Make k_diffusion optional

TTS/tts/layers/tortoise/diffusion.py

@@ -13,12 +13,19 @@ import math
 import numpy as np
 import torch
 import torch as th
-from k_diffusion.sampling import sample_dpmpp_2m, sample_euler_ancestral
 from tqdm import tqdm
 
 from TTS.tts.layers.tortoise.dpm_solver import DPM_Solver, NoiseScheduleVP, model_wrapper
 
-K_DIFFUSION_SAMPLERS = {"k_euler_a": sample_euler_ancestral, "dpm++2m": sample_dpmpp_2m}
+
+try:
+    from k_diffusion.sampling import sample_dpmpp_2m, sample_euler_ancestral
+
+    K_DIFFUSION_SAMPLERS = {"k_euler_a": sample_euler_ancestral, "dpm++2m": sample_dpmpp_2m}
+except ImportError:
+    K_DIFFUSION_SAMPLERS = None
+
+
 SAMPLERS = ["dpm++2m", "p", "ddim"]
 
 
@@ -531,6 +538,8 @@ class GaussianDiffusion:
             if self.conditioning_free is not True:
                 raise RuntimeError("cond_free must be true")
             with tqdm(total=self.num_timesteps) as pbar:
+                if K_DIFFUSION_SAMPLERS is None:
+                    raise ModuleNotFoundError("Install k_diffusion for using k_diffusion samplers")
                 return self.k_diffusion_sample_loop(K_DIFFUSION_SAMPLERS[s], pbar, *args, **kwargs)
         else:
             raise RuntimeError("sampler not impl")

requirements.txt

@@ -46,7 +46,6 @@ bangla
 bnnumerizer
 bnunicodenormalizer
 #deps for tortoise
-k_diffusion
 einops>=0.6.0
 transformers>=4.33.0
 #deps for bark