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# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
import folder_paths, os
from comfy.supported_models import FluxInpaint, models
from nodes import UNETLoader
try:
from scepter.modules.utils.file_system import FS
from scepter.modules.annotator.registry import ANNOTATORS
from scepter.modules.utils.config import Config
fs_list = [
Config(cfg_dict={"NAME": "HuggingfaceFs", "TEMP_DIR": "./"}, load=False),
Config(cfg_dict={"NAME": "ModelscopeFs", "TEMP_DIR": "./"}, load=False),
Config(cfg_dict={"NAME": "HttpFs", "TEMP_DIR": "./"}, load=False),
Config(cfg_dict={"NAME": "LocalFs", "TEMP_DIR": "./"}, load=False)
]
for one_fs in fs_list:
FS.init_fs_client(one_fs)
SCEPTER = True
except:
SCEPTER = False
class ACEPlus(FluxInpaint):
unet_config = {
"image_model": "flux",
"guidance_embed": True,
"in_channels": 112,
}
class ACEPlusFFTLoader(UNETLoader):
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {"required": {"unet_name": (folder_paths.get_filename_list("diffusion_models"), ),
"weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],)
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "load_unet"
CATEGORY = "ComfyUI-ACE_Plus"
def load_unet(self, unet_name, weight_dtype):
models.append(ACEPlus)
return super().load_unet(unet_name, weight_dtype)
import torch
import node_helpers
class ACEPlusFFTConditioning:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {"required": {"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"vae": ("VAE", ),
"ucpixels": ("IMAGE", ),
"cpixels": ("IMAGE", ),
"mask": ("MASK", ),
"noise_mask": ("BOOLEAN", {"default": True, "tooltip": "Add a noise mask to the latent "
"so sampling will only happen "
"within the mask. Might improve "
"results or completely break "
"things depending on the model."}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "ComfyUI-ACE_Plus"
def encode(self,
positive,
negative,
vae,
ucpixels,
cpixels,
mask,
noise_mask=True):
x = (ucpixels.shape[1] // 8) * 8
y = (ucpixels.shape[2] // 8) * 8
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])),
size=(ucpixels.shape[1], ucpixels.shape[2]), mode="bilinear")
orig_pixels = ucpixels
pixels = orig_pixels.clone()
if pixels.shape[1] != x or pixels.shape[2] != y:
x_offset = (pixels.shape[1] % 8) // 2
y_offset = (pixels.shape[2] % 8) // 2
pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]
mask = mask[:, :, x_offset:x + x_offset, y_offset:y + y_offset]
orig_c_pixels = cpixels
c_pixels = orig_c_pixels.clone()
if orig_c_pixels.shape[1] != x or orig_c_pixels.shape[2] != y:
x_offset = (orig_c_pixels.shape[1] % 8) // 2
y_offset = (orig_c_pixels.shape[2] % 8) // 2
c_pixels = orig_c_pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]
concat_latent = vae.encode(pixels)
orig_latent = vae.encode(orig_pixels)
c_concat_latent = vae.encode(c_pixels)
out_latent = {"samples": orig_latent}
if noise_mask:
out_latent["noise_mask"] = mask
out = []
for conditioning in [positive, negative]:
c = node_helpers.conditioning_set_values(conditioning, {
"concat_latent_image": torch.cat([concat_latent, c_concat_latent], dim=1),
"concat_mask": mask})
out.append(c)
return (out[0], out[1], out_latent)
class ACEPlusLoraConditioning:
@classmethod
def INPUT_TYPES(s):
return {"required": {"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"vae": ("VAE",),
