text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
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for _, module in self.named_modules():
if isinstance(module, BaseTunerLayer):
if hasattr(module, "enable_adapters"):
module.enable_adapters(enabled=False)
else:
# support for older PEFT versions
module.disable_adapte... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
for _, module in self.named_modules():
if isinstance(module, BaseTunerLayer):
if hasattr(module, "enable_adapters"):
module.enable_adapters(enabled=True)
else:
# support for older PEFT versions
module.disable_adapter... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
from peft.tuners.tuners_utils import BaseTunerLayer
for _, module in self.named_modules():
if isinstance(module, BaseTunerLayer):
return module.active_adapter
def fuse_lora(self, lora_scale=1.0, safe_fusing=False, adapter_names=None):
if not USE_PEFT_BACKEND:
... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
# For BC with prevous PEFT versions, we need to check the signature
# of the `merge` method to see if it supports the `adapter_names` argument.
supported_merge_kwargs = list(inspect.signature(module.merge).parameters)
if "adapter_names" in supported_merge_kwargs:
merg... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
def _unfuse_lora_apply(self, module):
from peft.tuners.tuners_utils import BaseTunerLayer
if isinstance(module, BaseTunerLayer):
module.unmerge()
def unload_lora(self):
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for `unload_lora()`.")
... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights(
"jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
)
... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights(
"jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
)
... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights(
"jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_names="cinematic"
)
... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
class UNet2DConditionLoadersMixin:
"""
Load LoRA layers into a [`UNet2DCondtionModel`].
"""
text_encoder_name = TEXT_ENCODER_NAME
unet_name = UNET_NAME
@validate_hf_hub_args
def load_attn_procs(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
- A string, the model id (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
the Hub.
- A path to a directory (for example `./my_model_directory`) containing the model weights saved
with [`ModelMixin.save_pretrained`].
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
local_files_only (`bool`, *optional*, defaults to `False`):
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
The subfolder location of a model file within a larger model repository on the Hub or locally.
network_alphas (`Dict[str, float]`):
The value of the network alpha used for stable learning and preventing underflow. This value has the
same meaning as the `--network_alpha` optio... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights. | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
Example:
```py
from diffusers import AutoPipelineForText2Image
import torch | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.unet.load_attn_procs(
"jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", False)
allow_pickle = False | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if low_cpu_mem_usage and is_peft_version("<=", "0.13.0"):
raise ValueError(
"`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
)
if use_safetensors is None:
use_safetensors = True
al... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
model_file = None
if not isinstance(pretrained_model_name_or_path_or_dict, dict):
# Let's first try to load .safetensors weights
if (use_safetensors and weight_name is None) or (
weight_name is not None and weight_name.endswith(".safetensors")
):
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
except IOError as e:
if not allow_pickle:
raise e
# try loading non-safetensors weights
pass
if model_file is None:
model_file = _get_model_file(
pretrained_model_name_or_path_or_dict,
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
is_custom_diffusion = any("custom_diffusion" in k for k in state_dict.keys())
is_lora = all(("lora" in k or k.endswith(".alpha")) for k in state_dict.keys())
is_model_cpu_offload = False
is_sequential_cpu_offload = False
if is_lora:
deprecation_message = "Using the `load_att... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if is_custom_diffusion:
attn_processors = self._process_custom_diffusion(state_dict=state_dict)
elif is_lora:
