From 40a18d38a8fcb88d1c2947a2653b52cd2085536f Mon Sep 17 00:00:00 2001 From: lambertae Date: Tue, 18 Jul 2023 00:32:01 -0400 Subject: add restart sampler --- modules/sd_samplers_kdiffusion.py | 70 +++++++++++++++++++++++++++++++++++++-- 1 file changed, 68 insertions(+), 2 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 71581b76..c63b677c 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -1,3 +1,5 @@ +# export PIP_CACHE_DIR=/scratch/dengm/cache +# export XDG_CACHE_HOME=/scratch/dengm/cache from collections import deque import torch import inspect @@ -30,12 +32,76 @@ samplers_k_diffusion = [ ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}), ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}), ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}), + ('Restart (new)', 'restart_sampler', ['restart'], {'scheduler': 'karras', "second_order": True}), ] + +@torch.no_grad() +def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = {0.1: [10, 2, 2]}): + """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)""" + '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}''' + + from tqdm.auto import trange, tqdm + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + step_id = 0 + + from k_diffusion.sampling import to_d, append_zero + + def heun_step(x, old_sigma, new_sigma): + nonlocal step_id + denoised = model(x, old_sigma * s_in, **extra_args) + d = to_d(x, old_sigma, denoised) + if callback is not None: + callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised}) + dt = new_sigma - old_sigma + if new_sigma == 0: + # Euler method + x = x + d * dt + else: + # Heun's method + x_2 = x + d * dt + denoised_2 = model(x_2, new_sigma * s_in, **extra_args) + d_2 = to_d(x_2, new_sigma, denoised_2) + d_prime = (d + d_2) / 2 + x = x + d_prime * dt + step_id += 1 + return x + # print(sigmas) + temp_list = dict() + for key, value in restart_list.items(): + temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value + restart_list = temp_list + + + def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'): + ramp = torch.linspace(0, 1, n).to(device) + min_inv_rho = (sigma_min ** (1 / rho)) + max_inv_rho = (sigma_max ** (1 / rho)) + if isinstance(min_inv_rho, torch.Tensor): + min_inv_rho = min_inv_rho.to(device) + if isinstance(max_inv_rho, torch.Tensor): + max_inv_rho = max_inv_rho.to(device) + sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho + return append_zero(sigmas).to(device) + + for i in trange(len(sigmas) - 1, disable=disable): + x = heun_step(x, sigmas[i], sigmas[i+1]) + if i + 1 in restart_list: + restart_steps, restart_times, restart_max = restart_list[i + 1] + min_idx = i + 1 + max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0)) + sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end + for times in range(restart_times): + x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5 + for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]): + x = heun_step(x, old_sigma, new_sigma) + return x + samplers_data_k_diffusion = [ sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options) for label, funcname, aliases, options in samplers_k_diffusion - if hasattr(k_diffusion.sampling, funcname) + if (hasattr(k_diffusion.sampling, funcname) or funcname == 'restart_sampler') ] sampler_extra_params = { @@ -245,7 +311,7 @@ class KDiffusionSampler: self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization) self.funcname = funcname - self.func = getattr(k_diffusion.sampling, self.funcname) + self.func = getattr(k_diffusion.sampling, self.funcname) if funcname != "restart_sampler" else restart_sampler self.extra_params = sampler_extra_params.get(funcname, []) self.model_wrap_cfg = CFGDenoiser(self.model_wrap) self.sampler_noises = None -- cgit v1.2.3 From 15a94d6cf7fa075c09362e73c1239692d021c559 Mon Sep 17 00:00:00 2001 From: lambertae Date: Tue, 18 Jul 2023 00:39:26 -0400 Subject: remove useless header --- modules/sd_samplers_kdiffusion.py | 2 -- 1 file changed, 2 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index c63b677c..7888d864 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -1,5 +1,3 @@ -# export PIP_CACHE_DIR=/scratch/dengm/cache -# export XDG_CACHE_HOME=/scratch/dengm/cache from collections import deque import torch import inspect -- cgit v1.2.3 From 37e048a7e2356f4caebfd976351112f03856f082 Mon Sep 17 00:00:00 2001 From: lambertae Date: Tue, 18 Jul 2023 00:55:02 -0400 Subject: fix floating error --- modules/sd_samplers_kdiffusion.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 7888d864..1bb25adf 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -89,11 +89,12 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No restart_steps, restart_times, restart_max = restart_list[i + 1] min_idx = i + 1 max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0)) - sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end - for times in range(restart_times): - x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5 - for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]): - x = heun_step(x, old_sigma, new_sigma) + if max_idx < min_idx: + sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end + for times in range(restart_times): + x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5 + for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]): + x = heun_step(x, old_sigma, new_sigma) return x samplers_data_k_diffusion = [ -- cgit v1.2.3 From 7bb0fbed136c6a345b211e09102659fd89362576 