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2.1.782
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lllyasviel
2023-11-11 01:43:01 -08:00
parent a9bb1079cf
commit 4fe08161a5
48 changed files with 1041 additions and 2639 deletions
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backend/headless/fcbh/samplers.py
+55 -92
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@@ -1,11 +1,8 @@
from .k_diffusion import sampling as k_diffusion_sampling
from .k_diffusion import external as k_diffusion_external
from .extra_samplers import uni_pc
import torch
import enum
from fcbh import model_management
from .ldm.models.diffusion.ddim import DDIMSampler
from .ldm.modules.diffusionmodules.util import make_ddim_timesteps
import math
from fcbh import model_base
import fcbh.utils
@@ -13,7 +10,7 @@ import fcbh.conds
#The main sampling function shared by all the samplers
#Returns predicted noise
#Returns denoised
def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
def get_area_and_mult(conds, x_in, timestep_in):
area = (x_in.shape[2], x_in.shape[3], 0, 0)
@@ -139,10 +136,10 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, mod
def calc_cond_uncond_batch(model_function, cond, uncond, x_in, timestep, max_total_area, model_options):
out_cond = torch.zeros_like(x_in)
out_count = torch.ones_like(x_in)/100000.0
out_count = torch.ones_like(x_in) * 1e-37
out_uncond = torch.zeros_like(x_in)
out_uncond_count = torch.ones_like(x_in)/100000.0
out_uncond_count = torch.ones_like(x_in) * 1e-37
COND = 0
UNCOND = 1
@@ -242,7 +239,6 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, mod
del out_count
out_uncond /= out_uncond_count
del out_uncond_count
return out_cond, out_uncond
@@ -252,29 +248,20 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, mod
cond, uncond = calc_cond_uncond_batch(model_function, cond, uncond, x, timestep, max_total_area, model_options)
if "sampler_cfg_function" in model_options:
args = {"cond": cond, "uncond": uncond, "cond_scale": cond_scale, "timestep": timestep}
return model_options["sampler_cfg_function"](args)
args = {"cond": x - cond, "uncond": x - uncond, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep}
return x - model_options["sampler_cfg_function"](args)
else:
return uncond + (cond - uncond) * cond_scale
class CompVisVDenoiser(k_diffusion_external.DiscreteVDDPMDenoiser):
def __init__(self, model, quantize=False, device='cpu'):
super().__init__(model, model.alphas_cumprod, quantize=quantize)
def get_v(self, x, t, cond, **kwargs):
return self.inner_model.apply_model(x, t, cond, **kwargs)
class CFGNoisePredictor(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
self.alphas_cumprod = model.alphas_cumprod
def apply_model(self, x, timestep, cond, uncond, cond_scale, model_options={}, seed=None):
out = sampling_function(self.inner_model.apply_model, x, timestep, uncond, cond, cond_scale, model_options=model_options, seed=seed)
return out
def forward(self, *args, **kwargs):
return self.apply_model(*args, **kwargs)
class KSamplerX0Inpaint(torch.nn.Module):
def __init__(self, model):
@@ -293,32 +280,40 @@ class KSamplerX0Inpaint(torch.nn.Module):
return out
def simple_scheduler(model, steps):
s = model.model_sampling
sigs = []
ss = len(model.sigmas) / steps
ss = len(s.sigmas) / steps
for x in range(steps):
sigs += [float(model.sigmas[-(1 + int(x * ss))])]
sigs += [float(s.sigmas[-(1 + int(x * ss))])]
sigs += [0.0]
return torch.FloatTensor(sigs)
def ddim_scheduler(model, steps):
s = model.model_sampling
sigs = []
ddim_timesteps = make_ddim_timesteps(ddim_discr_method="uniform", num_ddim_timesteps=steps, num_ddpm_timesteps=model.inner_model.inner_model.num_timesteps, verbose=False)
for x in range(len(ddim_timesteps) - 1, -1, -1):
ts = ddim_timesteps[x]
if ts > 999:
ts = 999
sigs.append(model.t_to_sigma(torch.tensor(ts)))
ss = len(s.sigmas) // steps
x = 1
while x < len(s.sigmas):
sigs += [float(s.sigmas[x])]
x += ss
sigs = sigs[::-1]
sigs += [0.0]
return torch.FloatTensor(sigs)
def sgm_scheduler(model, steps):
def normal_scheduler(model, steps, sgm=False, floor=False):
s = model.model_sampling
start = s.timestep(s.sigma_max)
end = s.timestep(s.sigma_min)
if sgm:
timesteps = torch.linspace(start, end, steps + 1)[:-1]
else:
timesteps = torch.linspace(start, end, steps)
sigs = []
timesteps = torch.linspace(model.inner_model.inner_model.num_timesteps - 1, 0, steps + 1)[:-1].type(torch.int)
for x in range(len(timesteps)):
ts = timesteps[x]
if ts > 999:
ts = 999
sigs.append(model.t_to_sigma(torch.tensor(ts)))
sigs.append(s.sigma(ts))
sigs += [0.0]
return torch.FloatTensor(sigs)
@@ -418,15 +413,16 @@ def create_cond_with_same_area_if_none(conds, c):
conds += [out]
def calculate_start_end_timesteps(model, conds):
s = model.model_sampling
for t in range(len(conds)):
x = conds[t]
timestep_start = None
timestep_end = None
if 'start_percent' in x:
timestep_start = model.sigma_to_t(model.t_to_sigma(torch.tensor(x['start_percent'] * 999.0)))
timestep_start = s.percent_to_sigma(x['start_percent'])
if 'end_percent' in x:
timestep_end = model.sigma_to_t(model.t_to_sigma(torch.tensor(x['end_percent'] * 999.0)))
timestep_end = s.percent_to_sigma(x['end_percent'])
if (timestep_start is not None) or (timestep_end is not None):
n = x.copy()
