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import torch
from tqdm import trange
from comfy.samplers import KSAMPLER
def generate_eta_values(steps, start_time, end_time, eta, eta_trend):
eta_values = [0] * steps
if eta_trend == 'constant':
for i in range(start_time, end_time):
eta_values[i] = eta
elif eta_trend == 'linear_increase':
for i in range(start_time, end_time):
progress = (i - start_time) / (end_time - start_time - 1)
eta_values[i] = eta * progress
elif eta_trend == 'linear_decrease':
for i in range(start_time, end_time):
progress = 1 - (i - start_time) / (end_time - start_time - 1)
eta_values[i] = eta * progress
return eta_values
def get_sample_forward(gamma, start_step, end_step, gamma_trend, seed):
# Controlled Forward ODE (Algorithm 1)
@torch.no_grad()
def sample_forward(model, y0, sigmas, extra_args=None, callback=None, disable=None):
generator = torch.Generator()
generator.manual_seed(seed)
extra_args = {} if extra_args is None else extra_args
Y = y0.clone()
y1 = torch.randn(Y.shape, generator=generator).to(y0.device)
N = len(sigmas)-1
s_in = y0.new_ones([y0.shape[0]])
gamma_values = generate_eta_values(N, start_step, end_step, gamma, gamma_trend)
for i in trange(N, disable=disable):
# t_i = model.inner_model.inner_model.model_sampling.timestep(sigmas[i]) - old code
t_i = sigmas[i] / max(sigmas) # - new code
# 6. Unconditional Vector field uti(Yti) = u(Yti, ti, Φ(“”); φ)
unconditional_vector_field = model(Y, s_in * sigmas[i], **extra_args) # this implementation takes sigma instead of timestep
# 7.Conditional Vector field uti(Yti|y1) = (y1−Yti)/1−ti
conditional_vector_field = (y1-Y)/(1-t_i)
# 8. Controlled Vector field ti(Yti) = uti(Yti) + γ (uti(Yti|y1) − uti(Yti))
controlled_vector_field = unconditional_vector_field + gamma_values[i] * (conditional_vector_field - unconditional_vector_field)
# 9. Next state Yti+1 = Yti + ˆuti(Yti) (σ(ti+1) − σ(ti))
Y = Y + controlled_vector_field * (sigmas[i+1] - sigmas[i])
if callback is not None:
callback({'x': Y, 'denoised': Y, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i]})
return Y
return sample_forward
def get_sample_reverse(latent_image, eta, start_time, end_time, eta_trend):
# Controlled Reverse ODE (Algorithm 2)
@torch.no_grad()
def sample_reverse(model, y1, sigmas, extra_args=None, callback=None, disable=None):
extra_args = {} if extra_args is None else extra_args
X = y1.clone()
N = len(sigmas)-1
y0 = latent_image.clone().to(y1.device)
s_in = y0.new_ones([y0.shape[0]])
eta_values = generate_eta_values(N, start_time, end_time, eta, eta_trend)
for i in trange(N, disable=disable):
# t_i = 1-model.inner_model.inner_model.model_sampling.timestep(sigmas[i]) # TODO: figure out which one to use
t_i = i/N # Empiracally better results
sigma = sigmas[i]
# 5. Unconditional Vector field uti(Xti) = -u(Xti, 1-ti, Φ(“prompt”); φ)
unconditional_vector_field = -model(X, sigma*s_in, **extra_args) # this implementation takes sigma instead of timestep
# 6.Conditional Vector field uti(Xti|y0) = (y0−Xti)/(1−ti)
conditional_vector_field = (y0-X)/(1-t_i)
# 7. Controlled Vector field ti(Yti) = uti(Yti) + γ (uti(Yti|y1) − uti(Yti))
controlled_vector_field = unconditional_vector_field + eta_values[i] * (conditional_vector_field - unconditional_vector_field)
# 8. Next state Yti+1 = Yti + ˆuti(Yti) (σ(ti+1) − σ(ti))
X = X + controlled_vector_field * (sigmas[i] - sigmas[i+1])
if callback is not None:
callback({'x': X, 'denoised': X, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i]})
return X
return sample_reverse
class HYForwardODESamplerNode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"gamma": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step": 0.01}),
"start_step": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
"end_step": ("INT", {"default": 5, "min": 0, "max": 1000, "step": 1}),
"gamma_trend": (['constant', 'linear_increase', 'linear_decrease'],)
}, "optional": {
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff }),
}}
RETURN_TYPES = ("SAMPLER",)
FUNCTION = "build"
CATEGORY = "hunyuanloom"
def build(self, gamma, start_step, end_step, gamma_trend, seed=0):
sampler = KSAMPLER(get_sample_forward(gamma, start_step, end_step, gamma_trend, seed))
return (sampler, )
class HYReverseODESamplerNode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL",),
"latent_image": ("LATENT",),
"eta": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 100.0, "step": 0.01}),
"start_step": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
"end_step": ("INT", {"default": 5, "min": 0, "max": 1000, "step": 1}),
}, "optional": {
"eta_trend": (['constant', 'linear_increase', 'linear_decrease'],)
}}
RETURN_TYPES = ("SAMPLER",)
FUNCTION = "build"
CATEGORY = "hunyuanloom"
def build(self, model, latent_image, eta, start_step, end_step, eta_trend='constant'):
process_latent_in = model.get_model_object("process_latent_in")
latent_image = process_latent_in(latent_image['samples'])
sampler = KSAMPLER(get_sample_reverse(latent_image, eta, start_step, end_step, eta_trend))
return (sampler, )
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