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ComfyUI/comfy_extras/nodes_custom_sampler.py

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import comfy.samplers
import comfy.sample
from comfy.k_diffusion import sampling as k_diffusion_sampling
import latent_preview
import torch
import comfy.utils
class BasicScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"scheduler": (comfy.samplers.SCHEDULER_NAMES, ),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers"
FUNCTION = "get_sigmas"
def get_sigmas(self, model, scheduler, steps, denoise):
total_steps = steps
if denoise < 1.0:
total_steps = int(steps/denoise)
comfy.model_management.load_models_gpu([model])
sigmas = comfy.samplers.calculate_sigmas_scheduler(model.model, scheduler, total_steps).cpu()
sigmas = sigmas[-(steps + 1):]
return (sigmas, )
class KarrasScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
"sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
"rho": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers"
FUNCTION = "get_sigmas"
def get_sigmas(self, steps, sigma_max, sigma_min, rho):
sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)
return (sigmas, )
class ExponentialScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
"sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers"
FUNCTION = "get_sigmas"
def get_sigmas(self, steps, sigma_max, sigma_min):
sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max)
return (sigmas, )
class PolyexponentialScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
"sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
"rho": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers"
FUNCTION = "get_sigmas"
def get_sigmas(self, steps, sigma_max, sigma_min, rho):
sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)
return (sigmas, )
class SDTurboScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"steps": ("INT", {"default": 1, "min": 1, "max": 10}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers"
FUNCTION = "get_sigmas"
def get_sigmas(self, model, steps, denoise):
start_step = 10 - int(10 * denoise)
timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps]
comfy.model_management.load_models_gpu([model])
sigmas = model.model.model_sampling.sigma(timesteps)
sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
return (sigmas, )
class VPScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"beta_d": ("FLOAT", {"default": 19.9, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), #TODO: fix default values
"beta_min": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
"eps_s": ("FLOAT", {"default": 0.001, "min": 0.0, "max": 1.0, "step":0.0001, "round": False}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers"
FUNCTION = "get_sigmas"
def get_sigmas(self, steps, beta_d, beta_min, eps_s):
sigmas = k_diffusion_sampling.get_sigmas_vp(n=steps, beta_d=beta_d, beta_min=beta_min, eps_s=eps_s)
return (sigmas, )
class SplitSigmas:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"sigmas": ("SIGMAS", ),
"step": ("INT", {"default": 0, "min": 0, "max": 10000}),
}
}
RETURN_TYPES = ("SIGMAS","SIGMAS")
CATEGORY = "sampling/custom_sampling/sigmas"
FUNCTION = "get_sigmas"
def get_sigmas(self, sigmas, step):
sigmas1 = sigmas[:step + 1]
sigmas2 = sigmas[step:]
return (sigmas1, sigmas2)
class FlipSigmas:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"sigmas": ("SIGMAS", ),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/sigmas"
FUNCTION = "get_sigmas"
def get_sigmas(self, sigmas):
sigmas = sigmas.flip(0)
if sigmas[0] == 0:
sigmas[0] = 0.0001
return (sigmas,)
class KSamplerSelect:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"sampler_name": (comfy.samplers.SAMPLER_NAMES, ),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, sampler_name):
sampler = comfy.samplers.sampler_object(sampler_name)
return (sampler, )
class SamplerDPMPP_3M_SDE:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"noise_device": (['gpu', 'cpu'], ),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, eta, s_noise, noise_device):
if noise_device == 'cpu':
sampler_name = "dpmpp_3m_sde"
else:
sampler_name = "dpmpp_3m_sde_gpu"
sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise})
return (sampler, )
class SamplerDPMPP_2M_SDE:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"solver_type": (['midpoint', 'heun'], ),
"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"noise_device": (['gpu', 'cpu'], ),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, solver_type, eta, s_noise, noise_device):
if noise_device == 'cpu':
sampler_name = "dpmpp_2m_sde"
else:
sampler_name = "dpmpp_2m_sde_gpu"
sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type})
return (sampler, )
class SamplerDPMPP_SDE:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"r": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"noise_device": (['gpu', 'cpu'], ),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, eta, s_noise, r, noise_device):
if noise_device == 'cpu':
sampler_name = "dpmpp_sde"
else:
sampler_name = "dpmpp_sde_gpu"
sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})
return (sampler, )
class SamplerEulerAncestral:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, eta, s_noise):
sampler = comfy.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise})
return (sampler, )
class SamplerLMS:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"order": ("INT", {"default": 4, "min": 1, "max": 100}),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, order):
sampler = comfy.samplers.ksampler("lms", {"order": order})
return (sampler, )
class SamplerDPMAdaptative:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"order": ("INT", {"default": 3, "min": 2, "max": 3}),
"rtol": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"atol": ("FLOAT", {"default": 0.0078, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"h_init": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"pcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"icoeff": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"dcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"accept_safety": ("FLOAT", {"default": 0.81, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"eta": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise):
sampler = comfy.samplers.ksampler("dpm_adaptive", {"order": order, "rtol": rtol, "atol": atol, "h_init": h_init, "pcoeff": pcoeff,
"icoeff": icoeff, "dcoeff": dcoeff, "accept_safety": accept_safety, "eta": eta,
"s_noise":s_noise })
return (sampler, )
class SamplerCustom:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"add_noise": ("BOOLEAN", {"default": True}),
"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"sampler": ("SAMPLER", ),
"sigmas": ("SIGMAS", ),
"latent_image": ("LATENT", ),
}
}
RETURN_TYPES = ("LATENT","LATENT")
RETURN_NAMES = ("output", "denoised_output")
FUNCTION = "sample"
CATEGORY = "sampling/custom_sampling"
def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image):
latent = latent_image
latent_image = latent["samples"]
if not add_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, noise_seed, batch_inds)
noise_mask = None
if "noise_mask" in latent:
noise_mask = latent["noise_mask"]
x0_output = {}
callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
out = latent.copy()
out["samples"] = samples
if "x0" in x0_output:
out_denoised = latent.copy()
out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
else:
out_denoised = out
return (out, out_denoised)
NODE_CLASS_MAPPINGS = {
"SamplerCustom": SamplerCustom,
"BasicScheduler": BasicScheduler,
"KarrasScheduler": KarrasScheduler,
"ExponentialScheduler": ExponentialScheduler,
"PolyexponentialScheduler": PolyexponentialScheduler,
"VPScheduler": VPScheduler,
"SDTurboScheduler": SDTurboScheduler,
"KSamplerSelect": KSamplerSelect,
"SamplerEulerAncestral": SamplerEulerAncestral,
"SamplerLMS": SamplerLMS,
"SamplerDPMPP_3M_SDE": SamplerDPMPP_3M_SDE,
"SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE,
"SamplerDPMPP_SDE": SamplerDPMPP_SDE,
"SamplerDPMAdaptative": SamplerDPMAdaptative,
"SplitSigmas": SplitSigmas,
"FlipSigmas": FlipSigmas,
}
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