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"""
Custom nodes for SDXL in ComfyUI
MIT License
Copyright (c) 2023 Searge
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import warnings
import comfy
from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel
from comfy.model_management import batch_area_memory, get_torch_device, load_models_gpu
import comfy.sample
import comfy.samplers
import comfy.utils
import latent_preview
import torch
from torch.nn.functional import pad
from ..nodes.interposer import VyroLatentInterposer
convert_latent = VyroLatentInterposer().convert
def next_multiple_of(value, factor):
return int(int((value + factor - 1) // factor) * factor)
def get_image_size(image):
if image is None:
return (None, None,)
(_, height, width, _) = image.shape
return (width, height,)
def get_mask_size(mask):
if mask is None:
return (None, None,)
(height, width) = mask.shape
return (width, height,)
def get_latent_size(latent):
if latent is None or "samples" not in latent:
return (None, None,)
samples = latent["samples"]
(_, _, height, width) = samples.shape
return (width, height,)
def get_latent_pixel_size(latent):
(width, height) = get_latent_size(latent)
if width is None or height is None:
return (None, None,)
return (width * 8, height * 8,)
def slerp(factor, input1, input2):
dims = input1.shape
input1 = input1.reshape(dims[0], -1)
input2 = input2.reshape(dims[0], -1)
input1_norm = input1 / torch.norm(input1, dim=1, keepdim=True)
input2_norm = input2 / torch.norm(input2, dim=1, keepdim=True)
input1_norm[input1_norm != input1_norm] = 0.0
input2_norm[input2_norm != input2_norm] = 0.0
omega = torch.acos((input1_norm * input2_norm).sum(1))
sin_omega = torch.sin(omega)
result = ((torch.sin((1.0 - factor) * omega) / sin_omega).unsqueeze(1) * input1
+ (torch.sin(factor * omega) / sin_omega).unsqueeze(1) * input2)
return result.reshape(dims)
def slerp_latents(latent1, latent2, factor):
result = slerp(factor, latent1.clone(), latent2.clone())
return result
def bilateral_blur(inp, kernel_size, sigma_color, sigma_space, border_type='reflect', color_distance_type='l1'):
if isinstance(sigma_color, torch.Tensor):
sigma_color = sigma_color.to(device=inp.device, dtype=inp.dtype).view(-1, 1, 1, 1, 1)
ky, kx = _unpack_2d_ks(kernel_size)
pad_y, pad_x = (ky - 1) // 2, (kx - 1) // 2
padded_input = pad(inp, (pad_x, pad_x, pad_y, pad_y), mode=border_type)
unfolded_input = padded_input.unfold(2, ky, 1).unfold(3, kx, 1).flatten(-2) # (B, C, H, W, Ky x Kx)
diff = unfolded_input - inp.unsqueeze(-1)
if color_distance_type == "l1":
color_distance_sq = diff.abs().sum(1, keepdim=True).square()
elif color_distance_type == "l2":
color_distance_sq = diff.square().sum(1, keepdim=True)
else:
color_distance_sq = diff.abs().sum(1, keepdim=True).square()
color_kernel = (-0.5 / sigma_color ** 2 * color_distance_sq).exp() # (B, 1, H, W, Ky x Kx)
space_kernel = get_gaussian_kernel2d(kernel_size, sigma_space, inp.device, inp.dtype)
space_kernel = space_kernel.view(-1, 1, 1, 1, kx * ky)
kernel = space_kernel * color_kernel
out = (unfolded_input * kernel).sum(-1) / kernel.sum(-1)
return out
def _unpack_2d_ks(kernel_size):
if isinstance(kernel_size, int):
ky = kx = kernel_size
else:
ky, kx = kernel_size
return (int(ky), int(kx))
def get_gaussian_kernel2d(kernel_size, sigma, device, dtype):
if isinstance(sigma, tuple):
sigma = torch.tensor([sigma], device=device, dtype=dtype)
else:
sigma = torch.tensor([[sigma, sigma]], device=device, dtype=dtype)
ksize_y, ksize_x = _unpack_2d_ks(kernel_size)
sigma_y, sigma_x = sigma[:, 0, None], sigma[:, 1, None]
kernel_y = get_gaussian_kernel1d(ksize_y, sigma_y, device, dtype)[..., None]
kernel_x = get_gaussian_kernel1d(ksize_x, sigma_x, device, dtype)[..., None]
return kernel_y * kernel_x.view(-1, 1, ksize_x)
def get_gaussian_kernel1d(kernel_size, sigma, device, dtype):
if isinstance(sigma, float):
sigma = torch.tensor([[sigma]], device=device, dtype=dtype)
batch_size = sigma.shape[0]
x = (torch.arange(kernel_size, device=sigma.device, dtype=sigma.dtype) - kernel_size // 2).expand(batch_size, -1)
if kernel_size % 2 == 0:
x = x + 0.5
gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0)))