Mon Sep 17 00:00:00 2001 From: lambertae Date: Tue, 18 Jul 2023 01:02:04 -0400 Subject: code styling --- modules/sd_samplers_kdiffusion.py | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 1bb25adf..db7013f2 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -35,17 +35,15 @@ samplers_k_diffusion = [ @torch.no_grad() -def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = {0.1: [10, 2, 2]}): +def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.): """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)""" '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}''' - - from tqdm.auto import trange, tqdm + restart_list = {0.1: [10, 2, 2]} + from tqdm.auto import trange extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) step_id = 0 - from k_diffusion.sampling import to_d, append_zero - def heun_step(x, old_sigma, new_sigma): nonlocal step_id denoised = model(x, old_sigma * s_in, **extra_args) @@ -70,8 +68,6 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No for key, value in restart_list.items(): temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value restart_list = temp_list - - def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'): ramp = torch.linspace(0, 1, n).to(device) min_inv_rho = (sigma_min ** (1 / rho)) @@ -82,7 +78,6 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No max_inv_rho = max_inv_rho.to(device) sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho return append_zero(sigmas).to(device) - for i in trange(len(sigmas) - 1, disable=disable): x = heun_step(x, sigmas[i], sigmas[i+1]) if i + 1 in restart_list: @@ -91,7 +86,8 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0)) if max_idx < min_idx: sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end - for times in range(restart_times): + while restart_times > 0: + restart_times -= 1 x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5 for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]): x = heun_step(x, old_sigma, new_sigma) -- cgit v1.2.3 From ddbf4a73f5c0cfe63ca0988b8e642d3b977a3fa9 Mon Sep 17 00:00:00 2001 From: lambertae Date: Thu, 20 Jul 2023 02:24:18 -0400 Subject: restart-sampler with correct steps --- modules/sd_samplers_kdiffusion.py | 22 ++++++++++++++-------- 1 file changed, 14 insertions(+), 8 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index db7013f2..ed5e6c79 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -38,20 +38,19 @@ samplers_k_diffusion = [ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.): """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)""" '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}''' - restart_list = {0.1: [10, 2, 2]} from tqdm.auto import trange extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) step_id = 0 from k_diffusion.sampling import to_d, append_zero - def heun_step(x, old_sigma, new_sigma): + def heun_step(x, old_sigma, new_sigma, second_order = True): nonlocal step_id denoised = model(x, old_sigma * s_in, **extra_args) d = to_d(x, old_sigma, denoised) if callback is not None: callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised}) dt = new_sigma - old_sigma - if new_sigma == 0: + if new_sigma == 0 or not second_order: # Euler method x = x + d * dt else: @@ -63,11 +62,6 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No x = x + d_prime * dt step_id += 1 return x - # print(sigmas) - temp_list = dict() - for key, value in restart_list.items(): - temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value - restart_list = temp_list def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'): ramp = torch.linspace(0, 1, n).to(device) min_inv_rho = (sigma_min ** (1 / rho)) @@ -78,6 +72,18 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No max_inv_rho = max_inv_rho.to(device) sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho return append_zero(sigmas).to(device) + steps = sigmas.shape[0] - 1 + if steps >= 20: + restart_steps = 9 + restart_times = 2 if steps >= 36 else 1 + sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2], sigmas[0], device=sigmas.device) + restart_list = {0.1: [restart_steps + 1, restart_times, 2]} + else: + restart_list = dict() + temp_list = dict() + for key, value in restart_list.items(): + temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value + restart_list = temp_list for i in trange(len(sigmas) - 1, disable=disable): x = heun_step(x, sigmas[i], sigmas[i+1]) if i + 1 in restart_list: -- cgit v1.2.3 From 2f57a559ac3381c1ef2516655c3a3d1088191c54 Mon Sep 17 00:00:00 2001 From: lambertae Date: Thu, 20 Jul 2023 20:34:41 -0400 Subject: allow choise of restart_list & use karras from kdiffusion --- modules/sd_samplers_kdiffusion.py | 32 ++++++++++++-------------------- 1 file changed, 12 insertions(+), 20 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index ed5e6c79..c72d01c8 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -35,14 +35,15 @@ samplers_k_diffusion = [ @torch.no_grad() -def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.): +def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = None): """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)""" '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}''' + '''If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list''' from tqdm.auto import trange extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) step_id = 0 - from k_diffusion.sampling import to_d, append_zero + from k_diffusion.sampling