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sd3 allow loading safetensors without clip

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This commit is contained in:
Vladimir Mandic
2024-06-13 09:07:47 -04:00
parent 9ec6816a91
commit 3a9f56a7ac
2 changed files with 30 additions and 16 deletions
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modules/model_sd3.py
+29 -15
View File
@@ -26,14 +26,37 @@ def load_sd3(te3=None, fn=None, cache_dir=None, config=None):
repo_id = 'stabilityai/stable-diffusion-3-medium-diffusers'
model_id = 'stabilityai/stable-diffusion-3-medium-diffusers'
dtype = torch.float16
kwargs = {}
if fn is not None and fn.endswith('.safetensors') and os.path.exists(fn):
model_id = fn
loader = diffusers.StableDiffusion3Pipeline.from_single_file
reload_te = True
kwargs = {
'text_encoder': transformers.CLIPTextModelWithProjection.from_pretrained(
repo_id,
subfolder='text_encoder',
cache_dir=cache_dir,
torch_dtype=dtype,
),
'text_encoder_2': transformers.CLIPTextModelWithProjection.from_pretrained(
repo_id,
subfolder='text_encoder_2',
cache_dir=cache_dir,
torch_dtype=dtype,
),
'tokenizer': transformers.CLIPTokenizer.from_pretrained(
repo_id,
subfolder='tokenizer',
cache_dir=cache_dir,
),
'tokenizer_2': transformers.CLIPTokenizer.from_pretrained(
repo_id,
subfolder='tokenizer_2',
cache_dir=cache_dir,
),
}
else:
model_id = repo_id
loader = diffusers.StableDiffusion3Pipeline.from_pretrained
reload_te = False
if te3 == 'fp16':
text_encoder_3 = transformers.T5EncoderModel.from_pretrained(
repo_id,
@@ -47,6 +70,7 @@ def load_sd3(te3=None, fn=None, cache_dir=None, config=None):
text_encoder_3=text_encoder_3,
cache_dir=cache_dir,
config=config,
**kwargs,
)
elif te3 == 'fp8':
quantization_config = transformers.BitsAndBytesConfig(load_in_8bit=True)
@@ -64,6 +88,7 @@ def load_sd3(te3=None, fn=None, cache_dir=None, config=None):
torch_dtype=dtype,
cache_dir=cache_dir,
config=config,
**kwargs,
)
else:
pipe = loader(
@@ -72,20 +97,9 @@ def load_sd3(te3=None, fn=None, cache_dir=None, config=None):
text_encoder_3=None,
cache_dir=cache_dir,
config=config,
**kwargs,
)
if reload_te:
pipe.text_encoder = transformers.CLIPTextModelWithProjection.from_pretrained(
repo_id,
subfolder='text_encoder',
cache_dir=cache_dir,
torch_dtype=pipe.vae.dtype,
)
pipe.text_encoder_2 = transformers.CLIPTextModelWithProjection.from_pretrained(
repo_id,
subfolder='text_encoder_2',
cache_dir=cache_dir,
torch_dtype=pipe.vae.dtype,
)
diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["StableDiffusion3Img2ImgPipeline"] = diffusers.StableDiffusion3Img2ImgPipeline
return pipe
modules/sd_models.py
+1 -1
View File
@@ -590,7 +590,7 @@ def detect_pipeline(f: str, op: str = 'model', warning=True):
if not shared.native:
warn(f'Model detected as Segmind Vega model, but attempting to load using backend=original: {op}={f} size={size} MB')
guess = 'Stable Diffusion XL'
elif size > 5692 and size < 5698:
elif size > 5692 and size < 5698 or size > 4134 and size < 4138:
if not shared.native:
warn(f'Model detected as Stable Diffusion 3 model, but attempting to load using backend=original: {op}={f} size={size} MB')
guess = 'Stable Diffusion 3'