is_model_cpu_offload, is_sequential_cpu_offload = self._process_lora(
state_dict=state_dict,
unet_identifier_key=self.unet_name,
n... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
# For LoRA, the UNet is already offloaded at this stage as it is handled inside `_process_lora`.
if is_custom_diffusion and _pipeline is not None:
is_model_cpu_offload, is_sequential_cpu_offload = self._optionally_disable_offloading(_pipeline=_pipeline)
# only custom diffusion needs to ... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
attn_processors = {}
custom_diffusion_grouped_dict = defaultdict(dict)
for key, value in state_dict.items():
if len(value) == 0:
custom_diffusion_grouped_dict[key] = {}
else:
if "to_out" in key:
attn_processor_key, sub_key = "."... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
for key, value_dict in custom_diffusion_grouped_dict.items():
if len(value_dict) == 0:
attn_processors[key] = CustomDiffusionAttnProcessor(
train_kv=False, train_q_out=False, hidden_size=None, cross_attention_dim=None
)
else:
cr... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
def _process_lora(
self, state_dict, unet_identifier_key, network_alphas, adapter_name, _pipeline, low_cpu_mem_usage
):
# This method does the following things:
# 1. Filters the `state_dict` with keys matching `unet_identifier_key` when using the non-legacy
# format. For legacy f... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
unet_keys = [k for k in keys if k.startswith(unet_identifier_key)]
unet_state_dict = {
k.replace(f"{unet_identifier_key}.", ""): v for k, v in state_dict.items() if k in unet_keys
}
if network_alphas is not None:
alpha_keys = [k for k in network_alphas.keys() if k.starts... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
state_dict = convert_unet_state_dict_to_peft(state_dict_to_be_used)
if network_alphas is not None:
# The alphas state dict have the same structure as Unet, thus we convert it to peft format using
# `convert_unet_state_dict_to_peft` method.
network_alphas = co... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
lora_config_kwargs = get_peft_kwargs(rank, network_alphas, state_dict, is_unet=True)
if "use_dora" in lora_config_kwargs:
if lora_config_kwargs["use_dora"]:
if is_peft_version("<", "0.9.0"):
raise ValueError(
"You need `... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if "lora_bias" in lora_config_kwargs:
if lora_config_kwargs["lora_bias"]:
if is_peft_version("<=", "0.13.2"):
raise ValueError(
"You need `peft` 0.14.0 at least to use `bias` in LoRAs. Please upgrade your installation of `peft`."
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
# In case the pipeline has been already offloaded to CPU - temporarily remove the hooks
# otherwise loading LoRA weights will lead to an error
is_model_cpu_offload, is_sequential_cpu_offload = self._optionally_disable_offloading(_pipeline)
peft_kwargs = {}
if is_peft_vers... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
warn_msg = ""
if incompatible_keys is not None:
# Check only for unexpected keys.
unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
if unexpected_keys:
lora_unexpected_keys = [k for k in unexpected_keys if "lora_" in k a... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
# Filter missing keys specific to the current adapter.
missing_keys = getattr(incompatible_keys, "missing_keys", None)
if missing_keys:
lora_missing_keys = [k for k in missing_keys if "lora_" in k and adapter_name in k]
if lora_missing_keys:
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
Args:
_pipeline (`DiffusionPipeline`):
The pipeline to disable offloading for.
Returns:
tuple:
A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.
"""
return _func_optionally_disable_offloading(_pipeline=_p... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to save an attention processor to (will be created if it doesn't exist).
is_main_process (`bool`, *optional*, defaults to `True`):
Whether the process calling this is the main process or not. Useful dur... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
```py
import torch
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
torch_dtype=torch.float16,
).to("cuda")
pipeline.unet.load_attn_procs("path-to-save-model", weight_name="pytorch_cust... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
is_custom_diffusion = any(
isinstance(
x,
(CustomDiffusionAttnProcessor, CustomDiffusionAttnProcessor2_0, CustomDiffusionXFormersAttnProcessor),
)
for (_, x) in self.attn_processors.items()
)
if is_custom_diffusion:
state_di... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
else:
deprecation_message = "Using the `save_attn_procs()` method has been deprecated and will be removed in a future version. Please use `save_lora_adapter()`."
deprecate("save_attn_procs", "0.40.0", deprecation_message) | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for saving LoRAs using the `save_attn_procs()` method.")