import to_d, append_zero, get_sigmas_karras def heun_step(x, old_sigma, new_sigma, second_order = True): nonlocal step_id denoised = model(x, old_sigma * s_in, **extra_args) @@ -62,24 +63,15 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No x = x + d_prime * dt step_id += 1 return x - def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'): - ramp = torch.linspace(0, 1, n).to(device) - min_inv_rho = (sigma_min ** (1 / rho)) - max_inv_rho = (sigma_max ** (1 / rho)) - if isinstance(min_inv_rho, torch.Tensor): - min_inv_rho = min_inv_rho.to(device) - if isinstance(max_inv_rho, torch.Tensor): - max_inv_rho = max_inv_rho.to(device) - sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho - return append_zero(sigmas).to(device) steps = sigmas.shape[0] - 1 - if steps >= 20: - restart_steps = 9 - restart_times = 2 if steps >= 36 else 1 - sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2], sigmas[0], device=sigmas.device) - restart_list = {0.1: [restart_steps + 1, restart_times, 2]} - else: - restart_list = dict() + if restart_list is None: + if steps >= 20: + restart_steps = 9 + restart_times = 2 if steps >= 36 else 1 + sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device) + restart_list = {0.1: [restart_steps + 1, restart_times, 2]} + else: + restart_list = dict() temp_list = dict() for key, value in restart_list.items(): temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value @@ -91,7 +83,7 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No min_idx = i + 1 max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0)) if max_idx < min_idx: - sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end + sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1] # remove the zero at the end while restart_times > 0: restart_times -= 1 x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5 -- cgit v1.2.3 From 128d59c9ccfbc9c7fccd6f1b2fe58bbbb18459f9 Mon Sep 17 00:00:00 2001 From: lambertae Date: Thu, 20 Jul 2023 20:36:40 -0400 Subject: fix ruff --- modules/sd_samplers_kdiffusion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index c72d01c8..21b347ed 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -43,7 +43,7 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) step_id = 0 - from k_diffusion.sampling import to_d, append_zero, get_sigmas_karras + from k_diffusion.sampling import to_d, get_sigmas_karras def heun_step(x, old_sigma, new_sigma, second_order = True): nonlocal step_id denoised = model(x, old_sigma * s_in, **extra_args) -- cgit v1.2.3 From f87389029839a27464a18846815339e81787b882 Mon Sep 17 00:00:00 2001 From: lambertae Date: Thu, 20 Jul 2023 21:27:43 -0400 Subject: new restart scheme --- modules/sd_samplers_kdiffusion.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 21b347ed..ed60670c 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -67,7 +67,10 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No if restart_list is None: if steps >= 20: restart_steps = 9 - restart_times = 2 if steps >= 36 else 1 + restart_times = 1 + if steps >= 36: + restart_steps = steps // 4 + restart_times = 2 sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device) restart_list = {0.1: [restart_steps + 1, restart_times, 2]} else: -- cgit v1.2.3 From 8de6d3ff77e841a5fd9d5f1b16bdd22737c8d657 Mon Sep 17 00:00:00 2001 From: lambertae Date: Tue, 25 Jul 2023 22:35:43 -0400 Subject: fix progress bar & torchHijack --- modules/sd_samplers_kdiffusion.py | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index ed60670c..7a2427b5 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -79,19 +79,26 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No for key, value in restart_list.items(): temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value restart_list = temp_list - for i in trange(len(sigmas) - 1, disable=disable): - x = heun_step(x, sigmas[i], sigmas[i+1]) + step_list = [] + for i in range(len(sigmas) - 1): + step_list.append((sigmas[i], sigmas[i + 1])) if i + 1 in restart_list: restart_steps, restart_times, restart_max = restart_list[i + 1] min_idx = i + 1 max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0)) if max_idx < min_idx: - sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1] # remove the zero at the end + sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1] while restart_times > 0: restart_times -= 1 - x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5 - for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]): - x = heun_step(x, old_sigma, new_sigma) + step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])]) + last_sigma = None + for i in trange(len(step_list), disable=disable): + if last_sigma is None: + last_sigma = step_list[i][0] + elif last_sigma < step_list[i][0]: + x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (step_list[i][0] ** 2 - last_sigma ** 2) ** 0.5 + x = heun_step(x, step_list[i][0], step_list[i][1]) + last_sigma = step_list[i][1] return x samplers_data_k_diffusion = [ -- cgit v1.2.3 From e1323fc1b70438165bbfd90d80dec00b8b2ab884 Mon Sep 17 00:00:00 2001 From: AUTOMATIC1111 <16777216c@gmail.com> Date: Sat, 29 Jul 2023 08:06:03 +0300 Subject: Split history: mv modules/sd_samplers_kdiffusion.py temp --- modules/sd_samplers_kdiffusion.py | 545 -------------------------------------- 1 file changed, 545 deletions(-) delete mode 100644 modules/sd_samplers_kdiffusion.py (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py deleted file mode 