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[from_single_file()]fix: local single file loading. (#5440)

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fix: local single file loading.
This commit is contained in:
Sayak Paul
2023-10-18 17:33:12 +05:30
committed by GitHub
parent 109d5bbe0d
commit 87fd3ce32b
2 changed files with 94 additions and 29 deletions
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1
src/diffusers/loaders.py
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@@ -2832,6 +2832,7 @@ class FromSingleFileMixin:
tokenizer=tokenizer,
original_config_file=original_config_file,
config_files=config_files,
local_files_only=local_files_only,
)
if torch_dtype is not None:

122
src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py
View File

@@ -787,7 +787,12 @@ def convert_ldm_bert_checkpoint(checkpoint, config):
def convert_ldm_clip_checkpoint(checkpoint, local_files_only=False, text_encoder=None):
if text_encoder is None:
config_name = "openai/clip-vit-large-patch14"
config = CLIPTextConfig.from_pretrained(config_name, local_files_only=local_files_only)
try:
config = CLIPTextConfig.from_pretrained(config_name, local_files_only=local_files_only)
except Exception:
raise ValueError(
f"With local_files_only set to {local_files_only}, you must first locally save the configuration in the following path: 'openai/clip-vit-large-patch14'."
)
ctx = init_empty_weights if is_accelerate_available() else nullcontext
with ctx():
@@ -922,7 +927,12 @@ def convert_open_clip_checkpoint(
# text_model = CLIPTextModelWithProjection.from_pretrained(
# "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", projection_dim=1280
# )
config = CLIPTextConfig.from_pretrained(config_name, **config_kwargs, local_files_only=local_files_only)
try:
config = CLIPTextConfig.from_pretrained(config_name, **config_kwargs, local_files_only=local_files_only)
except Exception:
raise ValueError(
f"With local_files_only set to {local_files_only}, you must first locally save the configuration in the following path: '{config_name}'."
)
ctx = init_empty_weights if is_accelerate_available() else nullcontext
with ctx():
@@ -1464,11 +1474,19 @@ def download_from_original_stable_diffusion_ckpt(
config_name = "stabilityai/stable-diffusion-2"
config_kwargs = {"subfolder": "text_encoder"}
text_model = convert_open_clip_checkpoint(checkpoint, config_name, **config_kwargs)
tokenizer = CLIPTokenizer.from_pretrained(
"stabilityai/stable-diffusion-2", subfolder="tokenizer", local_files_only=local_files_only
text_model = convert_open_clip_checkpoint(
checkpoint, config_name, local_files_only=local_files_only, **config_kwargs
)
try:
tokenizer = CLIPTokenizer.from_pretrained(
"stabilityai/stable-diffusion-2", subfolder="tokenizer", local_files_only=local_files_only
)
except Exception:
raise ValueError(
f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'stabilityai/stable-diffusion-2'."
)
if stable_unclip is None:
if controlnet:
pipe = pipeline_class(
@@ -1546,9 +1564,14 @@ def download_from_original_stable_diffusion_ckpt(
karlo_model, subfolder="prior", local_files_only=local_files_only
)
prior_tokenizer = CLIPTokenizer.from_pretrained(
"openai/clip-vit-large-patch14", local_files_only=local_files_only
)
try:
prior_tokenizer = CLIPTokenizer.from_pretrained(
"openai/clip-vit-large-patch14", local_files_only=local_files_only
)
except Exception:
raise ValueError(
f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'openai/clip-vit-large-patch14'."
)
prior_text_model = CLIPTextModelWithProjection.from_pretrained(
"openai/clip-vit-large-patch14", local_files_only=local_files_only
)
@@ -1581,10 +1604,22 @@ def download_from_original_stable_diffusion_ckpt(
raise NotImplementedError(f"unknown `stable_unclip` type: {stable_unclip}")
elif model_type == "PaintByExample":
vision_model = convert_paint_by_example_checkpoint(checkpoint)
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", local_files_only=local_files_only)
feature_extractor = AutoFeatureExtractor.from_pretrained(