from peft.utils import get_peft_model_state_dict
state_dict = get_peft_model_state_dict(self)
if save_function is None:
if safe_seri... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
# Save the model
save_path = Path(save_directory, weight_name).as_posix()
save_function(state_dict, save_path)
logger.info(f"Model weights saved in {save_path}")
def _get_custom_diffusion_state_dict(self):
from ..models.attention_processor import (
CustomDiffusionAttnPro... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
model_to_save = AttnProcsLayers(
{
y: x
for (y, x) in self.attn_processors.items()
if isinstance(
x,
(
CustomDiffusionAttnProcessor,
CustomDiffusionAttnProcessor2_0,
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
else:
low_cpu_mem_usage = False
logger.warning(
"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if "proj.weight" in state_dict:
# IP-Adapter
num_image_text_embeds = 4
clip_embeddings_dim = state_dict["proj.weight"].shape[-1]
cross_attention_dim = state_dict["proj.weight"].shape[0] // 4
with init_context():
image_projection = ImageProject... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
with init_context():
image_projection = IPAdapterFullImageProjection(
cross_attention_dim=cross_attention_dim, image_embed_dim=clip_embeddings_dim
)
for key, value in state_dict.items():
diffusers_name = key.replace("proj.0", "ff.net.0.pro... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
elif "perceiver_resampler.proj_in.weight" in state_dict:
# IP-Adapter Face ID Plus
id_embeddings_dim = state_dict["proj.0.weight"].shape[1]
embed_dims = state_dict["perceiver_resampler.proj_in.weight"].shape[0]
hidden_dims = state_dict["perceiver_resampler.proj_in.weight"... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
for key, value in state_dict.items():
diffusers_name = key.replace("perceiver_resampler.", "")
diffusers_name = diffusers_name.replace("0.to", "attn.to")
diffusers_name = diffusers_name.replace("0.1.0.", "0.ff.0.")
diffusers_name = diffusers_name.replace("... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
diffusers_name = diffusers_name.replace("3.1.0.", "3.ff.0.")
diffusers_name = diffusers_name.replace("3.1.1.weight", "3.ff.1.net.0.proj.weight")
diffusers_name = diffusers_name.replace("3.1.3.weight", "3.ff.1.net.2.weight")
diffusers_name = diffusers_name.replace("layers.... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if "norm1" in diffusers_name:
updated_state_dict[diffusers_name.replace("0.norm1", "0")] = value
elif "norm2" in diffusers_name:
updated_state_dict[diffusers_name.replace("0.norm2", "1")] = value
elif "to_kv" in diffusers_name:
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
updated_state_dict["proj.net.2.weight"] = value
elif "proj.2.bias" == diffusers_name:
updated_state_dict["proj.net.2.bias"] = value
else:
updated_state_dict[diffusers_name] = value | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
elif "norm.weight" in state_dict:
# IP-Adapter Face ID
id_embeddings_dim_in = state_dict["proj.0.weight"].shape[1]
id_embeddings_dim_out = state_dict["proj.0.weight"].shape[0]
multiplier = id_embeddings_dim_out // id_embeddings_dim_in
norm_layer = "norm.weight... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
for key, value in state_dict.items():
diffusers_name = key.replace("proj.0", "ff.net.0.proj")
diffusers_name = diffusers_name.replace("proj.2", "ff.net.2")
updated_state_dict[diffusers_name] = value
else:
# IP-Adapter Plus
num_image_text_e... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
with init_context():
image_projection = IPAdapterPlusImageProjection(
embed_dims=embed_dims,
output_dims=output_dims,
hidden_dims=hidden_dims,
heads=heads,
num_queries=num_image_text_embeds,
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
diffusers_name = diffusers_name.replace("0.0.norm1", "0.ln0")
diffusers_name = diffusers_name.replace("0.0.norm2", "0.ln1")
diffusers_name = diffusers_name.replace("1.0.norm1", "1.ln0")
diffusers_name = diffusers_name.replace("1.0.norm2", "1.ln1")
diffuser... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if "to_kv" in diffusers_name:
parts = diffusers_name.split(".")