return gauss / gauss.sum(-1, keepdim=True)
# --------------------------------------------------------------------------------
class CfgMethods:
INTERPOLATE = "interpolate"
RESCALE = "rescale"
TONEMAP = "tonemap"
# --------------------------------------------------------------------------------
def unet_function(func, params):
cond_or_uncond = params["cond_or_uncond"]
input_x = params["input"]
timestep = params["timestep"]
c = params["c"]
transformer_options = c["transformer_options"]
transformer_options["uc_mask"] = torch.Tensor(cond_or_uncond).to(input_x).float()[:, None, None, None]
# duplicate for each batch
batch_size = input_x.shape[0] / 2
if batch_size > 1:
transformer_options["uc_mask"] = transformer_options["uc_mask"].repeat_interleave(int(batch_size), dim=0)
return func(input_x, timestep, **c)
# --------------------------------------------------------------------------------
def new_unet_forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
x0 = old_unet_forward(self, x, timesteps, context, y, control, transformer_options, **kwargs)
# do filtering here
if "uc_mask" in transformer_options:
uc_mask = transformer_options["uc_mask"]
sharpness = 2.0
alpha = 1.0 - (timesteps / 999.0)[:, None, None, None].clone()
alpha *= 0.001 * sharpness
degraded_x0 = bilateral_blur(x0, (13, 13), 3.0, 3.0) * alpha + x0 * (1.0 - alpha)
x0 = x0 * uc_mask + degraded_x0 * (1.0 - uc_mask)
return x0
old_unet_forward = UNetModel.forward
UNetModel.forward = new_unet_forward
# --------------------------------------------------------------------------------
def sdxl_sample(base_model, refiner_model, noise, base_steps, refiner_steps, cfg, sampler_name, scheduler,
base_positive, base_negative, refiner_positive, refiner_negative, latent_image, batch_inds,
denoise=1.0, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None,
base_callback=None, refiner_callback=None, disable_pbar=False, seed=None, cfg_method=None,
dynamic_base_cfg=0.0, dynamic_refiner_cfg=0.0, refiner_detail_boost=0.0, restart_wrapper=None):
device = get_torch_device()
if noise_mask is not None:
noise_mask = comfy.sample.prepare_mask(noise_mask, noise.shape, device)
steps = base_steps + refiner_steps
def base_cfg_callback(args):
(cond, uncond, cond_scale, timestep) = (args["cond"], args["uncond"], args["cond_scale"], args["timestep"])
dyn_cfg = dynamic_base_cfg
if dyn_cfg < 0.0:
dyn_cfg = -dyn_cfg
ts = 1.0 - float(timestep) / 999.0
else:
ts = float(timestep) / 999.0
if dyn_cfg > 0.0999:
cond_scale = cond_scale * ts + (cond_scale * (1.0 - dyn_cfg) + dyn_cfg) * (1.0 - ts)
return uncond + (cond - uncond) * cond_scale
def base_rescale_cfg(args):
multiplier = dynamic_base_cfg if dynamic_base_cfg >= 0.0 else -dynamic_base_cfg
cond = args["cond"]
uncond = args["uncond"]
cond_scale = args["cond_scale"]
x_cfg = uncond + cond_scale * (cond - uncond)
ro_pos = torch.std(cond, dim=(1, 2, 3), keepdim=True)
ro_cfg = torch.std(x_cfg, dim=(1, 2, 3), keepdim=True)
x_rescaled = x_cfg * (ro_pos / ro_cfg)
x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg
return x_final
def base_tonemap_reinhard(args):
multiplier = dynamic_base_cfg if dynamic_base_cfg >= 0.0 else -dynamic_base_cfg
cond = args["cond"]
uncond = args["uncond"]
cond_scale = args["cond_scale"]
noise_pred = (cond - uncond)
noise_pred_vector_magnitude = (torch.linalg.vector_norm(noise_pred, dim=(1)) + 0.0000000001)[:, None]
noise_pred /= noise_pred_vector_magnitude
mean = torch.mean(noise_pred_vector_magnitude, dim=(1, 2, 3), keepdim=True)
std = torch.std(noise_pred_vector_magnitude, dim=(1, 2, 3), keepdim=True)
top = (std * 3 + mean) * multiplier
noise_pred_vector_magnitude *= (1.0 / top)
new_magnitude = noise_pred_vector_magnitude / (noise_pred_vector_magnitude + 1.0)
new_magnitude *= top
return uncond + noise_pred * new_magnitude * cond_scale
base_model = base_model.clone()
base_model.set_model_unet_function_wrapper(unet_function)
if cfg_method is not None:
if cfg_method == CfgMethods.INTERPOLATE:
base_model.set_model_sampler_cfg_function(base_cfg_callback)
elif cfg_method == CfgMethods.RESCALE and dynamic_base_cfg > 0.0:
base_model.set_model_sampler_cfg_function(base_rescale_cfg)
