You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
285 lines
11 KiB
285 lines
11 KiB
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}), |
|
} |
|
} |
|
RETURN_TYPES = ("SIGMAS",) |
|
CATEGORY = "sampling/custom_sampling/schedulers" |
|
|
|
FUNCTION = "get_sigmas" |
|
|
|
def get_sigmas(self, model, scheduler, steps): |
|
sigmas = comfy.samplers.calculate_sigmas_scheduler(model.model, scheduler, steps).cpu() |
|
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}), |
|
} |
|
} |
|
RETURN_TYPES = ("SIGMAS",) |
|
CATEGORY = "sampling/custom_sampling/schedulers" |
|
|
|
FUNCTION = "get_sigmas" |
|
|
|
def get_sigmas(self, model, steps): |
|
timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[:steps] |
|
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_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 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, |
|
"SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE, |
|
"SamplerDPMPP_SDE": SamplerDPMPP_SDE, |
|
"SplitSigmas": SplitSigmas, |
|
"FlipSigmas": FlipSigmas, |
|
}
|
|
|