nodes/Clybius-ComfyUI-Extra-Samplers
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Replace STRIKE with more consistent SHIDS.

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Clybius
2024-07-21 18:51:45 -05:00
parent c8fab8f468
commit bc2f7264bd
3 changed files with 59 additions and 25 deletions
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__init__.py
+1 -1
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@@ -28,7 +28,7 @@ NODE_CLASS_MAPPINGS = {
"SamplerSupreme": nodes.SamplerSUPREME,
"SamplerSENS": nodes.SamplerSENS,
"SamplerIPNDM_VAPP": nodes.SamplerIPNDM_VAPP,
"SamplerSTRIKE": nodes.SamplerSTRIKE,
"SamplerSHIDS": nodes.SamplerSHIDS,
### Schedulers
"SimpleExponentialScheduler": nodes.SimpleExponentialScheduler,
"KLOptimalScheduler": nodes.KLOptimalScheduler,
extra_samplers.py
+54 -20
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@@ -1462,22 +1462,22 @@ def sampler_ipndm_vapp(model, x, sigmas, extra_args=None, callback=None, disable
return x_next
@torch.no_grad()
def sample_ipndm_vapp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., max_order=4, noise_sampler_type="brownian", noise_sampler=None, pp_guidance=1.0):
def sample_ipndm_vapp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., max_order=4, noise_sampler_type="gaussian", noise_sampler=None, pp_guidance=1.0):
if len(sigmas) <= 1:
return x
noise_sampler, extra_args = check_set_immiscible(x, noise_sampler_type, extra_args)
return sampler_ipndm_vapp(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, max_order=max_order, noise_sampler=noise_sampler if noise_sampler is not None else get_noise_sampler(x, sigmas, noise_sampler_type, noise_sampler, extra_args), pp_guidance=pp_guidance)
@torch.no_grad()
def sampler_STRIKE(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, order=3):
"""Full ancestral sampling with STRIKE (Stochastic/Temporal, Reversible, and Improvised K-Diffusion Experiment) steps."""
def sampler_SHIDS(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, order=16):
"""Full ancestral sampling with SHIDS (Stochastic, Historical, Improvised Sampling) steps."""
extra_args = {} if extra_args is None else extra_args
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
temp = [0]
temp_uncond = [0]
temp_cond = [0]
def post_cfg_function(args):
temp[0] = args["uncond_denoised"]
temp_uncond[0] = args["uncond_denoised"]
temp_cond[0] = args["cond_denoised"]
return args["denoised"]
@@ -1488,33 +1488,67 @@ def sampler_STRIKE(model, x, sigmas, extra_args=None, callback=None, disable=Non
old_uncond, old_uncond_2 = None, None
old_cond, old_cond_2 = None, None
old_dt, old_dt_2 = None, None
buffer_model_cond = []
buffer_model_uncond = []
buffer_model_dt = []
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
d = to_d(x, sigmas[i], temp[0])
d_2 = to_d(x, sigmas[i], temp_cond[0])
d_step = to_d(x, sigmas[i], denoised)
d = to_d(x, sigmas[i], temp_uncond[0])
#d_2 = to_d(x, sigmas[i], temp_cond[0])
# Euler method
dt = sigma_down - sigmas[i]
x = denoised + d * dt - d_2 * dt
if old_uncond is not None and old_cond is not None and order >= 2:
x = x + (old_cond - old_uncond) / (old_dt / dt)
if old_uncond_2 is not None and old_cond_2 is not None and order >= 3:
x = x + (old_cond_2 - old_uncond_2) / (old_dt_2 / old_dt) / (old_dt / dt)
dt = sigma_down - sigmas[i] # Time Difference between now and next step (negative)
x_full = denoised + d_step * sigma_down
x_step = denoised + d * sigma_down
# Project denoised onto a line between (primarily) x_step (cfgpp), and x_full (normal cfg)
ba = x_step - denoised
ca = x_full - denoised
alpha = (ba * ca) / (ba ** 2 + 1e-8)
x = (1 - alpha)*denoised + alpha*x_step
for iteration in range(len(buffer_model_cond) - 1):
#x = x - (buffer_model_uncond[iteration] - buffer_model_cond[iteration]) / (buffer_model_dt[iteration + 1] / buffer_model_dt[iteration])
