115 lines
4.6 KiB
Python
115 lines
4.6 KiB
Python
import k_diffusion.sampling
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import k_diffusion.external
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import torch
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import contextlib
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class CFGDenoiser(torch.nn.Module):
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def __init__(self, model):
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super().__init__()
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self.inner_model = model
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def forward(self, x, sigma, uncond, cond, cond_scale):
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if len(uncond[0]) == len(cond[0]) and x.shape[0] * x.shape[2] * x.shape[3] <= (96 * 96): #TODO check memory instead
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x_in = torch.cat([x] * 2)
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sigma_in = torch.cat([sigma] * 2)
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cond_in = torch.cat([uncond, cond])
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uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
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else:
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cond = self.inner_model(x, sigma, cond=cond)
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uncond = self.inner_model(x, sigma, cond=uncond)
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return uncond + (cond - uncond) * cond_scale
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def simple_scheduler(model, steps):
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sigs = []
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ss = len(model.sigmas) / steps
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for x in range(steps):
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sigs += [float(model.sigmas[-(1 + int(x * ss))])]
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sigs += [0.0]
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return torch.FloatTensor(sigs)
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class KSampler:
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SCHEDULERS = ["karras", "normal", "simple"]
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SAMPLERS = ["sample_euler", "sample_euler_ancestral", "sample_heun", "sample_dpm_2", "sample_dpm_2_ancestral",
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"sample_lms", "sample_dpm_fast", "sample_dpm_adaptive", "sample_dpmpp_2s_ancestral", "sample_dpmpp_sde",
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"sample_dpmpp_2m"]
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def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None):
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self.model = model
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if self.model.parameterization == "v":
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self.model_wrap = k_diffusion.external.CompVisVDenoiser(self.model, quantize=True)
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else:
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self.model_wrap = k_diffusion.external.CompVisDenoiser(self.model, quantize=True)
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self.model_k = CFGDenoiser(self.model_wrap)
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self.device = device
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if scheduler not in self.SCHEDULERS:
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scheduler = self.SCHEDULERS[0]
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if sampler not in self.SAMPLERS:
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sampler = self.SAMPLERS[0]
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self.scheduler = scheduler
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self.sampler = sampler
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self.sigma_min=float(self.model_wrap.sigmas[0])
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self.sigma_max=float(self.model_wrap.sigmas[-1])
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self.set_steps(steps, denoise)
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def _calculate_sigmas(self, steps):
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sigmas = None
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discard_penultimate_sigma = False
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if self.sampler in ['sample_dpm_2', 'sample_dpm_2_ancestral']:
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steps += 1
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discard_penultimate_sigma = True
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if self.scheduler == "karras":
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sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max, device=self.device)
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elif self.scheduler == "normal":
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sigmas = self.model_wrap.get_sigmas(steps).to(self.device)
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elif self.scheduler == "simple":
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sigmas = simple_scheduler(self.model_wrap, steps).to(self.device)
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else:
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print("error invalid scheduler", self.scheduler)
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if discard_penultimate_sigma:
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sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
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return sigmas
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def set_steps(self, steps, denoise=None):
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self.steps = steps
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if denoise is None:
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self.sigmas = self._calculate_sigmas(steps)
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else:
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new_steps = int(steps/denoise)
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sigmas = self._calculate_sigmas(new_steps)
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self.sigmas = sigmas[-(steps + 1):]
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def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None):
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sigmas = self.sigmas
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sigma_min = self.sigma_min
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if last_step is not None:
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sigma_min = sigmas[last_step]
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sigmas = sigmas[:last_step + 1]
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if start_step is not None:
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sigmas = sigmas[start_step:]
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noise *= sigmas[0]
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if latent_image is not None:
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noise += latent_image
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if self.model.model.diffusion_model.dtype == torch.float16:
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precision_scope = torch.autocast
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else:
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precision_scope = contextlib.nullcontext
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with precision_scope(self.device):
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if self.sampler == "sample_dpm_fast":
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samples = k_diffusion.sampling.sample_dpm_fast(self.model_k, noise, sigma_min, sigmas[0], self.steps, extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg})
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elif self.sampler == "sample_dpm_adaptive":
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samples = k_diffusion.sampling.sample_dpm_adaptive(self.model_k, noise, sigma_min, sigmas[0], extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg})
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else:
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samples = getattr(k_diffusion.sampling, self.sampler)(self.model_k, noise, sigmas, extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg})
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return samples.to(torch.float32)
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