diff options
Diffstat (limited to 'modules/sd_hijack_inpainting.py')
| -rw-r--r-- | modules/sd_hijack_inpainting.py | 20 |
1 files changed, 13 insertions, 7 deletions
diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py index 46714a4f..938f9a58 100644 --- a/modules/sd_hijack_inpainting.py +++ b/modules/sd_hijack_inpainting.py @@ -199,8 +199,8 @@ def sample_plms(self, @torch.no_grad() def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None): + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, dynamic_threshold=None): b, *_, device = *x.shape, x.device def get_model_output(x, t): @@ -249,6 +249,8 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() if quantize_denoised: pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + if dynamic_threshold is not None: + pred_x0 = norm_thresholding(pred_x0, dynamic_threshold) # direction pointing to x_t dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature @@ -321,12 +323,16 @@ def should_hijack_inpainting(checkpoint_info): def do_inpainting_hijack(): - ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning + # most of this stuff seems to no longer be needed because it is already included into SD2.0 + # LatentInpaintDiffusion remains because SD2.0's LatentInpaintDiffusion can't be loaded without specifying a checkpoint + # p_sample_plms is needed because PLMS can't work with dicts as conditionings + # this file should be cleaned up later if weverything tuens out to work fine + + # ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion - ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim - ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim + # ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim + # ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms - ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms - + # ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms |