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modules/samplers_advanced.py

@@ -152,41 +152,7 @@ class KSamplerWithRefiner:
                                           extra_args=extra_args, noise_mask=denoise_mask, callback=callback,
                                           variant='bh2', disable=disable_pbar)
         elif self.sampler == "ddim":
-            timesteps = []
-            for s in range(sigmas.shape[0]):
-                timesteps.insert(0, self.model_wrap.sigma_to_discrete_timestep(sigmas[s]))
-            noise_mask = None
-            if denoise_mask is not None:
-                noise_mask = 1.0 - denoise_mask
-
-            ddim_callback = None
-            if callback is not None:
-                total_steps = len(timesteps) - 1
-                ddim_callback = lambda pred_x0, i: callback(i, pred_x0, None, total_steps)
-
-            sampler = DDIMSampler(self.model, device=self.device)
-            sampler.make_schedule_timesteps(ddim_timesteps=timesteps, verbose=False)
-            z_enc = sampler.stochastic_encode(latent_image,
-                                              torch.tensor([len(timesteps) - 1] * noise.shape[0]).to(self.device),
-                                              noise=noise, max_denoise=max_denoise)
-            samples, _ = sampler.sample_custom(ddim_timesteps=timesteps,
-                                               conditioning=positive,
-                                               batch_size=noise.shape[0],
-                                               shape=noise.shape[1:],
-                                               verbose=False,
-                                               unconditional_guidance_scale=cfg,
-                                               unconditional_conditioning=negative,
-                                               eta=0.0,
-                                               x_T=z_enc,
-                                               x0=latent_image,
-                                               img_callback=ddim_callback,
-                                               denoise_function=self.model_wrap.predict_eps_discrete_timestep,
-                                               extra_args=extra_args,
-                                               mask=noise_mask,
-                                               to_zero=sigmas[-1] == 0,
-                                               end_step=sigmas.shape[0] - 1,
-                                               disable_pbar=disable_pbar)
-
+            raise NotImplementedError('Swapped Refiner Does not support DDIM.')
         else:
             extra_args["denoise_mask"] = denoise_mask
             self.model_k.latent_image = latent_image