mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-07-27 16:26:39 +00:00
129 lines
4.1 KiB
Python
129 lines
4.1 KiB
Python
import numpy as np
|
|
import torch
|
|
from tqdm.auto import trange
|
|
|
|
import comfy.model_patcher
|
|
import comfy.samplers
|
|
import comfy.utils
|
|
from comfy.k_diffusion.sampling import to_d
|
|
from comfy_api.v3 import 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.ComfyNodeV3):
|
|
UPSCALE_METHODS = ["bislerp", "nearest-exact", "bilinear", "area", "bicubic"]
|
|
|
|
@classmethod
|
|
def define_schema(cls) -> io.SchemaV3:
|
|
return io.SchemaV3(
|
|
node_id="SamplerLCMUpscale_V3",
|
|
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.ComfyNodeV3):
|
|
@classmethod
|
|
def define_schema(cls) -> io.SchemaV3:
|
|
return io.SchemaV3(
|
|
node_id="SamplerEulerCFGpp_V3",
|
|
display_name="SamplerEulerCFG++ _V3",
|
|
category="_for_testing",
|
|
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)
|
|
|
|
|
|
NODES_LIST = [
|
|
SamplerLCMUpscale,
|
|
SamplerEulerCFGpp,
|
|
]
|