elif cfg_method == CfgMethods.TONEMAP and dynamic_base_cfg > 0.0:
base_model.set_model_sampler_cfg_function(base_tonemap_reinhard)
# base_models = comfy.sample.get_additional_models(base_positive, base_negative)
# load_models_gpu([base_model] + base_models, batch_area_memory(noise.shape[0] * noise.shape[2] * noise.shape[3]))
base_models, inference_memory = comfy.sample.get_additional_models(base_positive, base_negative,
base_model.model_dtype())
memory_required = batch_area_memory(noise.shape[0] * noise.shape[2] * noise.shape[3]) + inference_memory
load_models_gpu([base_model] + base_models, memory_required)
real_base_model = base_model.model
original_latent = latent_image
noise = noise.to(device)
latent_image = latent_image.to(device)
pos_base_copy = comfy.sample.broadcast_cond(base_positive, noise.shape[0], device)
neg_base_copy = comfy.sample.broadcast_cond(base_negative, noise.shape[0], device)
base_sampler = comfy.samplers.KSampler(real_base_model, steps=steps, device=device, sampler=sampler_name,
scheduler=scheduler, denoise=denoise, model_options=base_model.model_options)
base_samples = base_sampler.sample(noise, pos_base_copy, neg_base_copy, cfg=cfg, latent_image=latent_image,
start_step=start_step, last_step=base_steps, force_full_denoise=False,
denoise_mask=noise_mask, sigmas=sigmas, callback=base_callback,
disable_pbar=disable_pbar, seed=seed)
comfy.sample.cleanup_additional_models(base_models)
noise = torch.zeros(base_samples.size(), dtype=base_samples.dtype, layout=base_samples.layout, device=device)
if refiner_steps < 1:
return base_samples.cpu()
if refiner_detail_boost > 0.0:
new_noise = comfy.sample.prepare_noise(original_latent, seed + 1, batch_inds).to(device)
new_noise /= real_base_model.latent_format.scale_factor
factor = base_sampler.sigmas[-refiner_steps - 1]
new_noise = new_noise * factor
noised_samples = base_samples + new_noise
base_samples = slerp_latents(base_samples, noised_samples, refiner_detail_boost)
if noise_mask is not None:
latent_from_base = base_samples * noise_mask + latent_image * (1.0 - noise_mask)
else:
latent_from_base = base_samples
# latent_from_base = convert_latent(latent_from_base,'xl','v1')
# latent_from_base.to(base_samples.device)
def refiner_cfg_callback(args):
(cond, uncond, cond_scale, timestep) = (args["cond"], args["uncond"], args["cond_scale"], args["timestep"])
dyn_cfg = dynamic_refiner_cfg
if dyn_cfg < 0.0:
dyn_cfg = -dyn_cfg
ts = 1.0 - float(timestep) / 999.0
else:
ts = float(timestep) / 999.0
if dyn_cfg > 0.0999:
cond_scale = cond_scale * ts + (cond_scale * (1.0 - dyn_cfg) + dyn_cfg) * (1.0 - ts)
return uncond + (cond - uncond) * cond_scale
def refiner_rescale_cfg(args):
multiplier = dynamic_refiner_cfg if dynamic_refiner_cfg >= 0.0 else -dynamic_refiner_cfg
cond = args["cond"]
uncond = args["uncond"]
cond_scale = args["cond_scale"]
x_cfg = uncond + cond_scale * (cond - uncond)
ro_pos = torch.std(cond, dim=(1, 2, 3), keepdim=True)
ro_cfg = torch.std(x_cfg, dim=(1, 2, 3), keepdim=True)
x_rescaled = x_cfg * (ro_pos / ro_cfg)
return multiplier * x_rescaled + (1.0 - multiplier) * x_cfg
def refiner_tonemap_reinhard(args):
multiplier = dynamic_refiner_cfg if dynamic_refiner_cfg >= 0.0 else -dynamic_refiner_cfg
cond = args["cond"]
uncond = args["uncond"]
cond_scale = args["cond_scale"]
noise_pred = (cond - uncond)
noise_pred_vector_magnitude = (torch.linalg.vector_norm(noise_pred, dim=(1)) + 0.0000000001)[:, None]
noise_pred /= noise_pred_vector_magnitude
mean = torch.mean(noise_pred_vector_magnitude, dim=(1, 2, 3), keepdim=True)
std = torch.std(noise_pred_vector_magnitude, dim=(1, 2, 3), keepdim=True)
top = (std * 3 + mean) * multiplier
noise_pred_vector_magnitude *= (1.0 / top)
new_magnitude = noise_pred_vector_magnitude / (noise_pred_vector_magnitude + 1.0)
new_magnitude *= top
return uncond + noise_pred * new_magnitude * cond_scale
refiner_model = refiner_model.clone()
refiner_model.set_model_unet_function_wrapper(unet_function)
if cfg_method is not None:
if cfg_method == CfgMethods.INTERPOLATE:
refiner_model.set_model_sampler_cfg_function(refiner_cfg_callback)
