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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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7
launch.py
View file

@ -113,6 +113,13 @@ if not skip_torch_cuda_test:
if not is_installed("k_diffusion.sampling"):
run_pip(f"install {k_diffusion_package}", "k-diffusion")
if not check_run_python("import k_diffusion; import inspect; assert 'eta' in inspect.signature(k_diffusion.sampling.sample_euler_ancestral).parameters"):
print(f"k-diffusion does not have 'eta' parameter; reinstalling latest version")
try:
run_pip(f"install --upgrade --force-reinstall {k_diffusion_package}", "k-diffusion")
except RuntimeError as e:
print(str(e))
if not is_installed("gfpgan"):
run_pip(f"install {gfpgan_package}", "gfpgan")

5
modules/processing.py
View file

@ -49,7 +49,7 @@ def apply_color_correction(correction, image):
class StableDiffusionProcessing:
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
self.sd_model = sd_model
self.outpath_samples: str = outpath_samples
self.outpath_grids: str = outpath_grids
@ -75,11 +75,11 @@ class StableDiffusionProcessing:
self.do_not_save_grid: bool = do_not_save_grid
self.extra_generation_params: dict = extra_generation_params or {}
self.overlay_images = overlay_images
self.eta = eta
self.paste_to = None
self.color_corrections = None
self.denoising_strength: float = 0
self.eta = opts.eta
self.ddim_discretize = opts.ddim_discretize
self.s_churn = opts.s_churn
self.s_tmin = opts.s_tmin
@ -271,6 +271,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
"Denoising strength": getattr(p, 'denoising_strength', None),
"Eta": (None if p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
}
generation_params.update(p.extra_generation_params)

83
modules/sd_samplers.py
View file

@ -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)

5
modules/shared.py
View file

@ -221,8 +221,9 @@ options_templates.update(options_section(('ui', "User interface"), {
}))
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
"eta": OptionInfo(0.0, "DDIM and K Ancestral eta", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform','quad']}),
"eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

4
scripts/xy_grid.py
View file

@ -91,8 +91,8 @@ axis_options = [
AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
AxisOption("DDIM Eta", float, apply_field("ddim_eta"), format_value_add_label),
AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),# as it is now all AxisOptionImg2Img items must go after AxisOption ones
AxisOption("Eta", float, apply_field("eta"), format_value_add_label),
AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label), # as it is now all AxisOptionImg2Img items must go after AxisOption ones
]