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add generic t5 loader

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This commit is contained in:
Vladimir Mandic
2024-06-16 11:38:29 -04:00
parent a80fb38147
commit 9644063cd6
5 changed files with 53 additions and 27 deletions
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modules/model_sd3.py
+19 -8
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@@ -13,7 +13,6 @@ loggedin = False
def load_sd3(fn=None, cache_dir=None, config=None):
from modules import devices, modelloader
modelloader.hf_login()
repo_id = 'stabilityai/stable-diffusion-3-medium-diffusers'
model_id = 'stabilityai/stable-diffusion-3-medium-diffusers'
dtype = torch.float16
@@ -56,6 +55,7 @@ def load_sd3(fn=None, cache_dir=None, config=None):
else:
kwargs = {}
else:
modelloader.hf_login()
model_id = repo_id
loader = diffusers.StableDiffusion3Pipeline.from_pretrained
pipe = loader(
@@ -71,34 +71,45 @@ def load_sd3(fn=None, cache_dir=None, config=None):
return pipe
def load_te3(pipe, te3=None, cache_dir=None):
def load_t5(pipe, module, te3=None, cache_dir=None):
from modules import devices, modelloader
modelloader.hf_login()
repo_id = 'stabilityai/stable-diffusion-3-medium-diffusers'
if pipe is None or not hasattr(pipe, 'text_encoder_3'):
if pipe is None or not hasattr(pipe, module):
return pipe
if 'fp16' in te3.lower():
pipe.text_encoder_3 = transformers.T5EncoderModel.from_pretrained(
modelloader.hf_login()
t5 = transformers.T5EncoderModel.from_pretrained(
repo_id,
subfolder='text_encoder_3',
# torch_dtype=dtype,
cache_dir=cache_dir,
torch_dtype=pipe.text_encoder.dtype,
)
setattr(pipe, module, t5)
elif 'fp8' in te3.lower():
modelloader.hf_login()
from installer import install
install('bitsandbytes', quiet=True)
quantization_config = transformers.BitsAndBytesConfig(load_in_8bit=True)
pipe.text_encoder_3 = transformers.T5EncoderModel.from_pretrained(
t5 = transformers.T5EncoderModel.from_pretrained(
repo_id,
subfolder='text_encoder_3',
quantization_config=quantization_config,
cache_dir=cache_dir,
torch_dtype=pipe.text_encoder.dtype,
)
setattr(pipe, module, t5)
"""
if hasattr(pipe, 'remove_all_hooks'):
pipe.remove_all_hooks()
nn = getattr(pipe, module)
import accelerate
accelerate.hooks.remove_hook_from_module(nn, recurse=True)
nn.to(device=devices.device)
"""
else:
pipe.text_encoder_3 = None
if getattr(pipe, 'text_encoder_3', None) is not None and getattr(pipe, 'tokenizer_3', None) is None:
setattr(pipe, module, None)
if getattr(pipe, 'text_encoder_3', None) is not None and getattr(pipe, 'tokenizer_3', None) is None: # not needed anymore
pipe.tokenizer_3 = transformers.T5TokenizerFast.from_pretrained(
repo_id,
subfolder='tokenizer_3',