parts[2] = "attn"
diffusers_name = ".".join(parts)
v_chunk = value.chunk(2, dim=0)
updated_state_dict[diffusers_name.replace("to_kv", "to_k")] = v_chunk[0]
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
diffusers_name = diffusers_name.replace("0.1.0", "0.ff.0")
diffusers_name = diffusers_name.replace("0.1.1", "0.ff.1.net.0.proj")
diffusers_name = diffusers_name.replace("0.1.3", "0.ff.1.net.2") | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
diffusers_name = diffusers_name.replace("1.1.0", "1.ff.0")
diffusers_name = diffusers_name.replace("1.1.1", "1.ff.1.net.0.proj")
diffusers_name = diffusers_name.replace("1.1.3", "1.ff.1.net.2")
diffusers_name = diffusers_name.replace("2.1.0", "2.ff.0")
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if not low_cpu_mem_usage:
image_projection.load_state_dict(updated_state_dict, strict=True)
else:
load_model_dict_into_meta(image_projection, updated_state_dict, device=self.device, dtype=self.dtype)
return image_projection
def _convert_ip_adapter_attn_to_diffusers(self, st... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
else:
low_cpu_mem_usage = False
logger.warning(
"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
# set ip-adapter cross-attention processors & load state_dict
attn_procs = {}
key_id = 1
init_context = init_empty_weights if low_cpu_mem_usage else nullcontext
for name in self.attn_processors.keys():
cross_attention_dim = None if name.endswith("attn1.processor") else self.c... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if cross_attention_dim is None or "motion_modules" in name:
attn_processor_class = self.attn_processors[name].__class__
attn_procs[name] = attn_processor_class()
else:
if "XFormers" in str(self.attn_processors[name].__class__):
attn_process... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
num_image_text_embeds += [257] # 256 CLIP tokens + 1 CLS token
elif "perceiver_resampler.proj_in.weight" in state_dict["image_proj"]:
# IP-Adapter Face ID Plus
num_image_text_embeds += [4]
elif "norm.weight" in state_dict["image_pr... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
with init_context():
attn_procs[name] = attn_processor_class(
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
scale=1.0,
num_tokens=num_image_text_embeds,
)
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
key_id += 2
return attn_procs
def _load_ip_adapter_weights(self, state_dicts, low_cpu_mem_usage=False):
if not isinstance(state_dicts, list):
state_dicts = [state_dicts]
# Kolors Unet already has a `encoder_hid_proj`
if (
self.encoder_hid_proj is not None
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
# convert IP-Adapter Image Projection layers to diffusers
image_projection_layers = []
for state_dict in state_dicts:
image_projection_layer = self._convert_ip_adapter_image_proj_to_diffusers(
state_dict["image_proj"], low_cpu_mem_usage=low_cpu_mem_usage
)
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
def _load_ip_adapter_loras(self, state_dicts):
lora_dicts = {}
for key_id, name in enumerate(self.attn_processors.keys()):
for i, state_dict in enumerate(state_dicts):
if f"{key_id}.to_k_lora.down.weight" in state_dict["ip_adapter"]:
if i not in lora_dicts... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
{
f"unet.{name}.to_v_lora.down.weight": state_dict["ip_adapter"][
f"{key_id}.to_v_lora.down.weight"
]
}
)
lora_dicts[i].update(
{
... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
{f"unet.{name}.to_v_lora.up.weight": state_dict["ip_adapter"][f"{key_id}.to_v_lora.up.weight"]}
)
lora_dicts[i].update(
{
f"unet.{name}.to_out_lora.up.weight": state_dict["ip_adapter"][
f"{key_id}... | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
class TextualInversionLoaderMixin:
r"""
Load Textual Inversion tokens and embeddings to the tokenizer and text encoder.
