github bot update + heunpp2
This commit is contained in:
lllyasviel
parent
861c8d38df
commit
a9bd188555
@ -750,3 +750,61 @@ def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, n
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if sigmas[i + 1] > 0:
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x += sigmas[i + 1] * noise_sampler(sigmas[i], sigmas[i + 1])
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return x
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@torch.no_grad()
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def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
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# From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/
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extra_args = {} if extra_args is None else extra_args
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s_in = x.new_ones([x.shape[0]])
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s_end = sigmas[-1]
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for i in trange(len(sigmas) - 1, disable=disable):
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gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
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eps = torch.randn_like(x) * s_noise
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sigma_hat = sigmas[i] * (gamma + 1)
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if gamma > 0:
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x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
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denoised = model(x, sigma_hat * s_in, **extra_args)
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d = to_d(x, sigma_hat, denoised)
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if callback is not None:
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callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
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dt = sigmas[i + 1] - sigma_hat
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if sigmas[i + 1] == s_end:
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# Euler method
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x = x + d * dt
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elif sigmas[i + 2] == s_end:
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# Heun's method
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x_2 = x + d * dt
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denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
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d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
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w = 2 * sigmas[0]
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w2 = sigmas[i+1]/w
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w1 = 1 - w2
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d_prime = d * w1 + d_2 * w2
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x = x + d_prime * dt
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else:
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# Heun++
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x_2 = x + d * dt
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denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
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d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
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dt_2 = sigmas[i + 2] - sigmas[i + 1]
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x_3 = x_2 + d_2 * dt_2
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denoised_3 = model(x_3, sigmas[i + 2] * s_in, **extra_args)
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d_3 = to_d(x_3, sigmas[i + 2], denoised_3)
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w = 3 * sigmas[0]
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w2 = sigmas[i + 1] / w
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w3 = sigmas[i + 2] / w
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w1 = 1 - w2 - w3
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d_prime = w1 * d + w2 * d_2 + w3 * d_3
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x = x + d_prime * dt
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return x
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@ -624,6 +624,11 @@ class UNetModel(nn.Module):
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transformer_options["block"] = ("input", id)
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h = forward_timestep_embed(module, h, emb, context, transformer_options)
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h = apply_control(h, control, 'input')
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if "input_block_patch" in transformer_patches:
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patch = transformer_patches["input_block_patch"]
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for p in patch:
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h = p(h, transformer_options)
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hs.append(h)
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transformer_options["block"] = ("middle", 0)
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@ -96,6 +96,9 @@ class ModelPatcher:
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def set_model_attn2_output_patch(self, patch):
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self.set_model_patch(patch, "attn2_output_patch")
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def set_model_input_block_patch(self, patch):
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self.set_model_patch(patch, "input_block_patch")
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def set_model_output_block_patch(self, patch):
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self.set_model_patch(patch, "output_block_patch")
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@ -518,46 +518,63 @@ class UNIPCBH2(Sampler):
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def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
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return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, variant='bh2', disable=disable_pbar)
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KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
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KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
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"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
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"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"]
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class KSAMPLER(Sampler):
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def __init__(self, sampler_function, extra_options={}, inpaint_options={}):
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self.sampler_function = sampler_function
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self.extra_options = extra_options
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self.inpaint_options = inpaint_options
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def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
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extra_args["denoise_mask"] = denoise_mask
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model_k = KSamplerX0Inpaint(model_wrap)
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model_k.latent_image = latent_image
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if self.inpaint_options.get("random", False): #TODO: Should this be the default?
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generator = torch.manual_seed(extra_args.get("seed", 41) + 1)
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model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device)
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else:
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model_k.noise = noise
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if self.max_denoise(model_wrap, sigmas):
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noise = noise * torch.sqrt(1.0 + sigmas[0] ** 2.0)
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else:
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noise = noise * sigmas[0]
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k_callback = None
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total_steps = len(sigmas) - 1
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if callback is not None:
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k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
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if latent_image is not None:
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noise += latent_image
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samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
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return samples
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def ksampler(sampler_name, extra_options={}, inpaint_options={}):
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class KSAMPLER(Sampler):
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def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
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extra_args["denoise_mask"] = denoise_mask
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model_k = KSamplerX0Inpaint(model_wrap)
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model_k.latent_image = latent_image
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if inpaint_options.get("random", False): #TODO: Should this be the default?
