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custom_sampling.py
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custom_sampling.py
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import torch
from tqdm import trange
import os, sys
sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
import comfy.sample
from comfy.sampler import Sampler, KSamplerX0Inpaint, calculate_sigmas_scheduler, sampler_object
import latent_preview
from .k_diffusion import sampling as k_diffusion_sampling
SCHEDULER_NAMES = ["karras"]
SAMPLER_NAMES = ["dpm_2"]
def common_ksampler_extra_denoise(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
latent_image = latent["samples"]
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"]
callback = latent_preview.prepare_callback(model, steps)
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
samples = sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
out = latent.copy()
out["samples"] = samples
return (out, )
def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
real_model, positive_copy, negative_copy, noise_mask, models = comfy.sample.prepare_sampling(model, noise.shape, positive, negative, noise_mask)
noise = noise.to(model.load_device)
latent_image = latent_image.to(model.load_device)
sampler = KSampler(real_model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
samples = samples.to(comfy.model_management.intermediate_device())
comfy.sample.cleanup_additional_models(models)
comfy.sample.cleanup_additional_models(set(comfy.sample.get_models_from_cond(positive_copy, "control") + comfy.sample.get_models_from_cond(negative_copy, "control")))
return samples
class KSAMPLER(Sampler):
def __init__(self, sampler_function, extra_options={}, inpaint_options={}):
self.sampler_function = sampler_function
self.extra_options = extra_options
self.inpaint_options = inpaint_options
def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
extra_args["denoise_mask"] = denoise_mask
model_k = KSamplerX0Inpaint(model_wrap)
model_k.latent_image = latent_image
if self.inpaint_options.get("random", False): #TODO: Should this be the default?
generator = torch.manual_seed(extra_args.get("seed", 41) + 1)
model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device)
else:
model_k.noise = noise
if self.max_denoise(model_wrap, sigmas):
noise = noise * torch.sqrt(1.0 + sigmas[0] ** 2.0)
else:
noise = noise * sigmas[0]
k_callback = None
total_steps = len(sigmas) - 1
if callback is not None:
k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
if latent_image is not None:
noise += latent_image
samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
return samples
class KSampler:
SCHEDULERS = SCHEDULER_NAMES
SAMPLERS = SAMPLER_NAMES
def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}):
self.model = model
self.device = device
if scheduler not in self.SCHEDULERS:
scheduler = self.SCHEDULERS[0]
if sampler not in self.SAMPLERS:
sampler = self.SAMPLERS[0]
self.scheduler = scheduler
self.sampler = sampler
self.set_steps(steps, denoise)
self.denoise = denoise
self.model_options = model_options
def calculate_sigmas(self, steps):
sigmas = None
discard_penultimate_sigma = False
if self.sampler in ['dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2']:
steps += 1
discard_penultimate_sigma = True
sigmas = calculate_sigmas_scheduler(self.model, self.scheduler, steps)
if discard_penultimate_sigma:
sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
return sigmas
def set_steps(self, steps, denoise=None):
self.steps = steps
if denoise is None or denoise > 0.9999:
self.sigmas = self.calculate_sigmas(steps).to(self.device)
else:
new_steps = int(steps/denoise)
sigmas = self.calculate_sigmas(new_steps).to(self.device)
self.sigmas = sigmas[-(steps + 1):]
def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
if sigmas is None:
sigmas = self.sigmas
if last_step is not None and last_step < (len(sigmas) - 1):
sigmas = sigmas[:last_step + 1]
if force_full_denoise:
sigmas[-1] = 0
if start_step is not None:
if start_step < (len(sigmas) - 1):
sigmas = sigmas[start_step:]
else:
if latent_image is not None:
return latent_image
else:
return torch.zeros_like(noise)
sampler = sampler_object(self.sampler)
return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
def ksampler(sampler_name, extra_options={}, inpaint_options={}):
if sampler_name == "dpm_fast":
def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable):
sigma_min = sigmas[-1]
if sigma_min == 0:
sigma_min = sigmas[-2]
total_steps = len(sigmas) - 1
return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable)
sampler_function = dpm_fast_function
elif sampler_name == "dpm_adaptive":
def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable):
sigma_min = sigmas[-1]
if sigma_min == 0:
sigma_min = sigmas[-2]
return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable)
sampler_function = dpm_adaptive_function
else:
sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))
return KSAMPLER(sampler_function, extra_options, inpaint_options)