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Add gpu variations of the sde samplers that are less deterministic

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but faster.
This commit is contained in:
comfyanonymous 2023-07-05 01:37:34 -04:00
parent f81b192944
commit e57cba4c61
2 changed files with 33 additions and 8 deletions
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37
comfy/k_diffusion/sampling.py
View File

@ -66,6 +66,9 @@ class BatchedBrownianTree:
"""A wrapper around torchsde.BrownianTree that enables batches of entropy."""
def __init__(self, x, t0, t1, seed=None, **kwargs):
self.cpu_tree = True
if "cpu" in kwargs:
self.cpu_tree = kwargs.pop("cpu")
t0, t1, self.sign = self.sort(t0, t1)
w0 = kwargs.get('w0', torch.zeros_like(x))
if seed is None:
@ -77,7 +80,10 @@ class BatchedBrownianTree:
except TypeError:
seed = [seed]
self.batched = False
self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]
if self.cpu_tree:
self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]
else:
self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
@staticmethod
def sort(a, b):
@ -85,7 +91,11 @@ class BatchedBrownianTree:
def __call__(self, t0, t1):
t0, t1, sign = self.sort(t0, t1)
w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign)
if self.cpu_tree:
w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign)
else:
w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
return w if self.batched else w[0]
@ -104,10 +114,10 @@ class BrownianTreeNoiseSampler:
internal timestep.
"""
def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x):
def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False):
self.transform = transform
t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max))
self.tree = BatchedBrownianTree(x, t0, t1, seed)
self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu)
def __call__(self, sigma, sigma_next):
t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next))
@ -544,7 +554,7 @@ def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=N
"""DPM-Solver++ (stochastic)."""
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
seed = extra_args.get("seed", None)
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed) if noise_sampler is None else noise_sampler
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
sigma_fn = lambda t: t.neg().exp()
@ -616,7 +626,7 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
seed = extra_args.get("seed", None)
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed) if noise_sampler is None else noise_sampler
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
@ -651,3 +661,18 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
old_denoised = denoised
h_last = h
return x
@torch.no_grad()
def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
@torch.no_grad()
def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r)

4
comfy/samplers.py
View File

@ -483,8 +483,8 @@ def encode_adm(model, conds, batch_size, width, height, device, prompt_type):
class KSampler:
SCHEDULERS = ["normal", "karras", "exponential", "simple", "ddim_uniform"]
SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde",
"dpmpp_2m", "dpmpp_2m_sde", "ddim", "uni_pc", "uni_pc_bh2"]
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "ddim", "uni_pc", "uni_pc_bh2"]
def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}):
self.model = model