Commit f8ff8c06 authored by AUTOMATIC1111's avatar AUTOMATIC1111
Browse files

merge errors

parent 54c3e5c9

modules/sd_samplers_cfg_denoiser.py

+21 −2
Original line number Diff line number Diff line
@@ -38,16 +38,24 @@ class CFGDenoiser(torch.nn.Module): 
    negative prompt.

    """



    def __init__(self, model, sampler):

    def __init__(self, sampler):

        super().__init__()

        self.inner_model = model

        self.model_wrap = None

        self.mask = None

        self.nmask = None

        self.init_latent = None

        self.steps = None

        self.step = 0

        self.image_cfg_scale = None

        self.padded_cond_uncond = False

        self.sampler = sampler

        self.model_wrap = None

        self.p = None



    @property

    def inner_model(self):

        raise NotImplementedError()





    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):

        denoised_uncond = x_out[-uncond.shape[0]:]
@@ -68,10 +76,21 @@ class CFGDenoiser(torch.nn.Module): 
    def get_pred_x0(self, x_in, x_out, sigma):

        return x_out



    def update_inner_model(self):

        self.model_wrap = None



        c, uc = self.p.get_conds()

        self.sampler.sampler_extra_args['cond'] = c

        self.sampler.sampler_extra_args['uncond'] = uc



    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



        if sd_samplers_common.apply_refiner(self):

            cond = self.sampler.sampler_extra_args['cond']

            uncond = self.sampler.sampler_extra_args['uncond']



        # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,

        # so is_edit_model is set to False to support AND composition.

        is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0

modules/sd_samplers_common.py

+5 −1
Original line number Diff line number Diff line
@@ -202,8 +202,9 @@ class Sampler: 


        self.conditioning_key = shared.sd_model.model.conditioning_key



        self.model_wrap = None

        self.p = None

        self.model_wrap_cfg = None

        self.sampler_extra_args = None



    def callback_state(self, d):

        step = d['i']
@@ -215,6 +216,7 @@ class Sampler: 
        shared.total_tqdm.update()



    def launch_sampling(self, steps, func):

        self.model_wrap_cfg.steps = steps

        state.sampling_steps = steps

        state.sampling_step = 0
@@ -234,6 +236,8 @@ class Sampler: 
        return p.steps



    def initialize(self, p) -> dict:

        self.p = p

        self.model_wrap_cfg.p = 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.step = 0

modules/sd_samplers_kdiffusion.py

+12 −5
Original line number Diff line number Diff line
@@ -52,17 +52,24 @@ k_diffusion_scheduler = { 
}





class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser):

    @property

    def inner_model(self):

        if self.model_wrap is None:

            denoiser = k_diffusion.external.CompVisVDenoiser if shared.sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser

            self.model_wrap = denoiser(shared.sd_model, quantize=shared.opts.enable_quantization)



        return self.model_wrap





class KDiffusionSampler(sd_samplers_common.Sampler):

    def __init__(self, funcname, sd_model):



        super().__init__(funcname)



        self.extra_params = sampler_extra_params.get(funcname, [])

        self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)



        denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser

        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)

        self.model_wrap_cfg = sd_samplers_cfg_denoiser.CFGDenoiser(self.model_wrap, self)

        self.model_wrap_cfg = CFGDenoiserKDiffusion(self)

        self.model_wrap = self.model_wrap_cfg.inner_model



    def get_sigmas(self, p, steps):

        discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)

modules/sd_samplers_timesteps.py

+17 −10
Original line number Diff line number Diff line
@@ -44,10 +44,10 @@ class CompVisTimestepsVDenoiser(torch.nn.Module): 


class CFGDenoiserTimesteps(CFGDenoiser):



    def __init__(self, model, sampler):

        super().__init__(model, sampler)

    def __init__(self, sampler):

        super().__init__(sampler)



        self.alphas = model.inner_model.alphas_cumprod

        self.alphas = shared.sd_model.alphas_cumprod



    def get_pred_x0(self, x_in, x_out, sigma):

        ts = int(sigma.item())
@@ -60,6 +60,14 @@ class CFGDenoiserTimesteps(CFGDenoiser): 


        return pred_x0



    @property

    def inner_model(self):

        if self.model_wrap is None:

            denoiser = CompVisTimestepsVDenoiser if shared.sd_model.parameterization == "v" else CompVisTimestepsDenoiser

            self.model_wrap = denoiser(shared.sd_model)



        return self.model_wrap





class CompVisSampler(sd_samplers_common.Sampler):

    def __init__(self, funcname, sd_model):
@@ -68,9 +76,7 @@ class CompVisSampler(sd_samplers_common.Sampler): 
        self.eta_option_field = 'eta_ddim'

        self.eta_infotext_field = 'Eta DDIM'



        denoiser = CompVisTimestepsVDenoiser if sd_model.parameterization == "v" else CompVisTimestepsDenoiser

        self.model_wrap = denoiser(sd_model)

        self.model_wrap_cfg = CFGDenoiserTimesteps(self.model_wrap, self)

        self.model_wrap_cfg = CFGDenoiserTimesteps(self)



    def get_timesteps(self, p, steps):

        discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
@@ -106,7 +112,7 @@ class CompVisSampler(sd_samplers_common.Sampler): 


        self.model_wrap_cfg.init_latent = x

        self.last_latent = x

        extra_args = {

        self.sampler_extra_args = {

            'cond': conditioning,

            'image_cond': image_conditioning,

            'uncond': unconditional_conditioning,
@@ -114,7 +120,7 @@ class CompVisSampler(sd_samplers_common.Sampler): 
            '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))

        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))



        if self.model_wrap_cfg.padded_cond_uncond:

            p.extra_generation_params["Pad conds"] = True
@@ -132,13 +138,14 @@ class CompVisSampler(sd_samplers_common.Sampler): 
            extra_params_kwargs['timesteps'] = timesteps



        self.last_latent = x

        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={

        self.sampler_extra_args = {

            'cond': conditioning,

            'image_cond': image_conditioning,

            'uncond': unconditional_conditioning,

            'cond_scale': p.cfg_scale,

            's_min_uncond': self.s_min_uncond

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

        }

        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))



        if self.model_wrap_cfg.padded_cond_uncond:

            p.extra_generation_params["Pad conds"] = True

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