Merge pull request #9177 from devNegative-asm/master
(Optimization) Option to remove negative conditioning at low sigma values
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AUTOMATIC1111
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3591eefedf
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@ -106,7 +106,7 @@ class StableDiffusionProcessing:
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"""
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The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
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"""
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def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
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def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
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if sampler_index is not None:
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print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
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@ -141,6 +141,7 @@ class StableDiffusionProcessing:
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self.denoising_strength: float = denoising_strength
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self.sampler_noise_scheduler_override = None
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self.ddim_discretize = ddim_discretize or opts.ddim_discretize
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self.s_min_uncond = s_min_uncond or opts.s_min_uncond
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self.s_churn = s_churn or opts.s_churn
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self.s_tmin = s_tmin or opts.s_tmin
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self.s_tmax = s_tmax or float('inf') # not representable as a standard ui option
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@ -163,6 +164,7 @@ class StableDiffusionProcessing:
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self.all_seeds = None
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self.all_subseeds = None
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self.iteration = 0
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@property
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def sd_model(self):
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@ -76,7 +76,7 @@ class CFGDenoiser(torch.nn.Module):
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return denoised
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def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
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def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
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if state.interrupted or state.skipped:
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raise sd_samplers_common.InterruptedException
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@ -116,6 +116,14 @@ class CFGDenoiser(torch.nn.Module):
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tensor = denoiser_params.text_cond
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uncond = denoiser_params.text_uncond
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if self.step % 2 and s_min_uncond > 0 and not is_edit_model:
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# alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
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sigma_threshold = s_min_uncond
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if(torch.dot(sigma,sigma) < sigma.shape[0] * (sigma_threshold*sigma_threshold) ):
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uncond = torch.zeros([0,0,uncond.shape[2]])
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x_in=x_in[:x_in.shape[0]//2]
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sigma_in=sigma_in[:sigma_in.shape[0]//2]
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if tensor.shape[1] == uncond.shape[1]:
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if not is_edit_model:
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cond_in = torch.cat([tensor, uncond])
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@ -144,7 +152,8 @@ class CFGDenoiser(torch.nn.Module):
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x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
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x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
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if uncond.shape[0]:
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x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
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denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
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cfg_denoised_callback(denoised_params)
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@ -152,12 +161,15 @@ class CFGDenoiser(torch.nn.Module):
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devices.test_for_nans(x_out, "unet")
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if opts.live_preview_content == "Prompt":
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sd_samplers_common.store_latent(x_out[0:uncond.shape[0]])
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sd_samplers_common.store_latent(x_out[0:x_out.shape[0]-uncond.shape[0]])
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elif opts.live_preview_content == "Negative prompt":
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sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
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if not is_edit_model:
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denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
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if uncond.shape[0]:
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denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
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else:
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denoised = x_out
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else:
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denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
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@ -165,7 +177,6 @@ class CFGDenoiser(torch.nn.Module):
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denoised = self.init_latent * self.mask + self.nmask * denoised
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self.step += 1
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return denoised
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@ -244,6 +255,7 @@ class KDiffusionSampler:
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self.model_wrap_cfg.step = 0
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self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
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self.eta = p.eta if p.eta is not None else opts.eta_ancestral
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self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
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k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
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@ -326,6 +338,7 @@ class KDiffusionSampler:
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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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samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
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@ -359,7 +372,8 @@ class KDiffusionSampler:
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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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'cond_scale': p.cfg_scale,
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's_min_uncond': self.s_min_uncond
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}, disable=False, callback=self.callback_state, **extra_params_kwargs))
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return samples
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@ -430,6 +430,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
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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_min_uncond': OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "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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'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
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@ -212,6 +212,7 @@ axis_options = [
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AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
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AxisOptionImg2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
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AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: list(sd_models.checkpoints_list)),
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AxisOption("Negative Guidance minimum sigma", float, apply_field("s_min_uncond")),
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AxisOption("Sigma Churn", float, apply_field("s_churn")),
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AxisOption("Sigma min", float, apply_field("s_tmin")),
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AxisOption("Sigma max", float, apply_field("s_tmax")),
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