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import numpy as np
import torch
from tqdm.auto import trange
from typing_extensions import override
import comfy.model_patcher
import comfy.samplers
import comfy.utils
from comfy.k_diffusion.sampling import to_d
from comfy_api.latest import ComfyExtension, io
@torch.no_grad()
def sample_lcm_upscale(model, x, sigmas, extra_args=None, callback=None, disable=None, total_upscale=2.0, upscale_method="bislerp", upscale_steps=None):
extra_args = {} if extra_args is None else extra_args
if upscale_steps is None:
upscale_steps = max(len(sigmas) // 2 + 1, 2)
else:
upscale_steps += 1
upscale_steps = min(upscale_steps, len(sigmas) + 1)
upscales = np.linspace(1.0, total_upscale, upscale_steps)[1:]
orig_shape = x.size()
s_in = x.new_ones([x.shape[0]])
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
x = denoised
if i < len(upscales):
x = comfy.utils.common_upscale(x, round(orig_shape[-1] * upscales[i]), round(orig_shape[-2] * upscales[i]), upscale_method, "disabled")
if sigmas[i + 1] > 0:
x += sigmas[i + 1] * torch.randn_like(x)
return x
class SamplerLCMUpscale(io.ComfyNode):
UPSCALE_METHODS = ["bislerp", "nearest-exact", "bilinear", "area", "bicubic"]
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="SamplerLCMUpscale",
category="sampling/custom_sampling/samplers",
inputs=[
io.Float.Input("scale_ratio", default=1.0, min=0.1, max=20.0, step=0.01),
io.Int.Input("scale_steps", default=-1, min=-1, max=1000, step=1),
io.Combo.Input("upscale_method", options=cls.UPSCALE_METHODS),
],
outputs=[io.Sampler.Output()],
)
@classmethod
def execute(cls, scale_ratio, scale_steps, upscale_method) -> io.NodeOutput:
if scale_steps < 0:
scale_steps = None
sampler = comfy.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method})
return io.NodeOutput(sampler)
@torch.no_grad()
def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
extra_args = {} if extra_args is None else extra_args
temp = [0]
def post_cfg_function(args):
temp[0] = args["uncond_denoised"]
return args["denoised"]
model_options = extra_args.get("model_options", {}).copy()
extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
s_in = x.new_ones([x.shape[0]])
for i in trange(len(sigmas) - 1, disable=disable):
sigma_hat = sigmas[i]
denoised = model(x, sigma_hat * s_in, **extra_args)
d = to_d(x - denoised + temp[0], sigmas[i], denoised)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
dt = sigmas[i + 1] - sigma_hat
x = x + d * dt
return x
class SamplerEulerCFGpp(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="SamplerEulerCFGpp",
display_name="SamplerEulerCFG++",
category="_for_testing", # "sampling/custom_sampling/samplers"
inputs=[
io.Combo.Input("version", options=["regular", "alternative"]),
],
outputs=[io.Sampler.Output()],
is_experimental=True,
)
@classmethod
def execute(cls, version) -> io.NodeOutput:
if version == "alternative":
sampler = comfy.samplers.KSAMPLER(sample_euler_pp)
else:
sampler = comfy.samplers.ksampler("euler_cfg_pp")
return io.NodeOutput(sampler)
class AdvancedSamplersExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
SamplerLCMUpscale,
SamplerEulerCFGpp,
]
async def comfy_entrypoint() -> AdvancedSamplersExtension:
return AdvancedSamplersExtension()
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