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generator = torch.manual_seed(extra_args.get("seed", 41) + 1)
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model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device)
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else:
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model_k.noise = noise
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if self.max_denoise(model_wrap, sigmas):
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noise = noise * torch.sqrt(1.0 + sigmas[0] ** 2.0)
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else:
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noise = noise * sigmas[0]
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k_callback = None
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total_steps = len(sigmas) - 1
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if callback is not None:
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k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
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if sampler_name == "dpm_fast":
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def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable):
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sigma_min = sigmas[-1]
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if sigma_min == 0:
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sigma_min = sigmas[-2]
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total_steps = len(sigmas) - 1
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return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable)
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sampler_function = dpm_fast_function
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elif sampler_name == "dpm_adaptive":
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def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable):
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sigma_min = sigmas[-1]
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if sigma_min == 0:
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sigma_min = sigmas[-2]
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return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable)
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sampler_function = dpm_adaptive_function
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else:
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sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))
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if latent_image is not None:
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noise += latent_image
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if sampler_name == "dpm_fast":
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samples = k_diffusion_sampling.sample_dpm_fast(model_k, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=k_callback, disable=disable_pbar)
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elif sampler_name == "dpm_adaptive":
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samples = k_diffusion_sampling.sample_dpm_adaptive(model_k, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=k_callback, disable=disable_pbar)
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else:
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samples = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **extra_options)
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return samples
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return KSAMPLER
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return KSAMPLER(sampler_function, extra_options, inpaint_options)
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def wrap_model(model):
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model_denoise = CFGNoisePredictor(model)
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@ -618,11 +635,11 @@ def calculate_sigmas_scheduler(model, scheduler_name, steps):
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print("error invalid scheduler", self.scheduler)
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return sigmas
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def sampler_class(name):
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def sampler_object(name):
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if name == "uni_pc":
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sampler = UNIPC
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sampler = UNIPC()
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elif name == "uni_pc_bh2":
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sampler = UNIPCBH2
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sampler = UNIPCBH2()
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elif name == "ddim":
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sampler = ksampler("euler", inpaint_options={"random": True})
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else:
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@ -687,6 +704,6 @@ class KSampler:
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else:
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return torch.zeros_like(noise)
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sampler = sampler_class(self.sampler)
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sampler = sampler_object(self.sampler)
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return sample(self.model, noise, positive, negative, cfg, self.device, sampler(), sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
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return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
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@ -173,9 +173,9 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
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if getattr(self.transformer, self.inner_name).final_layer_norm.weight.dtype != torch.float32:
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precision_scope = torch.autocast
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else:
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precision_scope = lambda a, b: contextlib.nullcontext(a)
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precision_scope = lambda a, dtype: contextlib.nullcontext(a)
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with precision_scope(model_management.get_autocast_device(device), torch.float32):
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with precision_scope(model_management.get_autocast_device(device), dtype=torch.float32):
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attention_mask = None
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if self.enable_attention_masks:
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attention_mask = torch.zeros_like(tokens)
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@ -307,13 +307,13 @@ def bislerp(samples, width, height):
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res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1]
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return res
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def generate_bilinear_data(length_old, length_new):
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coords_1 = torch.arange(length_old).reshape((1,1,1,-1)).to(torch.float32)
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def generate_bilinear_data(length_old, length_new, device):
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coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1))
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coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear")
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ratios = coords_1 - coords_1.floor()
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coords_1 = coords_1.to(torch.int64)
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coords_2 = torch.arange(length_old).reshape((1,1,1,-1)).to(torch.float32) + 1
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coords_2 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + 1
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coords_2[:,:,:,-1] -= 1
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coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear")
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coords_2 = coords_2.to(torch.int64)
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@ -323,7 +323,7 @@ def bislerp(samples, width, height):
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h_new, w_new = (height, width)
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#linear w
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ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new)
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ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device)
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coords_1 = coords_1.expand((n, c, h, -1))
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coords_2 = coords_2.expand((n, c, h, -1))
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ratios = ratios.expand((n, 1, h, -1))
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@ -336,7 +336,7 @@ def bislerp(samples, width, height):
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result = result.reshape(n, h, w_new, c).movedim(-1, 1)
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#linear h