100644 index a54673eb..00000000 --- a/modules/sd_samplers_kdiffusion.py +++ /dev/null @@ -1,545 +0,0 @@ -from collections import deque -import torch -import inspect -import k_diffusion.sampling -from modules import prompt_parser, devices, sd_samplers_common - -from modules.shared import opts, state -import modules.shared as shared -from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback -from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback -from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback - -samplers_k_diffusion = [ - ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}), - ('Euler', 'sample_euler', ['k_euler'], {}), - ('LMS', 'sample_lms', ['k_lms'], {}), - ('Heun', 'sample_heun', ['k_heun'], {"second_order": True}), - ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}), - ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}), - ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}), - ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}), - ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}), - ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}), - ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}), - ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}), - ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}), - ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}), - ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}), - ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}), - ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}), - ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}), - ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}), - ('Restart (new)', 'restart_sampler', ['restart'], {'scheduler': 'karras', "second_order": True}), -] - - -@torch.no_grad() -def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = None): - """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)""" - '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}''' - '''If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list''' - from tqdm.auto import trange - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - step_id = 0 - from k_diffusion.sampling import to_d, get_sigmas_karras - def heun_step(x, old_sigma, new_sigma, second_order = True): - nonlocal step_id - denoised = model(x, old_sigma * s_in, **extra_args) - d = to_d(x, old_sigma, denoised) - if callback is not None: - callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised}) - dt = new_sigma - old_sigma - if new_sigma == 0 or not second_order: - # Euler method - x = x + d * dt - else: - # Heun's method - x_2 = x + d * dt - denoised_2 = model(x_2, new_sigma * s_in, **extra_args) - d_2 = to_d(x_2, new_sigma, denoised_2) - d_prime = (d + d_2) / 2 - x = x + d_prime * dt - step_id += 1 - return x - steps = sigmas.shape[0] - 1 - if restart_list is None: - if steps >= 20: - restart_steps = 9 - restart_times = 1 - if steps >= 36: - restart_steps = steps // 4 - restart_times = 2 - sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device) - restart_list = {0.1: [restart_steps + 1, restart_times, 2]} - else: - restart_list = dict() - temp_list = dict() - for key, value in restart_list.items(): - temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value - restart_list = temp_list - step_list = [] - for i in range(len(sigmas) - 1): - step_list.append((sigmas[i], sigmas[i + 1])) - if i + 1 in restart_list: - restart_steps, restart_times, restart_max = restart_list[i + 1] - min_idx = i + 1 - max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0)) - if max_idx < min_idx: - sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1] - while restart_times > 0: - restart_times -= 1 - step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])]) - last_sigma = None - for i in trange(len(step_list), disable=disable): - if last_sigma is None: - last_sigma = step_list[i][0] - elif last_sigma < step_list[i][0]: - x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (step_list[i][0] ** 2 - last_sigma ** 2) ** 0.5 - x = heun_step(x, step_list[i][0], step_list[i][1]) - last_sigma = step_list[i][1] - return x - -samplers_data_k_diffusion = [ - sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options) - for label, funcname, aliases, options in samplers_k_diffusion - if (hasattr(k_diffusion.sampling, funcname) or funcname == 'restart_sampler') -] - -sampler_extra_params = { - 'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'], - 'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'], - 'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'], -} - -k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion} -k_diffusion_scheduler = { - 'Automatic': None, - 'karras': k_diffusion.sampling.get_sigmas_karras, - 'exponential': k_diffusion.sampling.get_sigmas_exponential, - 'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential -} - - -def catenate_conds(conds): - if not isinstance(conds[0], dict): - return torch.cat(conds) - - return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()} - - -def subscript_cond(cond, a, b): - if not isinstance(cond, dict): - return cond[a:b] - - return {key: vec[a:b] for key, vec in cond.items()} - - -def pad_cond(tensor, repeats, empty): - if not isinstance(tensor, dict): - return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1) - - tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty) - return tensor - - -class CFGDenoiser(torch.nn.Module): - """ - Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet) - that can take a noisy picture and produce a noise-free picture using two guidances (prompts) - instead of one. Originally, the second prompt is just an empty string, but we use non-empty - negative prompt. - """ - - def __init__(self, model): - super().