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https://github.com/lllyasviel/stable-diffusion-webui-forge.git
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260 lines
12 KiB
Python
260 lines
12 KiB
Python
import inspect
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import k_diffusion
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import k_diffusion.external
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import torch
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import modules.shared as shared
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from backend.sampling.sampling_function import sampling_cleanup, sampling_prepare
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from modules import devices, sd_samplers_cfg_denoiser, sd_samplers_common, sd_samplers_extra, sd_schedulers
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from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
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from modules.sd_samplers_cfg_denoiser import CFGDenoiser # noqa: F401
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from modules.shared import opts
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samplers_k_diffusion = [
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("DPM++ 2M", "sample_dpmpp_2m", ["k_dpmpp_2m"], {"scheduler": "karras"}),
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("DPM++ SDE", "sample_dpmpp_sde", ["k_dpmpp_sde"], {"scheduler": "karras", "second_order": True, "brownian_noise": True}),
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("DPM++ 2M SDE", "sample_dpmpp_2m_sde", ["k_dpmpp_2m_sde"], {"scheduler": "exponential", "brownian_noise": True}),
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("DPM++ 3M SDE", "sample_dpmpp_3m_sde", ["k_dpmpp_3m_sde"], {"scheduler": "exponential", "discard_next_to_last_sigma": True, "brownian_noise": True}),
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("Flux Realistic" if opts.forbidden_knowledge else "DPM++ 2s a RF", "sample_dpmpp_2s_ancestral_RF", ["sample_dpmpp_2s_ancestral_RF"], {}),
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("Euler a", "sample_euler_ancestral", ["k_euler_a", "k_euler_ancestral"], {"uses_ensd": True}),
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("Euler", "sample_euler", ["k_euler"], {}),
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("ER SDE", "sample_er_sde", ["er_sde"], {}),
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("LCM", "sample_lcm", ["k_lcm"], {}),
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("LMS", "sample_lms", ["k_lms"], {}),
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("Heun", "sample_heun", ["k_heun"], {"second_order": True}),
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("DPM2", "sample_dpm_2", ["k_dpm_2"], {"scheduler": "karras", "discard_next_to_last_sigma": True, "second_order": True}),
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("Res Multistep", "sample_res_multistep", ["res_multistep"], {}),
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("Kohaku LoNyu Yog", "sample_Kohaku_LoNyu_Yog", ["Kohaku_LoNyu_Yog"], {}),
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("Restart", sd_samplers_extra.restart_sampler, ["restart"], {"scheduler": "karras", "second_order": True}),
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("UniPC", sd_samplers_extra.sample_unipc, ["unipc"], {"discard_next_to_last_sigma": True}),
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]
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samplers_data_k_diffusion = [sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options) for label, funcname, aliases, options in samplers_k_diffusion if callable(funcname) or hasattr(k_diffusion.sampling, funcname)]
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sampler_extra_params = {
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"sample_dpmpp_sde": ["eta", "s_noise", "r"],
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"sample_dpmpp_2m_sde": ["eta", "s_noise"],
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"sample_dpmpp_3m_sde": ["eta", "s_noise"],
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"sample_euler_ancestral": ["eta", "s_noise"],
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"sample_euler": ["s_churn", "s_tmin", "s_tmax", "s_noise"],
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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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}
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k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
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k_diffusion_scheduler = {x.name: x.function for x in sd_schedulers.schedulers}
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class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser):
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@property
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def inner_model(self):
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if self.model_wrap is None:
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self.model_wrap = k_diffusion.external.ForgeScheduleLinker(shared.sd_model.forge_objects.unet.model.predictor)
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self.model_wrap.inner_model = shared.sd_model
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return self.model_wrap
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class KDiffusionSampler(sd_samplers_common.Sampler):
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def __init__(self, funcname, sd_model, options=None):
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super().__init__(funcname)
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self.extra_params = sampler_extra_params.get(funcname, [])
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self.options = options or {}
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self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
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self.model_wrap_cfg = CFGDenoiserKDiffusion(self)
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self.model_wrap = self.model_wrap_cfg.inner_model
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def get_sigmas(self, p, steps):
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discard_next_to_last_sigma = self.config is not None and self.config.options.get("discard_next_to_last_sigma", False)
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if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
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discard_next_to_last_sigma = True
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p.extra_generation_params["Discard penultimate sigma"] = True
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steps += 1 if discard_next_to_last_sigma else 0
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scheduler_name = (p.hr_scheduler if p.is_hr_pass else p.scheduler) or "Automatic"
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if scheduler_name == "Automatic":
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from backend.args import dynamic_args
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if dynamic_args.klein:
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scheduler_name = "Flux2"
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else:
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scheduler_name = self.config.options.get("scheduler", None)
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if scheduler_name is None and not p.sd_model.is_webui_legacy_model():
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scheduler_name = "Normal"
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scheduler = sd_schedulers.schedulers_map.get(scheduler_name)
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m_sigma_min, m_sigma_max = self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item()
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if p.sampler_noise_scheduler_override:
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sigmas = p.sampler_noise_scheduler_override(steps)
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elif scheduler is None or scheduler.function is None:
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sigmas = self.model_wrap.get_sigmas(steps)
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else:
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sigmas_kwargs = {"sigma_min": m_sigma_min, "sigma_max": m_sigma_max}
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if scheduler.label != "Automatic" and not p.is_hr_pass:
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p.extra_generation_params["Schedule type"] = scheduler.label
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elif scheduler.label != p.extra_generation_params.get("Schedule type"):
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p.extra_generation_params["Hires schedule type"] = scheduler.label
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if opts.sigma_min != 0 and opts.sigma_min != m_sigma_min:
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sigmas_kwargs["sigma_min"] = opts.sigma_min
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p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
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if opts.sigma_max != 0 and opts.sigma_max != m_sigma_max:
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sigmas_kwargs["sigma_max"] = opts.sigma_max
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p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
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if scheduler.default_rho != -1 and opts.rho != 0 and opts.rho != scheduler.default_rho:
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sigmas_kwargs["rho"] = opts.rho