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ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new)
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ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new, samples.device)
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coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new))
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coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new))
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ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new))
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@ -16,7 +16,7 @@ class BasicScheduler:
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}
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}
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RETURN_TYPES = ("SIGMAS",)
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CATEGORY = "sampling/custom_sampling"
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CATEGORY = "sampling/custom_sampling/schedulers"
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FUNCTION = "get_sigmas"
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@ -36,7 +36,7 @@ class KarrasScheduler:
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}
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}
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RETURN_TYPES = ("SIGMAS",)
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CATEGORY = "sampling/custom_sampling"
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CATEGORY = "sampling/custom_sampling/schedulers"
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FUNCTION = "get_sigmas"
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@ -54,7 +54,7 @@ class ExponentialScheduler:
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}
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}
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RETURN_TYPES = ("SIGMAS",)
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CATEGORY = "sampling/custom_sampling"
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CATEGORY = "sampling/custom_sampling/schedulers"
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FUNCTION = "get_sigmas"
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@ -73,7 +73,7 @@ class PolyexponentialScheduler:
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}
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}
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RETURN_TYPES = ("SIGMAS",)
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CATEGORY = "sampling/custom_sampling"
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CATEGORY = "sampling/custom_sampling/schedulers"
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FUNCTION = "get_sigmas"
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@ -92,7 +92,7 @@ class VPScheduler:
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}
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}
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RETURN_TYPES = ("SIGMAS",)
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CATEGORY = "sampling/custom_sampling"
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CATEGORY = "sampling/custom_sampling/schedulers"
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FUNCTION = "get_sigmas"
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@ -109,7 +109,7 @@ class SplitSigmas:
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}
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}
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RETURN_TYPES = ("SIGMAS","SIGMAS")
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CATEGORY = "sampling/custom_sampling"
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CATEGORY = "sampling/custom_sampling/sigmas"
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FUNCTION = "get_sigmas"
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@ -118,6 +118,24 @@ class SplitSigmas:
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sigmas2 = sigmas[step:]
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return (sigmas1, sigmas2)
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class FlipSigmas:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"sigmas": ("SIGMAS", ),
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}
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}
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RETURN_TYPES = ("SIGMAS",)
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CATEGORY = "sampling/custom_sampling/sigmas"
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FUNCTION = "get_sigmas"
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def get_sigmas(self, sigmas):
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sigmas = sigmas.flip(0)
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if sigmas[0] == 0:
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sigmas[0] = 0.0001
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return (sigmas,)
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class KSamplerSelect:
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@classmethod
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def INPUT_TYPES(s):
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@ -126,12 +144,12 @@ class KSamplerSelect:
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}
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}
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RETURN_TYPES = ("SAMPLER",)
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CATEGORY = "sampling/custom_sampling"
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CATEGORY = "sampling/custom_sampling/samplers"
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FUNCTION = "get_sampler"
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def get_sampler(self, sampler_name):
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sampler = fcbh.samplers.sampler_class(sampler_name)()
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sampler = fcbh.samplers.sampler_object(sampler_name)
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return (sampler, )
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class SamplerDPMPP_2M_SDE:
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@ -145,7 +163,7 @@ class SamplerDPMPP_2M_SDE:
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}
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}
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RETURN_TYPES = ("SAMPLER",)
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CATEGORY = "sampling/custom_sampling"
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CATEGORY = "sampling/custom_sampling/samplers"
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FUNCTION = "get_sampler"
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@ -154,7 +172,7 @@ class SamplerDPMPP_2M_SDE:
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sampler_name = "dpmpp_2m_sde"
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else:
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sampler_name = "dpmpp_2m_sde_gpu"
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sampler = fcbh.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type})()
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sampler = fcbh.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type})
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return (sampler, )
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@ -169,7 +187,7 @@ class SamplerDPMPP_SDE:
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}
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}
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RETURN_TYPES = ("SAMPLER",)
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CATEGORY = "sampling/custom_sampling"
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CATEGORY = "sampling/custom_sampling/samplers"
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FUNCTION = "get_sampler"
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@ -178,7 +196,7 @@ class SamplerDPMPP_SDE:
|
||||
sampler_name = "dpmpp_sde"
|
||||
else:
|
||||
sampler_name = "dpmpp_sde_gpu"
|
||||
sampler = fcbh.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})()
|
||||
sampler = fcbh.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})
|
||||
return (sampler, )
|
||||
|
||||
class SamplerCustom:
|
||||
@ -234,6 +252,7 @@ class SamplerCustom:
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"SamplerCustom": SamplerCustom,
|
||||
"BasicScheduler": BasicScheduler,
|
||||
"KarrasScheduler": KarrasScheduler,
|
||||
"ExponentialScheduler": ExponentialScheduler,
|
||||
"PolyexponentialScheduler": PolyexponentialScheduler,
|
||||
@ -241,6 +260,6 @@ NODE_CLASS_MAPPINGS = {
|
||||
"KSamplerSelect": KSamplerSelect,
|
||||
"SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE,
|
||||
"SamplerDPMPP_SDE": SamplerDPMPP_SDE,
|
||||
"BasicScheduler": BasicScheduler,
|
||||
"SplitSigmas": SplitSigmas,
|
||||
"FlipSigmas": FlipSigmas,
|
||||
}
|
||||
|
||||
@ -1 +1 @@
|
||||
version = '2.1.810'
|
||||
version = '2.1.811'
|
||||
|
||||
@ -10,7 +10,7 @@ uov_list = [
|
||||
disabled, subtle_variation, strong_variation, upscale_15, upscale_2, upscale_fast
|
||||
]
|
||||
|
||||
KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
|
||||
KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
|
||||
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
|
||||
"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"]
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user