"CompVis/stable-diffusion-safety-checker", local_files_only=local_files_only
)
try:
tokenizer = CLIPTokenizer.from_pretrained(
"openai/clip-vit-large-patch14", local_files_only=local_files_only
)
except Exception:
raise ValueError(
f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'openai/clip-vit-large-patch14'."
)
try:
feature_extractor = AutoFeatureExtractor.from_pretrained(
"CompVis/stable-diffusion-safety-checker", local_files_only=local_files_only
)
except Exception:
raise ValueError(
f"With local_files_only set to {local_files_only}, you must first locally save the feature_extractor in the following path: 'CompVis/stable-diffusion-safety-checker'."
)
pipe = PaintByExamplePipeline(
vae=vae,
image_encoder=vision_model,
@@ -1597,11 +1632,16 @@ def download_from_original_stable_diffusion_ckpt(
text_model = convert_ldm_clip_checkpoint(
checkpoint, local_files_only=local_files_only, text_encoder=text_encoder
)
tokenizer = (
CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", local_files_only=local_files_only)
if tokenizer is None
else tokenizer
)
try:
tokenizer = (
CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", local_files_only=local_files_only)
if tokenizer is None
else tokenizer
)
except Exception:
raise ValueError(
f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'openai/clip-vit-large-patch14'."
)
if load_safety_checker:
safety_checker = StableDiffusionSafetyChecker.from_pretrained(
@@ -1637,18 +1677,33 @@ def download_from_original_stable_diffusion_ckpt(
)
elif model_type in ["SDXL", "SDXL-Refiner"]:
if model_type == "SDXL":
tokenizer = CLIPTokenizer.from_pretrained(
"openai/clip-vit-large-patch14", local_files_only=local_files_only
)
try:
tokenizer = CLIPTokenizer.from_pretrained(
"openai/clip-vit-large-patch14", local_files_only=local_files_only
)
except Exception:
raise ValueError(
f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'openai/clip-vit-large-patch14'."
)
text_encoder = convert_ldm_clip_checkpoint(checkpoint, local_files_only=local_files_only)
tokenizer_2 = CLIPTokenizer.from_pretrained(
"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", pad_token="!", local_files_only=local_files_only
)
try:
tokenizer_2 = CLIPTokenizer.from_pretrained(
"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", pad_token="!", local_files_only=local_files_only
)
except Exception:
raise ValueError(
f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'laion/CLIP-ViT-bigG-14-laion2B-39B-b160k' with `pad_token` set to '!'."
)
config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
config_kwargs = {"projection_dim": 1280}
text_encoder_2 = convert_open_clip_checkpoint(
checkpoint, config_name, prefix="conditioner.embedders.1.model.", has_projection=True, **config_kwargs
checkpoint,
config_name,
prefix="conditioner.embedders.1.model.",
has_projection=True,
local_files_only=local_files_only,
**config_kwargs,
)
if is_accelerate_available(): # SBM Now move model to cpu.
@@ -1682,14 +1737,23 @@ def download_from_original_stable_diffusion_ckpt(
else:
tokenizer = None
text_encoder = None
tokenizer_2 = CLIPTokenizer.from_pretrained(
"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", pad_token="!", local_files_only=local_files_only
)
try:
tokenizer_2 = CLIPTokenizer.from_pretrained(
"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", pad_token="!", local_files_only=local_files_only
)
except Exception:
raise ValueError(
f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'laion/CLIP-ViT-bigG-14-laion2B-39B-b160k' with `pad_token` set to '!'."
)
config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
config_kwargs = {"projection_dim": 1280}
text_encoder_2 = convert_open_clip_checkpoint(
checkpoint, config_name, prefix="conditioner.embedders.0.model.", has_projection=True, **config_kwargs
checkpoint,
config_name,
prefix="conditioner.embedders.0.model.",
has_projection=True,
local_files_only=local_files_only,
**config_kwargs,
)
if is_accelerate_available(): # SBM Now move model to cpu.