"pixels": ("IMAGE",),
"mask": ("MASK",),
"noise_mask": ("BOOLEAN", {"default": True,
"tooltip": "Add a noise mask to the latent so sampling will only happen within the mask. Might improve results or completely break things depending on the model."}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "ComfyUI-ACE_Plus"
def encode(self, positive, negative, pixels, vae, mask, noise_mask=True):
x = (pixels.shape[1] // 8) * 8
y = (pixels.shape[2] // 8) * 8
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])),
size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
orig_pixels = pixels
pixels = orig_pixels.clone()
if pixels.shape[1] != x or pixels.shape[2] != y:
x_offset = (pixels.shape[1] % 8) // 2
y_offset = (pixels.shape[2] % 8) // 2
pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]
mask = mask[:, :, x_offset:x + x_offset, y_offset:y + y_offset]
concat_latent = vae.encode(pixels)
orig_latent = vae.encode(orig_pixels)
out_latent = {}
out_latent["samples"] = orig_latent
if noise_mask:
out_latent["noise_mask"] = mask
out = []
for conditioning in [positive, negative]:
c = node_helpers.conditioning_set_values(conditioning, {"concat_latent_image": concat_latent,
"concat_mask": mask})
out.append(c)
return (out[0], out[1], out_latent)
import torch
import math
import os
import yaml
import torchvision.transforms as T
import numpy as np
from PIL import Image
class AcePlusFFTProcessor:
def __init__(self,
max_aspect_ratio=4,
d=16,
max_seq_len=1024):
self.max_aspect_ratio = max_aspect_ratio
self.max_seq_len = max_seq_len
self.d = d
current_dir = os.path.dirname(os.path.abspath(__file__))
config_path = os.path.join(current_dir, 'config', 'ace_plus_fft_processor.yaml')
self.processor_cfg = self.load_yaml(config_path)
self.task_list = {}
for task in self.processor_cfg['PREPROCESSOR']:
self.task_list[task['TYPE']] = task
self.transforms = T.Compose([
T.ToTensor(),
T.Normalize(mean=[0, 0, 0], std=[1.0, 1.0, 1.0])
])
CATEGORY = 'ComfyUI-ACE_Plus'
@classmethod
def INPUT_TYPES(s):
return {
'required': {
'use_reference': ('BOOLEAN', {'default': True}),
'height': ('INT', {
'default': 1024,
'min': 256,
'max': 1436,
'step': 16
}),
'width': ('INT', {
'default': 1024,
'min': 256,
'max': 1436,
'step': 16
}),
'task_type': (list(s().task_list.keys()),),
'keep_pixels_rate': ('FLOAT', {
'default': 0.8,
'min': 0,
'max': 1,
'step': 0.01
}),
'max_seq_length': ('INT', {
'default': 3072,
'min': 1024,
'max': 5120,
'step': 0.01
}),
},
'optional': {
'reference_image': ('IMAGE',),
'edit_image': ('IMAGE',),
'edit_mask': ('MASK',),
}
}
OUTPUT_NODE = True
RETURN_TYPES = ('IMAGE', 'IMAGE', 'MASK', 'INT', 'INT', 'INT')
RETURN_NAMES = ('UC_IMAGE', 'C_IMAGE', 'MASK', 'OUT_H', 'OUT_W', 'SLICE_W')
FUNCTION = 'preprocess'
def load_yaml(self, cfg_file):
with open(cfg_file, 'r') as f:
cfg = yaml.load(f.read(), Loader=yaml.SafeLoader)
return cfg
def image_check(self, image):
if image is None:
return image
# preprocess
H, W = image.shape[1: 3]
image = image.permute(0, 3, 1, 2)
if H / W > self.max_aspect_ratio:
image[0] = T.CenterCrop([int(self.max_aspect_ratio * W), W])(image[0])
elif W / H > self.max_aspect_ratio:
image[0] = T.CenterCrop([H, int(self.max_aspect_ratio * H)])(image[0])
return image[0]
def trans_pil_tensor(self, pil_image):
