Fixes to run on CPU and MPS

kandinsky2/kandinsky2_1_model.py

@@ -30,6 +30,8 @@ def __init__(
     ):
         self.config = config
         self.device = device
+        if not torch.has_cuda:
+            self.config["model_config"]["use_fp16"] = False
         self.use_fp16 = self.config["model_config"]["use_fp16"]
         self.task_type = task_type
         self.clip_image_size = config["clip_image_size"]
@@ -54,7 +56,7 @@ def __init__(
             clip_mean,
             clip_std,
         )
-        self.prior.load_state_dict(torch.load(prior_path), strict=False)
+        self.prior.load_state_dict(torch.load(prior_path, map_location='cpu'), strict=False)
         if self.use_fp16:
             self.prior = self.prior.half()
         self.text_encoder = TextEncoder(**self.config["text_enc_params"])
@@ -88,7 +90,7 @@ def __init__(
 
         self.config["model_config"]["cache_text_emb"] = True
         self.model = create_model(**self.config["model_config"])
-        self.model.load_state_dict(torch.load(model_path))
+        self.model.load_state_dict(torch.load(model_path, map_location='cpu'))
         if self.use_fp16:
             self.model.convert_to_fp16()
             self.image_encoder = self.image_encoder.half()

kandinsky2/model/gaussian_diffusion.py

@@ -822,7 +822,7 @@ def _extract_into_tensor(arr, timesteps, broadcast_shape):
                             dimension equal to the length of timesteps.
     :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
     """
-    res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
+    res = th.from_numpy(arr).to(dtype=th.float32).to(device=timesteps.device)[timesteps]
     while len(res.shape) < len(broadcast_shape):
         res = res[..., None]
     return res.expand(broadcast_shape)