscreteScheduler(**scheduler_config)
|
|
elif scheduler == "UniPCMultistepScheduler":
|
|
noise_scheduler = UniPCMultistepScheduler(**scheduler_config)
|
|
elif scheduler == "TCDScheduler":
|
|
noise_scheduler = TCDScheduler(**scheduler_config)
|
|
pipe.scheduler = noise_scheduler
|
|
|
|
B, H, W, C = image.shape
|
|
image = image.permute(0, 3, 1, 2).to(device)
|
|
|
|
#handle masks
|
|
if len(mask.shape) == 2:
|
|
mask = mask.unsqueeze(0)
|
|
mask = mask.to(device)
|
|
if mask.shape[0] < B:
|
|
repeat_times = B // mask.shape[0]
|
|
mask = mask.repeat(repeat_times, 1, 1, 1)
|
|
resized_mask = F.interpolate(mask.unsqueeze(1), size=[H, W], mode='nearest').squeeze(1)
|
|
|
|
image = image * (1-resized_mask)
|
|
|
|
if 'ip_adapter' in brushnet:
|
|
print("Using IP adapter")
|
|
prompt_embeds, negative_prompt_embeds = brushnet['ip_adapter'].get_prompt_embeds(
|
|
brushnet['ip_adapter_image'],
|
|
prompt=prompt,
|
|
negative_prompt=n_prompt,
|
|
weight=[brushnet['ip_adapter_weight']]
|
|
)
|
|
use_ipadapter = True
|
|
prompt_list = None
|
|
n_prompt_list = None
|
|
else:
|
|
prompt_list = []
|
|
prompt_list.append(prompt)
|
|
if len(prompt_list) < B:
|
|
prompt_list += [prompt_list[-1]] * (B - len(prompt_list))
|
|
|
|
n_prompt_list = []
|
|
n_prompt_list.append(n_prompt)
|
|
if len(n_prompt_list) < B:
|
|
n_prompt_list += [n_prompt_list[-1]] * (B - len(n_prompt_list))
|
|
|
|
prompt_embeds, negative_prompt_embeds = None, None
|
|
use_ipadapter = False
|
|
|
|
#sample
|
|
generator = torch.Generator(device).manual_seed(seed)
|
|
|
|
images = pipe(
|
|
prompt=prompt_list,
|
|
negative_prompt=n_prompt_list,
|
|
image=image,
|
|
ipadapter_image=None,
|
|
prompt_embeds=prompt_embeds if use_ipadapter else None,
|
|
negative_prompt_embeds=negative_prompt_embeds if use_ipadapter else None,
|
|
mask=resized_mask,
|
|
num_inference_steps=steps,
|
|
generator=generator,
|
|
guidance_scale=cfg,
|
|
guess_mode=guess_mode,
|
|
clip_skip=clip_skip if clip_skip > 0 else None,
|
|
brushnet_conditioning_scale=cfg_brushnet,
|
|
control_guidance_start=control_guidance_start,
|
|
control_guidance_end=control_guidance_end,
|
|
output_type="pt"
|
|
).images
|
|
|
|
image_out = images.permute(0, 2, 3, 1).cpu().float()
|
|
return (image_out,)
|
|
|
|
|
|
class brushnet_ella_loader:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"brushnet": ("BRUSHNET",),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("BRUSHNET",)
|
|
RETURN_NAMES = ("brushnet",)
|
|
FUNCTION = "loadmodel"
|
|
CATEGORY = "BrushNetWrapper"
|
|
|
|
def loadmodel(self, brushnet):
|
|
print("loading ELLA")
|
|
from .ella.model import ELLA
|
|
from .ella.ella_unet import ELLAProxyUNet
|
|
checkpoint_path = os.path.join(folder_paths.models_dir,'ella')
|
|
ella_path = os.path.join(checkpoint_path, 'ella-sd1.5-tsc-t5xl.safetensors')
|
|
if not os.path.exists(ella_path):
|
|
from huggingface_hub import snapshot_download
|
|
snapshot_download(repo_id="QQGYLab/ELLA", local_dir=checkpoint_path, local_dir_use_symlinks=False)
|
|
|
|
ella = ELLA()
|
|
safetensors.torch.load_model(ella, ella_path, strict=True)
|
|
ella_unet = ELLAProxyUNet(ella, brushnet['pipe'].unet)
|
|
brushnet['pipe'].unet = ella_unet
|
|
|
|
return (brushnet,)
|
|
|
|
class brushnet_ipadapter_matteo:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"brushnet": ("BRUSHNET",),
|
|
"image": ("IMAGE",),
