Unverified Commit 3591eefe authored by AUTOMATIC1111's avatar AUTOMATIC1111 Committed by GitHub
Browse files

Merge pull request #9177 from devNegative-asm/master 

(Optimization) Option to remove negative conditioning at low sigma values
parents 7b02b17a 44e8e9c3

modules/processing.py

+3 −1
Original line number Diff line number Diff line
@@ -106,7 +106,7 @@ class StableDiffusionProcessing: 
    """

    The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing

    """

    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):

    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):

        if sampler_index is not None:

            print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
@@ -141,6 +141,7 @@ class StableDiffusionProcessing: 
        self.denoising_strength: float = denoising_strength

        self.sampler_noise_scheduler_override = None

        self.ddim_discretize = ddim_discretize or opts.ddim_discretize

        self.s_min_uncond = s_min_uncond or opts.s_min_uncond

        self.s_churn = s_churn or opts.s_churn

        self.s_tmin = s_tmin or opts.s_tmin

        self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
@@ -164,6 +165,7 @@ class StableDiffusionProcessing: 
        self.all_subseeds = None

        self.iteration = 0

        



    @property

    def sd_model(self):

        return shared.sd_model

modules/sd_samplers_kdiffusion.py

+20 −6
Original line number Diff line number Diff line
@@ -76,7 +76,7 @@ class CFGDenoiser(torch.nn.Module): 


        return denoised



    def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):

    def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):

        if state.interrupted or state.skipped:

            raise sd_samplers_common.InterruptedException
@@ -116,6 +116,14 @@ class CFGDenoiser(torch.nn.Module): 
        tensor = denoiser_params.text_cond

        uncond = denoiser_params.text_uncond



        if self.step % 2 and s_min_uncond > 0 and not is_edit_model:

            # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it

            sigma_threshold = s_min_uncond

            if(torch.dot(sigma,sigma) < sigma.shape[0] * (sigma_threshold*sigma_threshold) ):

                uncond = torch.zeros([0,0,uncond.shape[2]])

                x_in=x_in[:x_in.shape[0]//2]

                sigma_in=sigma_in[:sigma_in.shape[0]//2]



        if tensor.shape[1] == uncond.shape[1]:

            if not is_edit_model:

                cond_in = torch.cat([tensor, uncond])
@@ -144,6 +152,7 @@ class CFGDenoiser(torch.nn.Module): 


                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]))



            if uncond.shape[0]:

                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]:]))



        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
@@ -152,12 +161,15 @@ class CFGDenoiser(torch.nn.Module): 
        devices.test_for_nans(x_out, "unet")



        if opts.live_preview_content == "Prompt":

            sd_samplers_common.store_latent(x_out[0:uncond.shape[0]])

            sd_samplers_common.store_latent(x_out[0:x_out.shape[0]-uncond.shape[0]])

        elif opts.live_preview_content == "Negative prompt":

            sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])



        if not is_edit_model:

            if uncond.shape[0]:

                denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)

            else:

                denoised = x_out

        else:

            denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
@@ -165,7 +177,6 @@ class CFGDenoiser(torch.nn.Module): 
            denoised = self.init_latent * self.mask + self.nmask * denoised



        self.step += 1



        return denoised
@@ -244,6 +255,7 @@ class KDiffusionSampler: 
        self.model_wrap_cfg.step = 0

        self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)

        self.eta = p.eta if p.eta is not None else opts.eta_ancestral

        self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)



        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
@@ -326,6 +338,7 @@ class KDiffusionSampler: 
            'image_cond': image_conditioning, 

            'uncond': unconditional_conditioning, 

            'cond_scale': p.cfg_scale,

            's_min_uncond': self.s_min_uncond

        }



        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))
@@ -359,7 +372,8 @@ class KDiffusionSampler: 
            'cond': conditioning, 

            'image_cond': image_conditioning, 

            'uncond': unconditional_conditioning, 

            'cond_scale': p.cfg_scale

            'cond_scale': p.cfg_scale,

            's_min_uncond': self.s_min_uncond

        }, disable=False, callback=self.callback_state, **extra_params_kwargs))



        return samples

modules/shared.py

+1 −0
Original line number Diff line number Diff line
@@ -430,6 +430,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters" 
    "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_min_uncond': OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "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}),

    'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),

scripts/xyz_grid.py

+1 −0
Original line number Diff line number Diff line
@@ -212,6 +212,7 @@ axis_options = [ 
    AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),

    AxisOptionImg2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),

    AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: list(sd_models.checkpoints_list)),

    AxisOption("Negative Guidance minimum sigma", float, apply_field("s_min_uncond")),

    AxisOption("Sigma Churn", float, apply_field("s_churn")),

    AxisOption("Sigma min", float, apply_field("s_tmin")),

    AxisOption("Sigma max", float, apply_field("s_tmax")),

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