cpu support for txt2img and img2img

ldm/models/diffusion/ddim.py

@@ -18,7 +18,7 @@ def __init__(self, model, schedule="linear", **kwargs):
 
     def register_buffer(self, name, attr):
         if type(attr) == torch.Tensor:
-            if attr.device != torch.device("cuda"):
+            if torch.cuda.is_available() and attr.device != torch.device("cuda"):
                 attr = attr.to(torch.device("cuda"))
         setattr(self, name, attr)
 
@@ -238,4 +238,4 @@ def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unco
             x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
                                           unconditional_guidance_scale=unconditional_guidance_scale,
                                           unconditional_conditioning=unconditional_conditioning)
-        return x_dec
+        return x_dec

ldm/models/diffusion/plms.py

@@ -7,7 +7,6 @@
 
 from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
 
-
 class PLMSSampler(object):
     def __init__(self, model, schedule="linear", **kwargs):
         super().__init__()
@@ -17,7 +16,7 @@ def __init__(self, model, schedule="linear", **kwargs):
 
     def register_buffer(self, name, attr):
         if type(attr) == torch.Tensor:
-            if attr.device != torch.device("cuda"):
+            if torch.cuda.is_available() and attr.device != torch.device("cuda"):
                 attr = attr.to(torch.device("cuda"))
         setattr(self, name, attr)
 

ldm/modules/encoders/modules.py

@@ -35,7 +35,7 @@ def forward(self, batch, key=None):
 
 class TransformerEmbedder(AbstractEncoder):
     """Some transformer encoder layers"""
-    def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
+    def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device='cuda' if torch.cuda.is_available() else 'cpu'):
         super().__init__()
         self.device = device
         self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
@@ -52,7 +52,7 @@ def encode(self, x):
 
 class BERTTokenizer(AbstractEncoder):
     """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
-    def __init__(self, device="cuda", vq_interface=True, max_length=77):
+    def __init__(self, device='cuda' if torch.cuda.is_available() else 'cpu', vq_interface=True, max_length=77):
         super().__init__()
         from transformers import BertTokenizerFast  # TODO: add to reuquirements
         self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
@@ -80,7 +80,7 @@ def decode(self, text):
 class BERTEmbedder(AbstractEncoder):
     """Uses the BERT tokenizr model and add some transformer encoder layers"""
     def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
-                 device="cuda",use_tokenizer=True, embedding_dropout=0.0):
+                 device='cuda' if torch.cuda.is_available() else 'cpu', use_tokenizer=True, embedding_dropout=0.0):
         super().__init__()
         self.use_tknz_fn = use_tokenizer
         if self.use_tknz_fn:
@@ -136,7 +136,7 @@ def encode(self, x):
 
 class FrozenCLIPEmbedder(AbstractEncoder):
     """Uses the CLIP transformer encoder for text (from Hugging Face)"""
-    def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77):
+    def __init__(self, version="openai/clip-vit-large-patch14", device='cuda' if torch.cuda.is_available() else 'cpu', max_length=77):
         super().__init__()
         self.tokenizer = CLIPTokenizer.from_pretrained(version)
         self.transformer = CLIPTextModel.from_pretrained(version)
@@ -166,7 +166,7 @@ class FrozenCLIPTextEmbedder(nn.Module):
     """
     Uses the CLIP transformer encoder for text.
     """
-    def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True):
+    def __init__(self, version='ViT-L/14', device='cuda' if torch.cuda.is_available() else 'cpu', max_length=77, n_repeat=1, normalize=True):
         super().__init__()
         self.model, _ = clip.load(version, jit=False, device="cpu")
         self.device = device
@@ -231,4 +231,4 @@ def forward(self, x):
 if __name__ == "__main__":
     from ldm.util import count_params
     model = FrozenCLIPEmbedder()
-    count_params(model, verbose=True)
+    count_params(model, verbose=True)

scripts/img2img.py

@@ -40,7 +40,8 @@ def load_model_from_config(config, ckpt, verbose=False):
         print("unexpected keys:")
         print(u)
 
-    model.cuda()
+    if torch.cuda.is_available():
+        model.cuda()
     model.eval()
     return model
 

scripts/txt2img.py

@@ -61,7 +61,8 @@ def load_model_from_config(config, ckpt, verbose=False):
         print("unexpected keys:")
         print(u)
 
-    model.cuda()
+    if torch.cuda.is_available():
+        model.cuda()
     model.eval()
     return model