diff options
Diffstat (limited to 'modules/sd_hijack_clip.py')
| -rw-r--r-- | modules/sd_hijack_clip.py | 36 |
1 files changed, 33 insertions, 3 deletions
diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py index c1d780a3..8f29057a 100644 --- a/modules/sd_hijack_clip.py +++ b/modules/sd_hijack_clip.py @@ -42,6 +42,10 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): self.hijack: sd_hijack.StableDiffusionModelHijack = hijack
self.chunk_length = 75
+ self.is_trainable = getattr(wrapped, 'is_trainable', False)
+ self.input_key = getattr(wrapped, 'input_key', 'txt')
+ self.legacy_ucg_val = None
+
def empty_chunk(self):
"""creates an empty PromptChunk and returns it"""
@@ -157,7 +161,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): position += 1
continue
- emb_len = int(embedding.vec.shape[0])
+ emb_len = int(embedding.vectors)
if len(chunk.tokens) + emb_len > self.chunk_length:
next_chunk()
@@ -199,8 +203,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): """
Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts.
Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will
- be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, and for SD2 it's 1024.
+ be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, for SD2 it's 1024, and for SDXL it's 1280.
An example shape returned by this function can be: (2, 77, 768).
+ For SDXL, instead of returning one tensor avobe, it returns a tuple with two: the other one with shape (B, 1280) with pooled values.
Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one elemenet
is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
"""
@@ -240,9 +245,14 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): hashes.append(f"{name}: {shorthash}")
if hashes:
+ if self.hijack.extra_generation_params.get("TI hashes"):
+ hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
- return torch.hstack(zs)
+ if getattr(self.wrapped, 'return_pooled', False):
+ return torch.hstack(zs), zs[0].pooled
+ else:
+ return torch.hstack(zs)
def process_tokens(self, remade_batch_tokens, batch_multipliers):
"""
@@ -262,6 +272,8 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): z = self.encode_with_transformers(tokens)
+ pooled = getattr(z, 'pooled', None)
+
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
original_mean = z.mean()
@@ -269,6 +281,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): new_mean = z.mean()
z = z * (original_mean / new_mean)
+ if pooled is not None:
+ z.pooled = pooled
+
return z
@@ -324,3 +339,18 @@ class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase): embedded = embedding_layer.token_embedding.wrapped(ids.to(embedding_layer.token_embedding.wrapped.weight.device)).squeeze(0)
return embedded
+
+
+class FrozenCLIPEmbedderForSDXLWithCustomWords(FrozenCLIPEmbedderWithCustomWords):
+ def __init__(self, wrapped, hijack):
+ super().__init__(wrapped, hijack)
+
+ def encode_with_transformers(self, tokens):
+ outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=self.wrapped.layer == "hidden")
+
+ if self.wrapped.layer == "last":
+ z = outputs.last_hidden_state
+ else:
+ z = outputs.hidden_states[self.wrapped.layer_idx]
+
+ return z
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