elif cfg_method == CfgMethods.RESCALE and dynamic_refiner_cfg > 0.0:
refiner_model.set_model_sampler_cfg_function(refiner_rescale_cfg)
elif cfg_method == CfgMethods.TONEMAP and dynamic_refiner_cfg > 0.0:
refiner_model.set_model_sampler_cfg_function(refiner_tonemap_reinhard)
# refiner_models = comfy.sample.get_additional_models(base_positive, base_negative)
# load_models_gpu([refiner_model] + refiner_models, batch_area_memory(noise.shape[0] * noise.shape[2] * noise.shape[3]))
refiner_models, inference_memory = comfy.sample.get_additional_models(refiner_positive, refiner_negative,
refiner_model.model_dtype())
memory_required = batch_area_memory(noise.shape[0] * noise.shape[2] * noise.shape[3]) + inference_memory
load_models_gpu([refiner_model] + refiner_models, memory_required)
real_refiner_model = refiner_model.model
pos_refiner_copy = comfy.sample.broadcast_cond(refiner_positive, noise.shape[0], device)
neg_refiner_copy = comfy.sample.broadcast_cond(refiner_negative, noise.shape[0], device)
if restart_wrapper is not None:
restart_wrapper.__class__.refiner_stage = True
refiner_sampler = comfy.samplers.KSampler(real_refiner_model, steps=steps, device=device, sampler=sampler_name,
scheduler=scheduler, denoise=denoise,
model_options=refiner_model.model_options)
refiner_samples = refiner_sampler.sample(noise, pos_refiner_copy, neg_refiner_copy, cfg=cfg,
latent_image=latent_from_base, start_step=base_steps, last_step=last_step,
force_full_denoise=force_full_denoise,
denoise_mask=noise_mask, sigmas=sigmas, callback=refiner_callback,
disable_pbar=disable_pbar, seed=seed)
refiner_samples = refiner_samples.cpu()
comfy.sample.cleanup_additional_models(refiner_models)
return refiner_samples
# --------------------------------------------------------------------------------
def sdxl_ksampler(base_model, refiner_model, seed, base_steps, refiner_steps, cfg, sampler_name, scheduler,
base_positive, base_negative, refiner_positive, refiner_negative, latent, denoise=1.0,
disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, cfg_method=None,
dynamic_base_cfg=0.0, dynamic_refiner_cfg=0.0, refiner_detail_boost=0.0, restart_wrapper=None):
# print(f"\n\nBase Model Type: {type(base_model.model.diffusion_model)}")
device = get_torch_device()
latent_image = latent["samples"]
batch_inds = None
if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
else:
batch_inds = latent["batch_index"] if "batch_index" in latent else None
noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
noise_mask = None
if "noise_mask" in latent:
noise_mask = latent["noise_mask"]
preview_format = "JPEG"
if preview_format not in ["JPEG", "PNG"]:
preview_format = "JPEG"
base_previewer = latent_preview.get_previewer(device, base_model.model.latent_format)
refiner_previewer = None
if refiner_model is not None:
refiner_previewer = latent_preview.get_previewer(device, refiner_model.model.latent_format)
steps = base_steps + refiner_steps
pbar = comfy.utils.ProgressBar(steps)
def base_callback(step, x0, x, total_steps):
preview_bytes = None
if base_previewer:
preview_bytes = base_previewer.decode_latent_to_preview_image(preview_format, x0)
pbar.update_absolute(step + 1, total_steps, preview_bytes)
def refiner_callback(step, x0, x, total_steps):
preview_bytes = None
if refiner_previewer:
preview_bytes = refiner_previewer.decode_latent_to_preview_image(preview_format, x0)
pbar.update_absolute(step + 1, total_steps, preview_bytes)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
samples = sdxl_sample(base_model, refiner_model, noise, base_steps, refiner_steps, cfg, sampler_name, scheduler,
base_positive, base_negative, refiner_positive, refiner_negative, latent_image,
batch_inds, denoise=denoise, start_step=start_step, last_step=last_step,
force_full_denoise=force_full_denoise, noise_mask=noise_mask,
base_callback=base_callback, refiner_callback=refiner_callback, seed=seed,
dynamic_base_cfg=dynamic_base_cfg, dynamic_refiner_cfg=dynamic_refiner_cfg,
cfg_method=cfg_method, refiner_detail_boost=refiner_detail_boost,restart_wrapper=restart_wrapper)
out = latent.copy()
out["samples"] = samples
return (out,)
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