"""
def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: "PreTrainedTokenizer"): # noqa: F821
r"""
Processes prompts that include a special token ... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
Returns:
`str` or list of `str`: The converted prompt
"""
if not isinstance(prompt, List):
prompts = [prompt]
else:
prompts = prompt
prompts = [self._maybe_convert_prompt(p, tokenizer) for p in prompts]
if not isinstance(prompt, List):
... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
Parameters:
prompt (`str`):
The prompt to guide the image generation.
tokenizer (`PreTrainedTokenizer`):
The tokenizer responsible for encoding the prompt into input tokens.
Returns:
`str`: The converted prompt
"""
tokens = tok... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
def _check_text_inv_inputs(self, tokenizer, text_encoder, pretrained_model_name_or_paths, tokens):
if tokenizer is None:
raise ValueError(
f"{self.__class__.__name__} requires `self.tokenizer` or passing a `tokenizer` of type `PreTrainedTokenizer` for calling"
f" `{se... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
valid_tokens = [t for t in tokens if t is not None]
if len(set(valid_tokens)) < len(valid_tokens):
raise ValueError(f"You have passed a list of tokens that contains duplicates: {tokens}") | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
@staticmethod
def _retrieve_tokens_and_embeddings(tokens, state_dicts, tokenizer):
all_tokens = []
all_embeddings = []
for state_dict, token in zip(state_dicts, tokens):
if isinstance(state_dict, torch.Tensor):
if token is None:
raise ValueErro... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
f"Loaded state dictionary is incorrect: {state_dict}. \n\n"
"Please verify that the loaded state dictionary of the textual embedding either only has a single key or includes the `string_to_param`"
" input key."
) | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
if token is not None and loaded_token != token:
logger.info(f"The loaded token: {loaded_token} is overwritten by the passed token {token}.")
else:
token = loaded_token
if token in tokenizer.get_vocab():
raise ValueError(
f"Toke... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
for embedding, token in zip(embeddings, tokens):
if f"{token}_1" in tokenizer.get_vocab():
multi_vector_tokens = [token]
i = 1
while f"{token}_{i}" in tokenizer.added_tokens_encoder:
multi_vector_tokens.append(f"{token}_{i}")
... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
is_multi_vector = len(embedding.shape) > 1 and embedding.shape[0] > 1
if is_multi_vector:
all_tokens += [token] + [f"{token}_{i}" for i in range(1, embedding.shape[0])]
all_embeddings += [e for e in embedding] # noqa: C416
else:
all_tokens += [tok... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
@validate_hf_hub_args
def load_textual_inversion(
self,
pretrained_model_name_or_path: Union[str, List[str], Dict[str, torch.Tensor], List[Dict[str, torch.Tensor]]],
token: Optional[Union[str, List[str]]] = None,
tokenizer: Optional["PreTrainedTokenizer"] = None, # noqa: F821
... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
- A string, the *model id* (for example `sd-concepts-library/low-poly-hd-logos-icons`) of a
pretrained model hosted on the Hub.
- A path to a *directory* (for example `./my_text_inversion_directory/`) containing the textual
inversion weights.
... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
token (`str` or `List[str]`, *optional*):
Override the token to use for the textual inversion weights. If `pretrained_model_name_or_path` is a
list, then `token` must also be a list of equal length.
text_encoder ([`~transformers.CLIPTextModel`], *optional*):
F... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
- The saved textual inversion file is in 🤗 Diffusers format, but was saved under a specific weight
name such as `text_inv.bin`.
- The saved textual inversion file is in the Automatic1111 format.
cache_dir (`Union[str, os.PathLike]`, *optional*):
Pat... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
local_files_only (`bool`, *optional*, defaults to `False`):
... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
The subfolder location of a model file within a larger model repository on the Hub or locally.