|
|
"ipadapter": (folder_paths.get_filename_list("ipadapter"), ),
|
|
"clip_vision" : (folder_paths.get_filename_list("clip_vision"), ),
|
|
"weight": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("BRUSHNET",)
|
|
RETURN_NAMES = ("brushnet",)
|
|
FUNCTION = "loadmodel"
|
|
CATEGORY = "BrushNetWrapper"
|
|
|
|
def loadmodel(self, image, brushnet, ipadapter, clip_vision, weight):
|
|
from .ip_adapter.ip_adapter import IPAdapter
|
|
from transformers import CLIPVisionConfig, CLIPVisionModelWithProjection
|
|
device = mm.get_torch_device()
|
|
dtype = mm.unet_dtype()
|
|
ipadapter_path = folder_paths.get_full_path("ipadapter", ipadapter)
|
|
|
|
clip_vision_path = folder_paths.get_full_path("clip_vision", clip_vision)
|
|
|
|
clip_vision_config_path = OmegaConf.load(os.path.join(script_directory, f"configs/clip_vision.json"))
|
|
clip_vision_config = CLIPVisionConfig(**clip_vision_config_path)
|
|
with (init_empty_weights() if is_accelerate_available() else nullcontext()):
|
|
image_encoder = CLIPVisionModelWithProjection(clip_vision_config)
|
|
clip_vision_sd = comfy.utils.load_torch_file(clip_vision_path)
|
|
for key in clip_vision_sd:
|
|
set_module_tensor_to_device(image_encoder, key, device=device, dtype=dtype, value=clip_vision_sd[key])
|
|
|
|
brushnet['pipe'].to(device)
|
|
ip_adapter = IPAdapter(brushnet['pipe'], ipadapter_path, image_encoder, device=device)
|
|
image = image.permute(0, 3, 1, 2).to(device)
|
|
|
|
brushnet['ip_adapter'] = ip_adapter
|
|
brushnet['ip_adapter_image'] = image
|
|
brushnet['ip_adapter_weight'] = weight
|
|
return (brushnet,)
|
|
|
|
class brushnet_sampler_ella:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"brushnet": ("BRUSHNET",),
|
|
"ella_embeds": ("ELLAEMBEDS",),
|
|
"image": ("IMAGE",),
|
|
"mask": ("MASK",),
|
|
"steps": ("INT", {"default": 25, "min": 1, "max": 200, "step": 1}),
|
|
"cfg": ("FLOAT", {"default": 7.5, "min": 0.0, "max": 20.0, "step": 0.01}),
|
|
"cfg_brushnet": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 20.0, "step": 0.01}),
|
|
"control_guidance_start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"control_guidance_end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"guess_mode": ("BOOLEAN", {"default": False}),
|
|
"clip_skip": ("INT", {"default": 0, "min": 0, "max": 20, "step": 1}),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
"scheduler": (
|
|
[
|
|
"DPMSolverMultistepScheduler",
|
|
"DPMSolverMultistepScheduler_SDE_karras",
|
|
"DDPMScheduler",
|
|
"LCMScheduler",
|
|
"PNDMScheduler",
|
|
"DEISMultistepScheduler",
|
|
"EulerDiscreteScheduler",
|
|
"EulerAncestralDiscreteScheduler",
|
|
"UniPCMultistepScheduler",
|
|
"TCDScheduler"
|
|
], {
|
|
"default": "UniPCMultistepScheduler"
|
|
}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
RETURN_NAMES = ("images",)
|
|
FUNCTION = "process"
|
|
CATEGORY = "BrushNetWrapper"
|
|
|
|
def process(self, brushnet, image, mask, steps, cfg, guess_mode, clip_skip, ella_embeds,
|
|
cfg_brushnet, control_guidance_start, control_guidance_end, seed, scheduler):
|
|
device = mm.get_torch_device()
|
|
dtype = mm.unet_dtype()
|
|
mm.soft_empty_cache()
|
|
pipe=brushnet["pipe"].to(dtype)
|
|
|
|
global IS_MODEL_CPU_OFFLOAD_ENABLED
|
|
if not IS_MODEL_CPU_OFFLOAD_ENABLED:
|
|
pipe.enable_model_cpu_offload()
|
|
IS_MODEL_CPU_OFFLOAD_ENABLED = True
|
|
|
|
