From 6d7ca54a1a9f448419acb31a54c5e28f3e4bcc4c Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 19 Sep 2022 16:42:56 +0300 Subject: added highres fix feature --- modules/sd_samplers.py | 55 +++++++++++++++++++++++++++++--------------------- 1 file changed, 32 insertions(+), 23 deletions(-) (limited to 'modules/sd_samplers.py') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index af6811a1..1fc9d18c 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -38,9 +38,9 @@ samplers = [ samplers_for_img2img = [x for x in samplers if x.name != 'PLMS'] -def setup_img2img_steps(p): - if opts.img2img_fix_steps: - steps = int(p.steps / min(p.denoising_strength, 0.999)) +def setup_img2img_steps(p, steps=None): + if opts.img2img_fix_steps or steps is not None: + steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0 t_enc = p.steps - 1 else: steps = p.steps @@ -115,8 +115,8 @@ class VanillaStableDiffusionSampler: self.step += 1 return res - def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning): - steps, t_enc = setup_img2img_steps(p) + def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None): + steps, t_enc = setup_img2img_steps(p, steps) # existing code fails with cetain step counts, like 9 try: @@ -127,16 +127,16 @@ class VanillaStableDiffusionSampler: x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise) self.sampler.p_sample_ddim = self.p_sample_ddim_hook - self.mask = p.mask - self.nmask = p.nmask - self.init_latent = p.init_latent + self.mask = p.mask if hasattr(p, 'mask') else None + self.nmask = p.nmask if hasattr(p, 'nmask') else None + self.init_latent = x self.step = 0 samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning) return samples - def sample(self, p, x, conditioning, unconditional_conditioning): + def sample(self, p, x, conditioning, unconditional_conditioning, steps=None): for fieldname in ['p_sample_ddim', 'p_sample_plms']: if hasattr(self.sampler, fieldname): setattr(self.sampler, fieldname, self.p_sample_ddim_hook) @@ -145,11 +145,13 @@ class VanillaStableDiffusionSampler: self.init_latent = None self.step = 0 + steps = steps or p.steps + # existing code fails with cetin step counts, like 9 try: - samples_ddim, _ = self.sampler.sample(S=p.steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x) + samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x) except Exception: - samples_ddim, _ = self.sampler.sample(S=p.steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x) + samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x) return samples_ddim @@ -186,7 +188,7 @@ class CFGDenoiser(torch.nn.Module): return denoised -def extended_trange(count, *args, **kwargs): +def extended_trange(sampler, count, *args, **kwargs): state.sampling_steps = count state.sampling_step = 0 @@ -194,6 +196,9 @@ def extended_trange(count, *args, **kwargs): if state.interrupted: break + if sampler.stop_at is not None and x > sampler.stop_at: + break + yield x state.sampling_step += 1 @@ -222,6 +227,7 @@ class KDiffusionSampler: self.model_wrap_cfg = CFGDenoiser(self.model_wrap) self.sampler_noises = None self.sampler_noise_index = 0 + self.stop_at = None def callback_state(self, d): store_latent(d["denoised"]) @@ -240,8 +246,8 @@ class KDiffusionSampler: self.sampler_noise_index += 1 return res - def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning): - steps, t_enc = setup_img2img_steps(p) + def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None): + steps, t_enc = setup_img2img_steps(p, steps) sigmas = self.model_wrap.get_sigmas(steps) @@ -251,33 +257,36 @@ class KDiffusionSampler: sigma_sched = sigmas[steps - t_enc - 1:] - self.model_wrap_cfg.mask = p.mask - self.model_wrap_cfg.nmask = p.nmask - self.model_wrap_cfg.init_latent = p.init_latent + 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.init_latent = x self.model_wrap.step = 0 self.sampler_noise_index = 0 if hasattr(k_diffusion.sampling, 'trange'): - k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs) + k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs) if self.sampler_noises is not None: k_diffusion.sampling.torch = TorchHijack(self) return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state) - def sample(self, p, x, conditioning, unconditional_conditioning): - sigmas = self.model_wrap.get_sigmas(p.steps) + def sample(self, p, x, conditioning, unconditional_conditioning, steps=None): + steps = steps or p.steps + + sigmas = self.model_wrap.get_sigmas(steps) x = x * sigmas[0] self.model_wrap_cfg.step = 0 self.sampler_noise_index = 0 if hasattr(k_diffusion.sampling, 'trange'): - k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs) + k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs) if self.sampler_noises is not None: k_diffusion.sampling.torch = TorchHijack(self) - samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state) - return samples_ddim + samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state) + + return samples -- cgit v1.2.3