transform = T.Compose([
T.ToTensor()
])
tensor_image = transform(pil_image)
return tensor_image
def edit_preprocess(self, processor, device, edit_image, edit_mask):
if edit_image is None or processor is None:
return edit_image
if not SCEPTER:
raise ImportError(f'Please install scepter to use edit processor {processor} by '
f'runing "pip install scepter" in the conda env')
processor = Config(cfg_dict=processor, load=False)
processor = ANNOTATORS.build(processor).to(device)
edit_image = Image.fromarray(np.array(edit_image[0] * 255).astype(np.uint8)).convert('RGB')
new_edit_image = processor(np.asarray(edit_image))
del processor
new_edit_image = Image.fromarray(new_edit_image)
if edit_mask is not None:
edit_mask = np.where(edit_mask > 0.5, 1, 0) * 255
edit_mask = Image.fromarray(np.array(edit_mask[0]).astype(np.uint8)).convert('L')
if new_edit_image.size != edit_image.size:
edit_image = T.Resize((edit_image.size[1], edit_image.size[0]),
interpolation=T.InterpolationMode.BILINEAR,
antialias=True)(new_edit_image)
image = Image.composite(new_edit_image, edit_image, edit_mask)
return self.trans_pil_tensor(image).unsqueeze(0).permute(0, 2, 3, 1)
def preprocess(self,
reference_image=None,
edit_image=None,
edit_mask=None,
use_reference=True,
task_type=None,
height=1024,
width=1024,
keep_pixels_rate=0.8,
max_seq_length=4096):
self.max_seq_len = max_seq_length
if not use_reference and edit_image is not None:
reference_image = None
if edit_mask is not None and edit_image is not None:
iH, iW = edit_image.shape[1:3]
mH, mW = edit_mask.shape[1:3]
if iH != mH or iW != mW:
edit_mask = torch.ones(edit_image.shape[:3])
if task_type != 'repainting':
repainting_scale = 0
else:
repainting_scale = 1
if task_type in self.task_list:
edit_image = self.edit_preprocess(self.task_list[task_type]['ANNOTATOR'], 0,
edit_image, edit_mask)
if reference_image is not None:
reference_image = self.image_check(reference_image) - 0.5
if edit_image is not None:
edit_image = self.image_check(edit_image) - 0.5
# for reference generation
if edit_image is None:
edit_image = torch.zeros([3, height, width])
edit_mask = torch.ones([1, height, width])
else:
if edit_mask is None:
_, eH, eW = edit_image.shape
edit_mask = np.ones((eH, eW))
else:
edit_mask = np.asarray(edit_mask)[0]
edit_mask = np.where(edit_mask > 0.5, 1, 0)
edit_mask = edit_mask.astype(
np.float32) if np.any(edit_mask) else np.ones_like(edit_mask).astype(
np.float32)
edit_mask = torch.tensor(edit_mask).unsqueeze(0)
edit_image = edit_image * (1 - edit_mask * repainting_scale)
out_h, out_w = edit_image.shape[-2:]
assert edit_mask is not None
if reference_image is not None:
_, H, W = reference_image.shape
_, eH, eW = edit_image.shape
if not True:
# align height with edit_image
scale = eH / H
tH, tW = eH, int(W * scale)
reference_image = T.Resize((tH, tW), interpolation=T.InterpolationMode.BILINEAR, antialias=True)(
reference_image)
else:
# padding
if H >= keep_pixels_rate * eH:
tH = int(eH * keep_pixels_rate)
scale = tH / H
tW = int(W * scale)
reference_image = T.Resize((tH, tW), interpolation=T.InterpolationMode.BILINEAR, antialias=True)(
reference_image)
rH, rW = reference_image.shape[-2:]
delta_w = 0
delta_h = eH - rH
padding = (delta_w // 2, delta_h // 2, delta_w - (delta_w // 2), delta_h - (delta_h // 2))
reference_image = T.Pad(padding, fill=0, padding_mode="constant")(reference_image)
edit_image = torch.cat([reference_image, edit_image], dim=-1)
edit_mask = torch.cat([torch.zeros([1, reference_image.shape[1], reference_image.shape[2]]), edit_mask],
dim=-1)
slice_w = reference_image.shape[-1]
else:
slice_w = 0
H, W = edit_image.shape[-2:]
scale = min(1.0, math.sqrt(self.max_seq_len / ((H / self.d) * (W / self.d))))
rH = int(H * scale) // self.d * self.d
rW = int(W * scale) // self.d * self.d
slice_w = int(slice_w * scale) // self.d * self.d
edit_image = T.Resize((rH, rW), interpolation=T.InterpolationMode.NEAREST_EXACT, antialias=True)(edit_image)
edit_mask = T.Resize((rH, rW), interpolation=T.InterpolationMode.NEAREST_EXACT, antialias=True)(edit_mask)
change_image = edit_image * edit_mask
edit_image = edit_image * (1 - edit_mask)
edit_image = edit_image.unsqueeze(0).permute(0, 2, 3, 1)
change_image = change_image.unsqueeze(0).permute(0, 2, 3, 1)
slice_w = slice_w if slice_w > 30 else slice_w + 30
return edit_image + 0.5, change_image + 0.5, edit_mask, out_h, out_w, slice_w
class AcePlusLoraProcessor:
def __init__(self,
max_aspect_ratio=4,
d=16,
max_seq_len=1024):
self.max_aspect_ratio = max_aspect_ratio
self.max_seq_len = max_seq_len
self.d = d
current_dir = os.path.dirname(os.path.abspath(__file__))
config_path = os.path.join(current_dir, 'config', 'ace_plus_fft_processor.yaml')
self.processor_cfg = self.load_yaml(config_path)
self.task_list = {}
for task in self.processor_cfg['PREPROCESSOR']:
self.task_list[task['TYPE']] = task
self.transforms = T.Compose([
T.ToTensor(),
T.Normalize(mean=[0, 0, 0], std=[1.0, 1.0, 1.0])
])
CATEGORY = 'ComfyUI-ACE_Plus'
@classmethod
def INPUT_TYPES(s):
return {
'required': {
'use_reference': ('BOOLEAN', {'default': True}),
'height': ('INT', {
'default': 1024,
'min': 256,
'max': 1436,
'step': 16
}),
'width': ('INT', {
'default': 1024,
'min': 256,
'max': 1436,
'step': 16
}),
'task_type': (list(s().task_list.keys()),),
'max_seq_length': ('INT', {
'default': 3072,
'min': 1024,
'max': 5120,
'step': 0.01
}),
},
'optional': {
'reference_image': ('IMAGE',),
'edit_image': ('IMAGE',),
'edit_mask': ('MASK',),
}
}
OUTPUT_NODE = True
RETURN_TYPES = ('IMAGE', 'MASK', 'INT', 'INT', 'INT')
RETURN_NAMES = ('IMAGE', 'MASK', 'OUT_H', 'OUT_W', 'SLICE_W')
FUNCTION = 'preprocess'
def load_yaml(self, cfg_file):
with open(cfg_file, 'r') as f:
cfg = yaml.load(f.read(), Loader=yaml.SafeLoader)
return cfg
def image_check(self, image):
if image is None:
return image
# preprocess
H, W = image.shape[1: 3]
image = image.permute(0, 3, 1, 2)
if H / W > self.max_aspect_ratio:
image[0] = T.CenterCrop([int(self.max_aspect_ratio * W), W])(image[0])
elif W / H > self.max_aspect_ratio:
image[0] = T.CenterCrop([H, int(self.max_aspect_ratio * H)])(image[0])
return image[0]
def trans_pil_tensor(self, pil_image):
transform = T.Compose([
T.ToTensor()
])
tensor_image = transform(pil_image)
return tensor_image
def edit_preprocess(self, processor, device, edit_image, edit_mask):
if edit_image is None or processor is None:
return edit_image
if not SCEPTER:
raise ImportError(f'Please install scepter to use edit processor {processor} by '
f'runing "pip install scepter" in the conda env')
processor = Config(cfg_dict=processor, load=False)
processor = ANNOTATORS.build(processor).to(device)