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
#denoised = model(x, sigmas[i + 1] * s_in, **extra_args)
ba = x - buffer_model_cond[iteration]
ca = x_step - buffer_model_cond[iteration]
alpha = (ba * ca) / (ba ** 2 + 1e-8)
x = (1 - alpha)*buffer_model_cond[iteration] + alpha*x
if len(buffer_model_cond) == max(order - 1, 1):
for k in range(order - 2):
buffer_model_cond[k] = buffer_model_cond[k+1]
buffer_model_uncond[k] = buffer_model_uncond[k+1]
buffer_model_dt[k] = buffer_model_dt[k+1]
buffer_model_cond[-1] = denoised.detach()
buffer_model_uncond[-1] = temp_uncond[0].detach()
buffer_model_dt[-1] = dt.detach()
else:
buffer_model_cond.append(denoised.detach())
buffer_model_uncond.append(temp_uncond[0].detach())
buffer_model_dt.append(dt.detach())
#if old_uncond is not None and old_cond is not None and order >= 2:
# x = x + (old_cond - old_uncond) / (old_dt / dt)
#if old_uncond_2 is not None and old_cond_2 is not None and order >= 3:
# x = x + (old_cond_2 - old_uncond_2) / (old_dt_2 / old_dt) / (old_dt / dt)
if sigmas[i + 1] > 0:
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigmas[i + 1]
old_uncond, old_uncond_2 = temp[0], old_uncond
old_cond, old_cond_2 = temp_cond[0], old_cond
old_dt, old_dt_2 = dt, old_dt
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
#old_uncond, old_uncond_2 = temp[0], old_uncond
#old_cond, old_cond_2 = temp_cond[0], old_cond
#old_dt, old_dt_2 = dt, old_dt
return x
@torch.no_grad()
def sample_STRIKE(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler_type="brownian", noise_sampler=None, order=3):
def sample_SHIDS(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler_type="gaussian", noise_sampler=None, order=16):
if len(sigmas) <= 1:
return x
noise_sampler, extra_args = check_set_immiscible(x, noise_sampler_type, extra_args)
return sampler_STRIKE(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler if noise_sampler is not None else get_noise_sampler(x, sigmas, noise_sampler_type, noise_sampler, extra_args), order=order)
return sampler_SHIDS(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler if noise_sampler is not None else get_noise_sampler(x, sigmas, noise_sampler_type, noise_sampler, extra_args), order=order)
# Add your personal samplers below here, just for formatting purposes ;3
@@ -1530,7 +1564,7 @@ extra_samplers = {
"supreme": sample_supreme,
"sens": sample_sens,
"ipndm_vapp": sample_ipndm_vapp,
"euler_clybtune": sample_euler_clybtune,
"SHIDS": sample_SHIDS,
}
discard_penultimate_sigma_samplers = set((
nodes.py
+4 -4
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@@ -229,15 +229,15 @@ class SamplerIPNDM_VAPP:
sampler = comfy.samplers.ksampler("ipndm_vapp", {"noise_sampler_type": noise_sampler_type, "eta": eta, "s_noise": s_noise, "max_order": max_order, "pp_guidance": pp_guidance})
return (sampler, )
# STRIKE (Stochastic Temporal Reversible Improvised K-Diffusion Experiment)
class SamplerSTRIKE:
# SHIDS (Stochastic Historical Improvised Sampling)
class SamplerSHIDS:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"noise_sampler_type": (get_noise_sampler_names(default="gaussian"), ),
"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01}),
"s_noise": ("FLOAT", {"default": 1, "min": 0.0, "max": 100.0, "step":0.01}),
"order": ("INT", {"default": 3, "min": 1, "max": 3, "step":1}),
"order": ("INT", {"default": 16, "min": 1, "max": 100, "step":1}),
}
}
RETURN_TYPES = ("SAMPLER",)
@@ -246,7 +246,7 @@ class SamplerSTRIKE:
FUNCTION = "get_sampler"
def get_sampler(self, noise_sampler_type, eta, s_noise, order):
sampler = comfy.samplers.ksampler("STRIKE", {"noise_sampler_type": noise_sampler_type, "eta": eta, "s_noise": s_noise, "order": order})
sampler = comfy.samplers.ksampler("SHIDS", {"noise_sampler_type": noise_sampler_type, "eta": eta, "s_noise": s_noise, "order": order})
return (sampler, )
### Noise