Merge branch 'master' into inpaint_textual_inversion

1 files changed, 34 insertions, 0 deletions

diff --git a/modules/sd_hijack_xlmr.py b/modules/sd_hijack_xlmr.py
new file mode 100644
index 00000000..4ac51c38
--- /dev/null
+++ b/modules/sd_hijack_xlmr.py
@@ -0,0 +1,34 @@
+import open_clip.tokenizer

+import torch

+

+from modules import sd_hijack_clip, devices

+from modules.shared import opts

+

+

+class FrozenXLMREmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords):

+    def __init__(self, wrapped, hijack):

+        super().__init__(wrapped, hijack)

+

+        self.id_start = wrapped.config.bos_token_id

+        self.id_end = wrapped.config.eos_token_id

+        self.id_pad = wrapped.config.pad_token_id

+

+        self.comma_token = self.tokenizer.get_vocab().get(',', None)  # alt diffusion doesn't have </w> bits for comma

+

+    def encode_with_transformers(self, tokens):

+        # there's no CLIP Skip here because all hidden layers have size of 1024 and the last one uses a

+        # trained layer to transform those 1024 into 768 for unet; so you can't choose which transformer

+        # layer to work with - you have to use the last

+

+        attention_mask = (tokens != self.id_pad).to(device=tokens.device, dtype=torch.int64)

+        features = self.wrapped(input_ids=tokens, attention_mask=attention_mask)

+        z = features['projection_state']

+

+        return z

+

+    def encode_embedding_init_text(self, init_text, nvpt):

+        embedding_layer = self.wrapped.roberta.embeddings

+        ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]

+        embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)

+

+        return embedded