@@ -437,14 +433,15 @@ def calculate_start_end_timesteps(model, conds):
conds[t] = n
def pre_run_control(model, conds):
s = model.model_sampling
for t in range(len(conds)):
x = conds[t]
timestep_start = None
timestep_end = None
percent_to_timestep_function = lambda a: model.sigma_to_t(model.t_to_sigma(torch.tensor(a) * 999.0))
percent_to_timestep_function = lambda a: s.percent_to_sigma(a)
if 'control' in x:
x['control'].pre_run(model.inner_model.inner_model, percent_to_timestep_function)
x['control'].pre_run(model, percent_to_timestep_function)
def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func):
cond_cnets = []
@@ -508,42 +505,9 @@ class Sampler:
pass
def max_denoise(self, model_wrap, sigmas):
return math.isclose(float(model_wrap.sigma_max), float(sigmas[0]), rel_tol=1e-05)
class DDIM(Sampler):
def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
timesteps = []
for s in range(sigmas.shape[0]):
timesteps.insert(0, model_wrap.sigma_to_discrete_timestep(sigmas[s]))
noise_mask = None
if denoise_mask is not None:
noise_mask = 1.0 - denoise_mask
ddim_callback = None
if callback is not None:
total_steps = len(timesteps) - 1
ddim_callback = lambda pred_x0, i: callback(i, pred_x0, None, total_steps)
max_denoise = self.max_denoise(model_wrap, sigmas)
ddim_sampler = DDIMSampler(model_wrap.inner_model.inner_model, device=noise.device)
ddim_sampler.make_schedule_timesteps(ddim_timesteps=timesteps, verbose=False)
z_enc = ddim_sampler.stochastic_encode(latent_image, torch.tensor([len(timesteps) - 1] * noise.shape[0]).to(noise.device), noise=noise, max_denoise=max_denoise)
samples, _ = ddim_sampler.sample_custom(ddim_timesteps=timesteps,
batch_size=noise.shape[0],
shape=noise.shape[1:],
verbose=False,
eta=0.0,
x_T=z_enc,
x0=latent_image,
img_callback=ddim_callback,
denoise_function=model_wrap.predict_eps_discrete_timestep,
extra_args=extra_args,
mask=noise_mask,
to_zero=sigmas[-1]==0,
end_step=sigmas.shape[0] - 1,
disable_pbar=disable_pbar)
return samples
max_sigma = float(model_wrap.inner_model.model_sampling.sigma_max)
sigma = float(sigmas[0])
return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma
class UNIPC(Sampler):
def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
@@ -555,15 +519,19 @@ class UNIPCBH2(Sampler):
KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm"]
"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"]
def ksampler(sampler_name, extra_options={}):
def ksampler(sampler_name, extra_options={}, inpaint_options={}):
class KSAMPLER(Sampler):
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
model_k.noise = noise
if 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)
@@ -592,11 +560,7 @@ def ksampler(sampler_name, extra_options={}):
def wrap_model(model):
model_denoise = CFGNoisePredictor(model)
if model.model_type == model_base.ModelType.V_PREDICTION:
model_wrap = CompVisVDenoiser(model_denoise, quantize=True)
else:
model_wrap = k_diffusion_external.CompVisDenoiser(model_denoise, quantize=True)
return model_wrap
return model_denoise
def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
positive = positive[:]
@@ -607,8 +571,8 @@ def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model
model_wrap = wrap_model(model)
calculate_start_end_timesteps(model_wrap, negative)
calculate_start_end_timesteps(model_wrap, positive)
calculate_start_end_timesteps(model, negative)
calculate_start_end_timesteps(model, positive)
#make sure each cond area has an opposite one with the same area
for c in positive:
@@ -616,7 +580,7 @@ def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model
for c in negative:
create_cond_with_same_area_if_none(positive, c)
pre_run_control(model_wrap, negative + positive)
pre_run_control(model, negative + positive)
apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x])
apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x])
@@ -637,19 +601,18 @@ SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "
SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
def calculate_sigmas_scheduler(model, scheduler_name, steps):
model_wrap = wrap_model(model)
if scheduler_name == "karras":
sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model_wrap.sigma_min), sigma_max=float(model_wrap.sigma_max))
sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max))
elif scheduler_name == "exponential":
sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model_wrap.sigma_min), sigma_max=float(model_wrap.sigma_max))
sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max))
elif scheduler_name == "normal":
sigmas = model_wrap.get_sigmas(steps)
sigmas = normal_scheduler(model, steps)
elif scheduler_name == "simple":
sigmas = simple_scheduler(model_wrap, steps)
sigmas = simple_scheduler(model, steps)
elif scheduler_name == "ddim_uniform":
sigmas = ddim_scheduler(model_wrap, steps)
sigmas = ddim_scheduler(model, steps)
elif scheduler_name == "sgm_uniform":
sigmas = sgm_scheduler(model_wrap, steps)
sigmas = normal_scheduler(model, steps, sgm=True)
else:
print("error invalid scheduler", self.scheduler)
return sigmas
@@ -660,7 +623,7 @@ def sampler_class(name):
elif name == "uni_pc_bh2":
sampler = UNIPCBH2
elif name == "ddim":
sampler = DDIM
sampler = ksampler("euler", inpaint_options={"random": True})
else:
sampler = ksampler(name)
return sampler