custom-diffusion-space

app.py

@@ -154,6 +154,7 @@ def create_training_demo(trainer: Trainer,
 def find_weight_files() -> list[str]:
     curr_dir = pathlib.Path(__file__).parent
     paths = sorted(curr_dir.rglob('*.bin'))
+    paths = [path for path in paths if '.lfs' not in path.name]
     return [path.relative_to(curr_dir).as_posix() for path in paths]
 
 

inference.py

@@ -13,27 +13,6 @@ from diffusers import StableDiffusionPipeline
 sys.path.insert(0, './custom-diffusion')
 
 
-# def load_model(text_encoder, tokenizer, unet, save_path, modifier_token, freeze_model='crossattn_kv'):
-#     st = torch.load(save_path)
-#     if 'text_encoder' in st:
-#         text_encoder.load_state_dict(st['text_encoder'])
-#     if modifier_token in st:
-#         _ = tokenizer.add_tokens(modifier_token)
-#         modifier_token_id = tokenizer.convert_tokens_to_ids(modifier_token)
-#         # Resize the token embeddings as we are adding new special tokens to the tokenizer
-#         text_encoder.resize_token_embeddings(len(tokenizer))
-#         token_embeds = text_encoder.get_input_embeddings().weight.data
-#         token_embeds[modifier_token_id] = st[modifier_token]
-#     print(st.keys())
-#     for name, params in unet.named_parameters():
-#         if freeze_model == 'crossattn':
-#             if 'attn2' in name:
-#                 params.data.copy_(st['unet'][f'{name}'])
-#         else:
-#             if 'attn2.to_k' in name or 'attn2.to_v' in name:
-#                 params.data.copy_(st['unet'][f'{name}'])
-
-
 class InferencePipeline:
     def __init__(self):
         self.pipe = None