restore some legacy codes from webui

#933

modules/sd_samplers_cfg_denoiser.py

@@ -31,6 +31,10 @@ def pad_cond(tensor, repeats, empty):
     return tensor
 
 
+def append_zero(x):
+    return torch.cat([x, x.new_zeros([1])])
+
+
 class CFGDenoiser(torch.nn.Module):
     """
     Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
@@ -66,6 +70,36 @@ class CFGDenoiser(torch.nn.Module):
 
         self.classic_ddim_eps_estimation = False
 
+        self.sigmas = model.forge_objects.unet.model.predictor.sigmas
+        self.log_sigmas = self.sigmas.log()
+
+    def get_sigmas(self, n=None):
+        if n is None:
+            return append_zero(self.sigmas.flip(0))
+        t_max = len(self.sigmas) - 1
+        t = torch.linspace(t_max, 0, n, device=self.sigmas.device)
+        return append_zero(self.t_to_sigma(t))
+
+    def sigma_to_t(self, sigma, quantize=None):
+        quantize = self.quantize if quantize is None else quantize
+        log_sigma = sigma.log()
+        dists = log_sigma - self.log_sigmas[:, None]
+        if quantize:
+            return dists.abs().argmin(dim=0).view(sigma.shape)
+        low_idx = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
+        high_idx = low_idx + 1
+        low, high = self.log_sigmas[low_idx], self.log_sigmas[high_idx]
+        w = (low - log_sigma) / (low - high)
+        w = w.clamp(0, 1)
+        t = (1 - w) * low_idx + w * high_idx
+        return t.view(sigma.shape)
+
+    def t_to_sigma(self, t):
+        t = t.float()
+        low_idx, high_idx, w = t.floor().long(), t.ceil().long(), t.frac()
+        log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
+        return log_sigma.exp()
+
     def combine_denoised(self, x_out, conds_list, uncond, cond_scale, timestep, x_in, cond):
         denoised_uncond = x_out[-uncond.shape[0]:]
         denoised = torch.clone(denoised_uncond)

modules/sd_samplers_kdiffusion.py

@@ -85,7 +85,7 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
         elif scheduler is None or scheduler.function is None:
-            raise ValueError('Wrong scheduler!')
+            sigmas = self.model_wrap.get_sigmas(steps)
         else:
             sigmas_kwargs = {'sigma_min': sigma_min, 'sigma_max': sigma_max}