__init__() - self.inner_model = model - self.mask = None - self.nmask = None - self.init_latent = None - self.step = 0 - self.image_cfg_scale = None - self.padded_cond_uncond = False - - def combine_denoised(self, x_out, conds_list, uncond, cond_scale): - denoised_uncond = x_out[-uncond.shape[0]:] - denoised = torch.clone(denoised_uncond) - - for i, conds in enumerate(conds_list): - for cond_index, weight in conds: - denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale) - - return denoised - - def combine_denoised_for_edit_model(self, x_out, cond_scale): - out_cond, out_img_cond, out_uncond = x_out.chunk(3) - denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond) - - return denoised - - 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 - - # 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 - - conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) - uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step) - - assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)" - - batch_size = len(conds_list) - repeats = [len(conds_list[i]) for i in range(batch_size)] - - if shared.sd_model.model.conditioning_key == "crossattn-adm": - image_uncond = torch.zeros_like(image_cond) - make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm} - else: - image_uncond = image_cond - if isinstance(uncond, dict): - make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]} - else: - make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]} - - if not is_edit_model: - x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x]) - sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma]) - image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond]) - else: - x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x]) - sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma]) - image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)]) - - denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond) - cfg_denoiser_callback(denoiser_params) - x_in = denoiser_params.x - image_cond_in = denoiser_params.image_cond - sigma_in = denoiser_params.sigma - tensor = denoiser_params.text_cond - uncond = denoiser_params.text_uncond - skip_uncond = False - - # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it - if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model: - skip_uncond = True - x_in = x_in[:-batch_size] - sigma_in = sigma_in[:-batch_size] - - self.padded_cond_uncond = False - if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]: - empty = shared.sd_model.cond_stage_model_empty_prompt - num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1] - - if num_repeats < 0: - tensor = pad_cond(tensor, -num_repeats, empty) - self.padded_cond_uncond = True - elif num_repeats > 0: - uncond = pad_cond(uncond, num_repeats, empty) - self.padded_cond_uncond = True - - if tensor.shape[1] == uncond.shape[1] or skip_uncond: - if is_edit_model: - cond_in = catenate_conds([tensor, uncond, uncond]) - elif skip_uncond: - cond_in = tensor - else: - cond_in = catenate_conds([tensor, uncond]) - - if shared.batch_cond_uncond: - x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in)) - else: - x_out = torch.zeros_like(x_in) - for batch_offset in range(0, x_out.shape[0], batch_size): - a = batch_offset - b = a + batch_size - x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b])) - else: - x_out = torch.zeros_like(x_in) - batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size - for batch_offset in range(0, tensor.shape[0], batch_size): - a = batch_offset - b = min(a + batch_size, tensor.shape[0]) - - if not is_edit_model: - c_crossattn = subscript_cond(tensor, a, b) - else: - c_crossattn = torch.cat([tensor[a:b]], uncond) - - 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 not skip_uncond: - 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_image_indexes = [x[0][0] for x in conds_list] - if skip_uncond: - fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes]) - x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be - - denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model) - cfg_denoised_callback(denoised_params) - - devices.test_for_nans(x_out, "unet") - - if opts.live_preview_content == "Prompt": - sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes])) - elif opts.live_preview_content == "Negative prompt": - sd_samplers_common.store_latent(x_out[-uncond.shape[0]:]) - - if is_edit_model: - denoised = self.combine_denoised_for_edit_model(x_out, cond_scale) - elif skip_uncond: - denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0) - else: - denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale) - - if self.mask is not None: - denoised = self.init_latent * self.mask + self.nmask * denoised - - after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps) - cfg_after_cfg_callback(after_cfg_callback_params) - denoised = after_cfg_callback_params.x - - self.step += 1 - return denoised - - -class TorchHijack: - def __init__(self, sampler_noises): - # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based - # implementation. - self.sampler_noises = deque(sampler_noises) - - def __getattr__(self, item): - if item == 'randn_like': - return self.randn_like - - if hasattr(torch, item): - return getattr(torch, item) - - raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'") - - def randn_like(self, x): - if self.sampler_noises: - noise = self.sampler_noises.popleft() - if noise.shape == x.shape: - return noise - - if opts.randn_source == "CPU" or x.device.type == 'mps': - return torch.randn_like(x, device=devices.cpu).to(x.device) - else: - return torch.randn_like(x) - - -class KDiffusionSampler: - def __init__(self, funcname, sd_model): - 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.funcname = funcname - self.func = getattr(k_diffusion.sampling, self.funcname) if funcname != "restart_sampler" else restart_sampler - self.extra_params = sampler_extra_params.get(funcname, []) - self.model_wrap_cfg = CFGDenoiser(self.model_wrap) - self.sampler_noises = None - self.stop_at = None - self.eta = None - self.config = None # set by the function calling the constructor - self.last_latent = None - self.s_min_uncond = None - - self.conditioning_key = sd_model.model.conditioning_key - - def callback_state(self, d): - step = d['i'] - latent = d["denoised"] - if opts.live_preview_content == "Combined": - sd_samplers_common.store_latent(latent) - self.last_latent = latent - - if self.stop_at is not None and step > self.stop_at: - raise sd_samplers_common.InterruptedException - - state.sampling_step = step - shared.total_tqdm.update() - - def launch_sampling(self, steps, func): - state.sampling_steps = steps - state.sampling_step = 0 - - try: - return func() - except RecursionError: - print( - 'Encountered RecursionError during sampling, returning last latent. ' - 'rho >5 with a polyexponential scheduler may cause this error. ' - 'You should try to use a smaller rho value instead.' - ) - return self.last_latent - except sd_samplers_common.InterruptedException: - return self.last_latent - - def number_of_needed_noises(self, p): - return p.steps - - def initialize(self, 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 - 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 []) - - extra_params_kwargs = {} - for param_name in self.extra_params: - if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters: - extra_params_kwargs[param_name] = getattr(p, param_name) - - if 'eta' in inspect.signature(self.func).parameters: - if self.eta != 1.0: - p.extra_generation_params["Eta"] = self.eta - - extra_params_kwargs['eta'] = self.eta - - return extra_params_kwargs - - 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) - if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma: - discard_next_to_last_sigma = True - p.extra_generation_params["Discard penultimate sigma"] = True - - steps += 1 if discard_next_to_last_sigma else 0 - - if p.sampler_noise_scheduler_override: - sigmas = p.sampler_noise_scheduler_override(steps) - elif opts.k_sched_type != "Automatic": - m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item()) - sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max) - sigmas_kwargs = { - 'sigma_min': sigma_min, - 'sigma_max': sigma_max, - } - - sigmas_func = k_diffusion_scheduler[opts.k_sched_type] - p.extra_generation_params["Schedule type"] = opts.k_sched_type - - if opts.sigma_min != m_sigma_min and opts.sigma_min != 0: - sigmas_kwargs['sigma_min'] = opts.sigma_min - p.extra_generation_params["Schedule min sigma"] = opts.sigma_min - if opts.sigma_max != m_sigma_max and opts.sigma_max != 0: - sigmas_kwargs['sigma_max'] = opts.sigma_max - p.extra_generation_params["Schedule max sigma"] = opts.sigma_max - - default_rho = 1. if opts.k_sched_type == "polyexponential" else 7. - - if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho: - sigmas_kwargs['rho'] = opts.rho - p.extra_generation_params["Schedule rho"] = opts.rho - - sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device) - elif self.config is not None and self.config.options.get('scheduler', None) == 'karras': - sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item()) - - sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device) - else: - sigmas = self.model_wrap.get_sigmas(steps) - - if discard_next_to_last_sigma: - sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) - - return sigmas - - def create_noise_sampler(self, x, sigmas, p): - """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes""" - if shared.opts.no_dpmpp_sde_batch_determinism: - return None - - from k_diffusion.sampling import BrownianTreeNoiseSampler - sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() - current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size] - return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds) - - def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None): - steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps) - - sigmas = self.get_sigmas(p, steps) - - sigma_sched = sigmas[steps - t_enc - 1:] - xi = x + noise * sigma_sched[0] - - extra_params_kwargs = self.initialize(p) - parameters = inspect.signature(self.func).parameters - - if 'sigma_min' in parameters: - ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last - extra_params_kwargs['sigma_min'] = sigma_sched[-2] - if 'sigma_max' in parameters: - extra_params_kwargs['sigma_max'] = sigma_sched[0] - if 'n' in parameters: - extra_params_kwargs['n'] = len(sigma_sched) - 1 - if 'sigma_sched' in parameters: - extra_params_kwargs['sigma_sched'] = sigma_sched - if 'sigmas' in parameters: - extra_params_kwargs['sigmas'] = sigma_sched - - if self.config.options.get('brownian_noise', False): - noise_sampler = self.create_noise_sampler(x, sigmas, p) - extra_params_kwargs['noise_sampler'] = noise_sampler - - self.model_wrap_cfg.init_latent = x - self.last_latent = x - extra_args = { - 'cond': conditioning, - '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)) - - if self.model_wrap_cfg.padded_cond_uncond: - p.extra_generation_params["Pad conds"] = True - - return samples - - def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None): - steps = steps or p.steps - - sigmas = self.get_sigmas(p, steps) - - x = x * sigmas[0] - - extra_params_kwargs = self.initialize(p) - parameters = inspect.signature(self.func).parameters - - if 'sigma_min' in parameters: - extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item() - extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item() - if 'n' in parameters: - extra_params_kwargs['n'] = steps - else: - extra_params_kwargs['sigmas'] = sigmas - - if self.config.options.get('brownian_noise', False): - noise_sampler = self.create_noise_sampler(x, sigmas, p) - extra_params_kwargs['noise_sampler'] = noise_sampler - - self.last_latent = x - samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, 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)) - - if self.model_wrap_cfg.padded_cond_uncond: - p.extra_generation_params["Pad conds"] = True - - return samples - -- cgit v1.2.3 From 11dc92dc0ae8fe477539583e6a743e57a7cd69ad Mon Sep 17 00:00:00 2001 From: AUTOMATIC1111 <16777216c@gmail.com> Date: Sat, 29 Jul 2023 08:06:04 +0300 Subject: Split history: mv temp modules/sd_samplers_kdiffusion.py --- modules/sd_samplers_kdiffusion.py | 545 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 545 insertions(+) create mode 100644 modules/sd_samplers_kdiffusion.py (limited to 'modules/sd_samplers_kdiffusion.py') diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py new file mode 100644 index 00000000..a54673eb --- /dev/null +++ b/modules/sd_samplers_kdiffusion.py @@ -0,0 +1,545 @@ +from collections import deque +import torch +import inspect +import k_diffusion.sampling +from modules import prompt_parser, devices, sd_samplers_common + +from modules.shared import opts, state +import modules.shared as shared +from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback +from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback +from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback + +samplers_k_diffusion = [ + ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}), + ('Euler', 'sample_euler', ['k_euler'], {}), + ('LMS', 'sample_lms', ['k_lms'], {}), + ('Heun', 'sample_heun', ['k_heun'], {"second_order": True}), + ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}), + ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}), + ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}), + ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}), + ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}), + ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}), + ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}), + ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}), + ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}), + ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}), + ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}), + ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}), + ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}), + ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}), + ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}), + ('Restart (new)', 'restart_sampler', ['restart'], {'scheduler': 'karras', "second_order": True}), +] + + +@torch.no_grad() +def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = None): + """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)""" + '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}''' + '''If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list''' + from tqdm.auto import trange + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + step_id = 0 + from k_diffusion.sampling import to_d, get_sigmas_karras + def heun_step(x, old_sigma, new_sigma, second_order = True): + nonlocal step_id + denoised = model(x, old_sigma * s_in, **extra_args) + d = to_d(x, old_sigma, denoised) + if callback is not None: + callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised}) + dt = new_sigma - old_sigma + if new_sigma == 0 or not second_order: + # Euler method + x = x + d * dt + else: + # Heun's method + x_2 = x + d * dt + denoised_2 = model(x_2, new_sigma * s_in, **extra_args) + d_2 = to_d(x_2, new_sigma, denoised_2) + d_prime = (d + d_2) / 2 + x = x + d_prime * dt + step_id += 1 + return x + steps = sigmas.shape[0] - 1 + if restart_list is None: + if steps >= 20: + restart_steps = 9 + restart_times = 1 + if steps >= 36: + restart_steps = steps // 4 + restart_times = 2 + sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device) + restart_list = {0.1: [restart_steps + 1, restart_times, 2]} + else: + restart_list = dict() + temp_list = dict() + for key, value in restart_list.items(): + temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value + restart_list = temp_list + step_list = [] + for i in range(len(sigmas) - 1): + step_list.append((sigmas[i], sigmas[i + 1])) + if i + 1 in restart_list: + restart_steps, restart_times, restart_max = restart_list[i + 1] + min_idx = i + 1 + max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0)) + if max_idx < min_idx: + sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1] + while restart_times > 0: + restart_times -= 1 + step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])]) + last_sigma = None + for i in trange(len(step_list), disable=disable): + if last_sigma is None: + last_sigma = step_list[i][0] + elif last_sigma < step_list[i][0]: + x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (step_list[i][0] ** 2 - last_sigma ** 2) ** 0.5 + x = heun_step(x, step_list[i][0], step_list[i][1]) + last_sigma = step_list[i][1] + return x + +samplers_data_k_diffusion = [ + sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options) + for label, funcname, aliases, options in samplers_k_diffusion + if (hasattr(k_diffusion.sampling, funcname) or funcname == 'restart_sampler') +] + +sampler_extra_params = { + 'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'], + 'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'], + 'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'], +} + +k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion} +k_diffusion_scheduler = { + 'Automatic': None, + 'karras': k_diffusion.sampling.get_sigmas_karras, + 'exponential': k_diffusion.sampling.get_sigmas_exponential, + 'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential +} + + +def catenate_conds(conds): + if not isinstance(conds[0], dict): + return torch.cat(conds) + + return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()} + + +def subscript_cond(cond, a, b): + if not isinstance(cond, dict): + return cond[a:b] + + return {key: vec[a:b] for key, vec in cond.items()} + + +def pad_cond(tensor, repeats, empty): + if not isinstance(tensor, dict): + return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1) + + tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty) + return tensor + + +class CFGDenoiser(torch.nn.Module): + """ + Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet) + that can take a noisy picture and produce a noise-free picture using two guidances (prompts) + instead of one. Originally, the second prompt is just an empty string, but we use non-empty + negative prompt. + """ + + def __init__(self, model): + super().__init__() + self.inner_model = model + self.mask = None + self.nmask = None + self.init_latent = None + self.step = 0 + self.image_cfg_scale = None + self.padded_cond_uncond = False + + def combine_denoised(self, x_out, conds_list, uncond, cond_scale): + denoised_uncond = x_out[-uncond.shape[0]:] + denoised = torch.clone(denoised_uncond) + + for i, conds in enumerate(conds_list): + for cond_index, weight in conds: + denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale) + + return denoised + + def combine_denoised_for_edit_model(self, x_out, cond_scale): + out_cond, out_img_cond, out_uncond = x_out.chunk(3) + denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond) + + return denoised + + 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 + + # 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 + + conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) + uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step) + + assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)" + + batch_size = len(conds_list) + repeats = [len(conds_list[i]) for i in range(batch_size)] + + if shared.sd_model.model.conditioning_key == "crossattn-adm": + image_uncond = torch.zeros_like(image_cond) + make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm} + else: + image_uncond = image_cond + if isinstance(uncond, dict): + make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]} + else: + make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]} + + if not is_edit_model: + x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x]) + sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma]) + image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond]) + else: + x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x]) + sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma]) + image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)]) + + denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond) + cfg_denoiser_callback(denoiser_params) + x_in = denoiser_params.x + image_cond_in = denoiser_params.image_cond + sigma_in = denoiser_params.sigma + tensor = denoiser_params.text_cond + uncond = denoiser_params.text_uncond + skip_uncond = False + + # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it + if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model: + skip_uncond = True + x_in = x_in[:-batch_size] + sigma_in = sigma_in[:-batch_size] + + self.padded_cond_uncond = False + if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]: + empty = shared.sd_model.cond_stage_model_empty_prompt + num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1] + + if num_repeats < 0: + tensor = pad_cond(tensor, -num_repeats, empty) + self.padded_cond_uncond = True + elif num_repeats > 0: + uncond = pad_cond(uncond, num_repeats, empty) + self.padded_cond_uncond = True + + if tensor.shape[1] == uncond.shape[1] or skip_uncond: + if is_edit_model: + cond_in = catenate_conds([tensor, uncond, uncond]) + elif skip_uncond: + cond_in = tensor + else: + cond_in = catenate_conds([tensor, uncond]) + + if shared.batch_cond_uncond: + x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))