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added support for automatically installing latest k-diffusion

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added eta parameter to parameters output for generated images
split eta settings into ancestral and ddim (because they have different default values)
This commit is contained in:
AUTOMATIC
2022-09-28 18:09:06 +03:00
parent 9be0d1b89e
commit d64b451681
5 changed files with 65 additions and 51 deletions
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83
modules/sd_samplers.py
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@@ -40,10 +40,8 @@ samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
sampler_extra_params = {
'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
'sample_euler_ancestral': ['eta'],
'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
'sample_dpm_2_ancestral': ['eta'],
}
def setup_img2img_steps(p, steps=None):
@@ -101,6 +99,8 @@ class VanillaStableDiffusionSampler:
self.init_latent = None
self.sampler_noises = None
self.step = 0
self.eta = None
self.default_eta = 0.0
def number_of_needed_noises(self, p):
return 0
@@ -123,20 +123,29 @@ class VanillaStableDiffusionSampler:
self.step += 1
return res
def initialize(self, p):
self.eta = p.eta or opts.eta_ddim
for fieldname in ['p_sample_ddim', 'p_sample_plms']:
if hasattr(self.sampler, fieldname):
setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
self.mask = p.mask if hasattr(p, 'mask') else None
self.nmask = p.nmask if hasattr(p, 'nmask') else None
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
steps, t_enc = setup_img2img_steps(p, steps)
# existing code fails with cetain step counts, like 9
try:
self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
except Exception:
self.sampler.make_schedule(ddim_num_steps=steps+1,ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
self.sampler.make_schedule(ddim_num_steps=steps+1, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
self.sampler.p_sample_ddim = self.p_sample_ddim_hook
self.mask = p.mask if hasattr(p, 'mask') else None
self.nmask = p.nmask if hasattr(p, 'nmask') else None
self.initialize(p)
self.init_latent = x
self.step = 0
@@ -145,11 +154,8 @@ class VanillaStableDiffusionSampler:
return samples
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
for fieldname in ['p_sample_ddim', 'p_sample_plms']:
if hasattr(self.sampler, fieldname):
setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
self.mask = None
self.nmask = None
self.initialize(p)
self.init_latent = None
self.step = 0
@@ -157,9 +163,9 @@ class VanillaStableDiffusionSampler:
# existing code fails with cetin step counts, like 9
try:
samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.eta)
samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
except Exception:
samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.eta)
samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
return samples_ddim
@@ -237,6 +243,8 @@ class KDiffusionSampler:
self.sampler_noises = None
self.sampler_noise_index = 0
self.stop_at = None
self.eta = None
self.default_eta = 1.0
def callback_state(self, d):
store_latent(d["denoised"])
@@ -255,22 +263,12 @@ class KDiffusionSampler:
self.sampler_noise_index += 1
return res
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
steps, t_enc = setup_img2img_steps(p, steps)
sigmas = self.model_wrap.get_sigmas(steps)
noise = noise * sigmas[steps - t_enc - 1]
xi = x + noise
sigma_sched = sigmas[steps - t_enc - 1:]
def initialize(self, p):
self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
self.model_wrap_cfg.init_latent = x
self.model_wrap.step = 0
self.sampler_noise_index = 0
self.eta = p.eta or opts.eta_ancestral
if hasattr(k_diffusion.sampling, 'trange'):
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
@@ -283,6 +281,25 @@ class KDiffusionSampler:
if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
extra_params_kwargs[param_name] = getattr(p, param_name)
if 'eta' in inspect.signature(self.func).parameters:
extra_params_kwargs['eta'] = self.eta
return extra_params_kwargs
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
steps, t_enc = setup_img2img_steps(p, steps)
sigmas = self.model_wrap.get_sigmas(steps)
noise = noise * sigmas[steps - t_enc - 1]
xi = x + noise
extra_params_kwargs = self.initialize(p)
sigma_sched = sigmas[steps - t_enc - 1:]
self.model_wrap_cfg.init_latent = x
return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
@@ -291,19 +308,7 @@ class KDiffusionSampler:
sigmas = self.model_wrap.get_sigmas(steps)
x = x * sigmas[0]
self.model_wrap_cfg.step = 0
self.sampler_noise_index = 0
if hasattr(k_diffusion.sampling, 'trange'):
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
if self.sampler_noises is not None:
k_diffusion.sampling.torch = TorchHijack(self)
extra_params_kwargs = {}
for param_name in self.extra_params:
if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
extra_params_kwargs[param_name] = getattr(p, param_name)
extra_params_kwargs = self.initialize(p)
samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)