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p.extra_generation_params["Schedule rho"] = opts.rho
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if scheduler.need_inner_model:
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sigmas_kwargs["inner_model"] = self.model_wrap
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if scheduler.label == "Beta":
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p.extra_generation_params["Beta schedule alpha"] = opts.beta_dist_alpha
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p.extra_generation_params["Beta schedule beta"] = opts.beta_dist_beta
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if scheduler.label == "Flux2":
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if p.is_hr_pass:
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sigmas_kwargs["width"] = p.hr_upscale_to_x
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sigmas_kwargs["height"] = p.hr_upscale_to_y
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else:
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sigmas_kwargs["width"] = p.width
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sigmas_kwargs["height"] = p.height
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sigmas = scheduler.function(n=steps, **sigmas_kwargs, device=devices.cpu)
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if discard_next_to_last_sigma:
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sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
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return sigmas.cpu()
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def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
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unet_patcher = self.model_wrap.inner_model.forge_objects.unet
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sampling_prepare(self.model_wrap.inner_model.forge_objects.unet, x=x)
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steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
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sigmas = self.get_sigmas(p, steps).to(x.device)
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sigma_sched = sigmas[steps - t_enc - 1 :]
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x = x.to(noise)
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xi = self.model_wrap.predictor.noise_scaling(sigma_sched[0], noise, x, max_denoise=False)
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if opts.img2img_extra_noise > 0:
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p.extra_generation_params["Extra noise"] = opts.img2img_extra_noise
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extra_noise_params = ExtraNoiseParams(noise, x, xi)
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extra_noise_callback(extra_noise_params)
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noise = extra_noise_params.noise
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xi += noise * opts.img2img_extra_noise
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extra_params_kwargs = self.initialize(p)
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parameters = inspect.signature(self.func).parameters
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if "sigma_min" in parameters:
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## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
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extra_params_kwargs["sigma_min"] = sigma_sched[-2]
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if "sigma_max" in parameters:
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extra_params_kwargs["sigma_max"] = sigma_sched[0]
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if "n" in parameters:
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extra_params_kwargs["n"] = len(sigma_sched) - 1
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if "sigma_sched" in parameters:
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extra_params_kwargs["sigma_sched"] = sigma_sched
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if "sigmas" in parameters:
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extra_params_kwargs["sigmas"] = sigma_sched
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if self.config.options.get("brownian_noise", False):
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noise_sampler = self.create_noise_sampler(x, sigmas, p)
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extra_params_kwargs["noise_sampler"] = noise_sampler
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if self.config.options.get("solver_type", None) == "heun":
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extra_params_kwargs["solver_type"] = "heun"
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self.model_wrap_cfg.init_latent = x
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self.last_latent = x
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self.sampler_extra_args = {
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"cond": conditioning,
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"image_cond": image_conditioning,
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"uncond": unconditional_conditioning,
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"cond_scale": p.cfg_scale,
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"s_min_uncond": self.s_min_uncond,
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}
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p.sd_model.forge_objects.unet.model_options["transformer_options"]["sampling_sigmas"] = sigmas
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samples = self.launch_sampling(
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t_enc + 1,
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lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs),
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)
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self.add_infotext(p)
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sampling_cleanup(unet_patcher)
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return samples
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def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
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unet_patcher = self.model_wrap.inner_model.forge_objects.unet
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sampling_prepare(self.model_wrap.inner_model.forge_objects.unet, x=x)
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steps = steps or p.steps
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sigmas = self.get_sigmas(p, steps).to(x.device)
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if opts.sgm_noise_multiplier:
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p.extra_generation_params["SGM noise multiplier"] = True
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x = self.model_wrap.predictor.noise_scaling(sigmas[0], x, torch.zeros_like(x), max_denoise=opts.sgm_noise_multiplier)
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extra_params_kwargs = self.initialize(p)
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parameters = inspect.signature(self.func).parameters
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if "n" in parameters:
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extra_params_kwargs["n"] = steps
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if "sigma_min" in parameters:
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extra_params_kwargs["sigma_min"] = self.model_wrap.sigmas[0].item()
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extra_params_kwargs["sigma_max"] = self.model_wrap.sigmas[-1].item()
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if "sigmas" in parameters:
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extra_params_kwargs["sigmas"] = sigmas
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if self.config.options.get("brownian_noise", False):
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noise_sampler = self.create_noise_sampler(x, sigmas, p)
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extra_params_kwargs["noise_sampler"] = noise_sampler
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if self.config.options.get("solver_type", None) == "heun":
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extra_params_kwargs["solver_type"] = "heun"
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self.last_latent = x
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self.sampler_extra_args = {
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"cond": conditioning,
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"image_cond": image_conditioning,
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"uncond": unconditional_conditioning,
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"cond_scale": p.cfg_scale,
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"s_min_uncond": self.s_min_uncond,
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}
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p.sd_model.forge_objects.unet.model_options["transformer_options"]["sampling_sigmas"] = sigmas
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samples = self.launch_sampling(
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steps,
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lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs),
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)
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self.add_infotext(p)
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sampling_cleanup(unet_patcher)
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return samples
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