scheduler_config = {
|
|
"num_train_timesteps": 1000,
|
|
"beta_start": 0.00085,
|
|
"beta_end": 0.012,
|
|
"beta_schedule": "scaled_linear",
|
|
"steps_offset": 1,
|
|
}
|
|
if scheduler == "DPMSolverMultistepScheduler":
|
|
noise_scheduler = DPMSolverMultistepScheduler(**scheduler_config)
|
|
elif scheduler == "DPMSolverMultistepScheduler_SDE_karras":
|
|
scheduler_config.update({"algorithm_type": "sde-dpmsolver++"})
|
|
scheduler_config.update({"use_karras_sigmas": True})
|
|
noise_scheduler = DPMSolverMultistepScheduler(**scheduler_config)
|
|
elif scheduler == "DDPMScheduler":
|
|
noise_scheduler = DDPMScheduler(**scheduler_config)
|
|
elif scheduler == "LCMScheduler":
|
|
noise_scheduler = LCMScheduler(**scheduler_config)
|
|
elif scheduler == "PNDMScheduler":
|
|
scheduler_config.update({"set_alpha_to_one": False})
|
|
scheduler_config.update({"trained_betas": None})
|
|
noise_scheduler = PNDMScheduler(**scheduler_config)
|
|
elif scheduler == "DEISMultistepScheduler":
|
|
noise_scheduler = DEISMultistepScheduler(**scheduler_config)
|
|
elif scheduler == "EulerDiscreteScheduler":
|
|
noise_scheduler = EulerDiscreteScheduler(**scheduler_config)
|
|
elif scheduler == "EulerAncestralDiscreteScheduler":
|
|
noise_scheduler = EulerAncestralDiscreteScheduler(**scheduler_config)
|
|
elif scheduler == "UniPCMultistepScheduler":
|
|
noise_scheduler = UniPCMultistepScheduler(**scheduler_config)
|
|
elif scheduler == "TCDScheduler":
|
|
noise_scheduler = TCDScheduler(**scheduler_config)
|
|
pipe.scheduler = noise_scheduler
|
|
|
|
B, H, W, C = image.shape
|
|
image = image.permute(0, 3, 1, 2).to(device)
|
|
|
|
#handle masks
|
|
if len(mask.shape) == 2:
|
|
mask = mask.unsqueeze(0)
|
|
mask = mask.to(device)
|
|
if mask.shape[0] < B:
|
|
repeat_times = B // mask.shape[0]
|
|
mask = mask.repeat(repeat_times, 1, 1, 1)
|
|
resized_mask = F.interpolate(mask.unsqueeze(1), size=[H, W], mode='nearest').squeeze(1)
|
|
|
|
image = image * (1-resized_mask)
|
|
|
|
#sample
|
|
generator = torch.Generator(device).manual_seed(seed)
|
|
|
|
images = pipe(
|
|
prompt=None,
|
|
negative_prompt=None,
|
|
prompt_embeds=ella_embeds["prompt_embeds"],
|
|
negative_prompt_embeds=ella_embeds["negative_prompt_embeds"],
|
|
image=image,
|
|
ipadapter_image=None,
|
|
mask=resized_mask,
|
|
num_inference_steps=steps,
|
|
generator=generator,
|
|
guidance_scale=cfg,
|
|
guess_mode=guess_mode,
|
|
clip_skip=clip_skip if clip_skip > 0 else None,
|
|
brushnet_conditioning_scale=cfg_brushnet,
|
|
control_guidance_start=control_guidance_start,
|
|
control_guidance_end=control_guidance_end,
|
|
output_type="pt"
|
|
).images
|
|
|
|
image_out = images.permute(0, 2, 3, 1).cpu().float()
|
|
return (image_out,)
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"brushnet_model_loader": brushnet_model_loader,
|
|
"brushnet_sampler": brushnet_sampler,
|
|
"brushnet_sampler_ella": brushnet_sampler_ella,
|
|
"brushnet_ella_loader": brushnet_ella_loader,
|
|
"brushnet_ipadapter_matteo": brushnet_ipadapter_matteo,
|
|
}
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"brushnet_model_loader": "BrushNet Model Loader",
|
|
"brushnet_sampler": "BrushNet Sampler",
|
|
"brushnet_sampler_ella": "BrushNet Sampler (ELLA)",
|
|
"brushnet_ella_loader": "BrushNet ELLA Loader",
|
|
"brushnet_ipadapter_matteo": "BrushNet IP Adapter (Matteo)",
|
|
}
|