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
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@ -113,6 +113,13 @@ if not skip_torch_cuda_test:
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if not is_installed("k_diffusion.sampling"):
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run_pip(f"install {k_diffusion_package}", "k-diffusion")
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if not check_run_python("import k_diffusion; import inspect; assert 'eta' in inspect.signature(k_diffusion.sampling.sample_euler_ancestral).parameters"):
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print(f"k-diffusion does not have 'eta' parameter; reinstalling latest version")
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try:
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run_pip(f"install --upgrade --force-reinstall {k_diffusion_package}", "k-diffusion")
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except RuntimeError as e:
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print(str(e))
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if not is_installed("gfpgan"):
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run_pip(f"install {gfpgan_package}", "gfpgan")
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@ -49,7 +49,7 @@ def apply_color_correction(correction, image):
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class StableDiffusionProcessing:
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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):
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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):
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self.sd_model = sd_model
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self.outpath_samples: str = outpath_samples
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self.outpath_grids: str = outpath_grids
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@ -75,15 +75,15 @@ class StableDiffusionProcessing:
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self.do_not_save_grid: bool = do_not_save_grid
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self.extra_generation_params: dict = extra_generation_params or {}
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self.overlay_images = overlay_images
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self.eta = eta
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self.paste_to = None
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self.color_corrections = None
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self.denoising_strength: float = 0
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self.eta = opts.eta
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self.ddim_discretize = opts.ddim_discretize
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self.s_churn = opts.s_churn
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self.s_tmin = opts.s_tmin
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self.s_tmax = float('inf') # not representable as a standard ui option
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self.s_tmax = float('inf') # not representable as a standard ui option
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self.s_noise = opts.s_noise
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if not seed_enable_extras:
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@ -271,6 +271,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
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"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
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"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}"),
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"Denoising strength": getattr(p, 'denoising_strength', None),
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"Eta": (None if p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
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}
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generation_params.update(p.extra_generation_params)
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@ -40,10 +40,8 @@ samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
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sampler_extra_params = {
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'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
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'sample_euler_ancestral': ['eta'],
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'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
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'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
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'sample_dpm_2_ancestral': ['eta'],
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}
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def setup_img2img_steps(p, steps=None):
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@ -101,6 +99,8 @@ class VanillaStableDiffusionSampler:
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self.init_latent = None
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self.sampler_noises = None
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self.step = 0
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self.eta = None
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self.default_eta = 0.0
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def number_of_needed_noises(self, p):
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return 0
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@ -123,20 +123,29 @@ class VanillaStableDiffusionSampler:
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self.step += 1
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return res
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def initialize(self, p):
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self.eta = p.eta or opts.eta_ddim
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for fieldname in ['p_sample_ddim', 'p_sample_plms']:
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if hasattr(self.sampler, fieldname):
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setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
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self.mask = p.mask if hasattr(p, 'mask') else None
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self.nmask = p.nmask if hasattr(p, 'nmask') else None
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def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
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steps, t_enc = setup_img2img_steps(p, steps)
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# existing code fails with cetain step counts, like 9
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try:
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self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
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self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
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except Exception:
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self.sampler.make_schedule(ddim_num_steps=steps+1,ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
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self.sampler.make_schedule(ddim_num_steps=steps+1, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
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x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
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self.sampler.p_sample_ddim = self.p_sample_ddim_hook
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self.mask = p.mask if hasattr(p, 'mask') else None
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self.nmask = p.nmask if hasattr(p, 'nmask') else None
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self.initialize(p)
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self.init_latent = x
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self.step = 0
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@ -145,11 +154,8 @@ class VanillaStableDiffusionSampler:
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return samples
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def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
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for fieldname in ['p_sample_ddim', 'p_sample_plms']:
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if hasattr(self.sampler, fieldname):
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setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
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self.mask = None
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self.nmask = None
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self.initialize(p)
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self.init_latent = None
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self.step = 0
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@ -157,9 +163,9 @@ class VanillaStableDiffusionSampler:
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# existing code fails with cetin step counts, like 9
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try:
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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)
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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)
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except Exception:
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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)
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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)
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return samples_ddim
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@ -237,6 +243,8 @@ class KDiffusionSampler:
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self.sampler_noises = None
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self.sampler_noise_index = 0
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self.stop_at = None
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self.eta = None
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self.default_eta = 1.0
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def callback_state(self, d):
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store_latent(d["denoised"])
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@ -255,22 +263,12 @@ class KDiffusionSampler:
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self.sampler_noise_index += 1
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return res
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def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
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steps, t_enc = setup_img2img_steps(p, steps)
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sigmas = self.model_wrap.get_sigmas(steps)
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noise = noise * sigmas[steps - t_enc - 1]
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xi = x + noise
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sigma_sched = sigmas[steps - t_enc - 1:]
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def initialize(self, p):
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self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
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self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
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self.model_wrap_cfg.init_latent = x
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self.model_wrap.step = 0
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self.sampler_noise_index = 0
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self.eta = p.eta or opts.eta_ancestral
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if hasattr(k_diffusion.sampling, 'trange'):
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k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
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@ -283,6 +281,25 @@ class KDiffusionSampler:
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if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
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extra_params_kwargs[param_name] = getattr(p, param_name)
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if 'eta' in inspect.signature(self.func).parameters:
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extra_params_kwargs['eta'] = self.eta
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return extra_params_kwargs
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def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
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steps, t_enc = setup_img2img_steps(p, steps)
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sigmas = self.model_wrap.get_sigmas(steps)
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noise = noise * sigmas[steps - t_enc - 1]
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xi = x + noise
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extra_params_kwargs = self.initialize(p)
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sigma_sched = sigmas[steps - t_enc - 1:]
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self.model_wrap_cfg.init_latent = x
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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)
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def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
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@ -291,19 +308,7 @@ class KDiffusionSampler:
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sigmas = self.model_wrap.get_sigmas(steps)
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x = x * sigmas[0]
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self.model_wrap_cfg.step = 0
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self.sampler_noise_index = 0
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if hasattr(k_diffusion.sampling, 'trange'):
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k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
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if self.sampler_noises is not None:
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k_diffusion.sampling.torch = TorchHijack(self)
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extra_params_kwargs = {}
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for param_name in self.extra_params:
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if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
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extra_params_kwargs[param_name] = getattr(p, param_name)
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extra_params_kwargs = self.initialize(p)
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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)
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@ -221,8 +221,9 @@ options_templates.update(options_section(('ui', "User interface"), {
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}))
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options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
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"eta": OptionInfo(0.0, "DDIM and K Ancestral eta", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform','quad']}),
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"eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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"eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
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's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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@ -87,12 +87,12 @@ axis_options = [
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AxisOption("Prompt S/R", str, apply_prompt, format_value),
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AxisOption("Sampler", str, apply_sampler, format_value),
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AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
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AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
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AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
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AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
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AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
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AxisOption("DDIM Eta", float, apply_field("ddim_eta"), format_value_add_label),
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AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),# as it is now all AxisOptionImg2Img items must go after AxisOption ones
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AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
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AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
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AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
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AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
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AxisOption("Eta", float, apply_field("eta"), format_value_add_label),
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AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label), # as it is now all AxisOptionImg2Img items must go after AxisOption ones
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]
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