edit_image = Image.fromarray(np.array(edit_image[0] * 255).astype(np.uint8)).convert('RGB')
new_edit_image = processor(np.asarray(edit_image))
del processor
new_edit_image = Image.fromarray(new_edit_image)
if edit_mask is not None:
edit_mask = np.where(edit_mask > 0.5, 1, 0) * 255
edit_mask = Image.fromarray(np.array(edit_mask[0]).astype(np.uint8)).convert('L')
if new_edit_image.size != edit_image.size:
new_edit_image = T.Resize((edit_image.size[1], edit_image.size[0]),
interpolation=T.InterpolationMode.BILINEAR,
antialias=True)(new_edit_image)
image = Image.composite(new_edit_image, edit_image, edit_mask)
return self.trans_pil_tensor(image).unsqueeze(0).permute(0, 2, 3, 1)
def preprocess(self,
reference_image=None,
edit_image=None,
edit_mask=None,
use_reference=True,
task_type=None,
height=1024,
width=1024,
max_seq_length=4096):
self.max_seq_len = max_seq_length
if not use_reference and edit_image is not None:
reference_image = None
if edit_mask is not None and edit_image is not None:
iH, iW = edit_image.shape[1:3]
mH, mW = edit_mask.shape[1:3]
if iH != mH or iW != mW:
edit_mask = torch.ones(edit_image.shape[:3])
if task_type != 'repainting':
repainting_scale = 0.0
else:
repainting_scale = 1.0
if task_type in self.task_list:
edit_image = self.edit_preprocess(self.task_list[task_type]['ANNOTATOR'], 0,
edit_image, edit_mask)
if reference_image is not None:
reference_image = self.image_check(reference_image) - 0.5
if edit_image is not None:
edit_image = self.image_check(edit_image) - 0.5
# for reference generation
if edit_image is None:
edit_image = torch.zeros([3, height, width])
edit_mask = torch.ones([1, height, width])
else:
if edit_mask is None:
_, eH, eW = edit_image.shape
edit_mask = np.ones((eH, eW))
else:
edit_mask = np.asarray(edit_mask)[0]
edit_mask = np.where(edit_mask > 0.5, 1, 0)
edit_mask = edit_mask.astype(
np.float32) if np.any(edit_mask) else np.ones_like(edit_mask).astype(
np.float32)
edit_mask = torch.tensor(edit_mask).unsqueeze(0)
edit_image = edit_image * (1 - edit_mask * repainting_scale)
out_h, out_w = edit_image.shape[-2:]
assert edit_mask is not None
if reference_image is not None:
_, H, W = reference_image.shape
_, eH, eW = edit_image.shape
# align height with edit_image
scale = eH / H
tH, tW = eH, int(W * scale)
reference_image = T.Resize((tH, tW), interpolation=T.InterpolationMode.BILINEAR, antialias=True)(
reference_image)
edit_image = torch.cat([reference_image, edit_image], dim=-1)
edit_mask = torch.cat([torch.zeros([1, reference_image.shape[1], reference_image.shape[2]]), edit_mask],
dim=-1)
slice_w = reference_image.shape[-1]
else:
slice_w = 0
H, W = edit_image.shape[-2:]
scale = min(1.0, math.sqrt(self.max_seq_len * 2 / ((H / self.d) * (W / self.d))))
rH = int(H * scale) // self.d * self.d
rW = int(W * scale) // self.d * self.d
slice_w = int(slice_w * scale) // self.d * self.d
edit_image = T.Resize((rH, rW), interpolation=T.InterpolationMode.NEAREST_EXACT, antialias=True)(edit_image)
edit_mask = T.Resize((rH, rW), interpolation=T.InterpolationMode.NEAREST_EXACT, antialias=True)(edit_mask)
edit_image = edit_image.unsqueeze(0).permute(0, 2, 3, 1)
slice_w = slice_w if slice_w < 30 else slice_w + 30
return edit_image + 0.5, edit_mask, out_h, out_w, slice_w
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