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support for SD3: infinite prompt length, token counting

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AUTOMATIC1111
2024-06-26 23:22:00 +03:00
parent a8fba9af35
commit d686e73daa
6 changed files with 278 additions and 139 deletions
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59
modules/sd_hijack_clip.py
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@@ -27,24 +27,21 @@ chunk. Those objects are found in PromptChunk.fixes and, are placed into FrozenC
are applied by sd_hijack.EmbeddingsWithFixes's forward function."""
class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
"""A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
have unlimited prompt length and assign weights to tokens in prompt.
"""
def __init__(self, wrapped, hijack):
class TextConditionalModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.wrapped = wrapped
"""Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
depending on model."""
self.hijack: sd_hijack.StableDiffusionModelHijack = hijack
self.hijack = sd_hijack.model_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
self.is_trainable = False
self.input_key = 'txt'
self.return_pooled = False
self.comma_token = None
self.id_start = None
self.id_end = None
self.id_pad = None
def empty_chunk(self):
"""creates an empty PromptChunk and returns it"""
@@ -210,10 +207,6 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
"""
if opts.use_old_emphasis_implementation:
import modules.sd_hijack_clip_old
return modules.sd_hijack_clip_old.forward_old(self, texts)
batch_chunks, token_count = self.process_texts(texts)
used_embeddings = {}
@@ -252,7 +245,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
if any(x for x in texts if "(" in x or "[" in x) and opts.emphasis != "Original":
self.hijack.extra_generation_params["Emphasis"] = opts.emphasis
if getattr(self.wrapped, 'return_pooled', False):
if self.return_pooled:
return torch.hstack(zs), zs[0].pooled
else:
return torch.hstack(zs)
@@ -292,6 +285,34 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
return z
class FrozenCLIPEmbedderWithCustomWordsBase(TextConditionalModel):
"""A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
have unlimited prompt length and assign weights to tokens in prompt.
"""
def __init__(self, wrapped, hijack):
super().__init__()
self.hijack = hijack
self.wrapped = wrapped
"""Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
depending on model."""
self.is_trainable = getattr(wrapped, 'is_trainable', False)
self.input_key = getattr(wrapped, 'input_key', 'txt')
self.return_pooled = getattr(self.wrapped, 'return_pooled', False)
self.legacy_ucg_val = None # for sgm codebase
def forward(self, texts):
if opts.use_old_emphasis_implementation:
import modules.sd_hijack_clip_old
return modules.sd_hijack_clip_old.forward_old(self, texts)
return super().forward(texts)
class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
def __init__(self, wrapped, hijack):
super().__init__(wrapped, hijack)