added support for automatically installing latest k-diffusion
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
parent
9be0d1b89e
commit
d64b451681
5 changed files with 65 additions and 51 deletions
|
|
@ -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")
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
@ -75,15 +75,15 @@ 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
|
||||
self.s_tmax = float('inf') # not representable as a standard ui option
|
||||
self.s_tmax = float('inf') # not representable as a standard ui option
|
||||
self.s_noise = opts.s_noise
|
||||
|
||||
if not seed_enable_extras:
|
||||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
||||
|
|
|
|||
|
|
@ -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}),
|
||||
|
|
|
|||
|
|
@ -87,12 +87,12 @@ axis_options = [
|
|||
AxisOption("Prompt S/R", str, apply_prompt, format_value),
|
||||
AxisOption("Sampler", str, apply_sampler, format_value),
|
||||
AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
|
||||
AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
|
||||
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("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
|
||||
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("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
|
||||
]
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue