From 19a013ba9efaad53b7fc0eef647671e7143efc2a Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Tue, 4 Oct 2022 21:16:08 +0200
Subject: Inference: Add support for embeddings

---
 infer.py | 32 ++++++++++++++++++++++++++++++--
 1 file changed, 30 insertions(+), 2 deletions(-)

(limited to 'infer.py')

diff --git a/infer.py b/infer.py
index 3dc0f32..3487e5a 100644
--- a/infer.py
+++ b/infer.py
@@ -23,6 +23,7 @@ default_args = {
     "model": None,
     "scheduler": "euler_a",
     "precision": "bf16",
+    "embeddings_dir": "embeddings",
     "output_dir": "output/inference",
     "config": None,
 }
@@ -71,6 +72,10 @@ def create_args_parser():
         type=str,
         choices=["fp32", "fp16", "bf16"],
     )
+    parser.add_argument(
+        "--embeddings_dir",
+        type=str,
+    )
     parser.add_argument(
         "--output_dir",
         type=str,
@@ -162,7 +167,28 @@ def save_args(basepath, args, extra={}):
         json.dump(info, f, indent=4)
 
 
-def create_pipeline(model, scheduler, dtype):
+def load_embeddings(tokenizer, text_encoder, embeddings_dir):
+    embeddings_dir = Path(embeddings_dir)
+    embeddings_dir.mkdir(parents=True, exist_ok=True)
+
+    token_embeds = text_encoder.get_input_embeddings().weight.data
+
+    for file in embeddings_dir.iterdir():
+        placeholder_token = file.stem
+        placeholder_token_id = tokenizer.convert_tokens_to_ids(placeholder_token)
+
+        data = torch.load(file, map_location="cpu")
+
+        assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
+
+        emb = next(iter(data.values()))
+        if len(emb.shape) == 1:
+            emb = emb.unsqueeze(0)
+
+        token_embeds[placeholder_token_id] = emb
+
+
+def create_pipeline(model, scheduler, embeddings_dir, dtype):
     print("Loading Stable Diffusion pipeline...")
 
     tokenizer = CLIPTokenizer.from_pretrained(model + '/tokenizer', torch_dtype=dtype)
@@ -170,6 +196,8 @@ def create_pipeline(model, scheduler, dtype):
     vae = AutoencoderKL.from_pretrained(model + '/vae', torch_dtype=dtype)
     unet = UNet2DConditionModel.from_pretrained(model + '/unet', torch_dtype=dtype)
 
+    load_embeddings(tokenizer, text_encoder, embeddings_dir)
+
     if scheduler == "plms":
         scheduler = PNDMScheduler(
             beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
@@ -290,7 +318,7 @@ def main():
     output_dir = Path(args.output_dir)
     dtype = {"fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16}[args.precision]
 
-    pipeline = create_pipeline(args.model, args.scheduler, dtype)
+    pipeline = create_pipeline(args.model, args.scheduler, args.embeddings_dir, dtype)
     cmd_parser = create_cmd_parser()
     cmd_prompt = CmdParse(output_dir, pipeline, cmd_parser)
     cmd_prompt.cmdloop()
-- 
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