mirror (`str`, *optional*):
Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
guarantee the timeliness or safety of the source, and you... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
Example:
To load a Textual Inversion embedding vector in 🤗 Diffusers format:
```py
from diffusers import StableDiffusionPipeline
import torch
model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
pipe.load_textual_inversion("./charturnerv2.pt", token="charturnerv2")
prompt = "charturnerv2, multiple views of the same character in the sam... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# 2. Normalize inputs
pretrained_model_name_or_paths = (
[pretrained_model_name_or_path]
if not isinstance(pretrained_model_name_or_path, list)
else pretrained_model_name_or_path
)
tokens = [token] if not isinstance(token, list) else token
if tokens[0]... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# 4.1 Handle the special case when state_dict is a tensor that contains n embeddings for n tokens
if len(tokens) > 1 and len(state_dicts) == 1:
if isinstance(state_dicts[0], torch.Tensor):
state_dicts = list(state_dicts[0])
if len(tokens) != len(state_dicts):
... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# 6. Make sure all embeddings have the correct size
expected_emb_dim = text_encoder.get_input_embeddings().weight.shape[-1]
if any(expected_emb_dim != emb.shape[-1] for emb in embeddings):
raise ValueError(
"Loaded embeddings are of incorrect shape. Expected each textual inve... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# 7.1 Offload all hooks in case the pipeline was cpu offloaded before make sure, we offload and onload again
is_model_cpu_offload = False
is_sequential_cpu_offload = False
if self.hf_device_map is None:
for _, component in self.components.items():
if isinstance(compon... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
"Accelerate hooks detected. Since you have called `load_textual_inversion()`, the previous hooks will be first removed. Then the textual inversion parameters will be loaded and the hooks will be applied again."
)
remove_hook_from_module(component, recurse=is_sequential_cp... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# 7.2 save expected device and dtype
device = text_encoder.device
dtype = text_encoder.dtype
# 7.3 Increase token embedding matrix
text_encoder.resize_token_embeddings(len(tokenizer) + len(tokens))
input_embeddings = text_encoder.get_input_embeddings().weight
# 7.4 Load... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
def unload_textual_inversion(
self,
tokens: Optional[Union[str, List[str]]] = None,
tokenizer: Optional["PreTrainedTokenizer"] = None,
text_encoder: Optional["PreTrainedModel"] = None,
):
r"""
Unload Textual Inversion embeddings from the text encoder of [`StableDiffus... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# Remove just one token
pipeline.unload_textual_inversion("<moe-bius>")
# Example 3: unload from SDXL
pipeline = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
embedding_path = hf_hub_download(
repo_id="linoyts/web_y2k", filename="web_y... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# Unload explicitly from both text encoders and tokenizers
pipeline.unload_textual_inversion(
tokens=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer
)
pipeline.unload_textual_inversion(
tokens=["<s0>", "<s1>"], text_encoder=pipeline.text... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
if tokens:
if isinstance(tokens, str):
tokens = [tokens]
for added_token_id, added_token in tokenizer.added_tokens_decoder.items():
if not added_token.special:
if added_token.content in tokens:
token_ids.append(added_tok... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# Delete from tokenizer
for token_id, token_to_remove in zip(token_ids, tokens):
del tokenizer._added_tokens_decoder[token_id]
del tokenizer._added_tokens_encoder[token_to_remove]
# Make all token ids sequential in tokenizer
key_id = 1
for token_id in tokenizer.a... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# Delete from text encoder
text_embedding_dim = text_encoder.get_input_embeddings().embedding_dim
temp_text_embedding_weights = text_encoder.get_input_embeddings().weight
text_embedding_weights = temp_text_embedding_weights[: last_special_token_id + 1]
to_append = []
for i in ran... | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
class FromOriginalModelMixin:
"""
Load pretrained weights saved in the `.ckpt` or `.safetensors` format into a model.
"""
@classmethod
@validate_hf_hub_args
def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] = None, **kwargs):
r"""
Instantiate a model... | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
Parameters:
pretrained_model_link_or_path_or_dict (`str`, *optional*):
Can be either:
- A link to the `.safetensors` or `.ckpt` file (for example
`"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.safetensors"`) on the Hub.
... | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
original_config (`str`, *optional*):
Dict or path to a yaml file containing the configuration for the model in its original format.
If a dict is provided, it will be used to initialize the model configuration.
torch_dtype (`str` or `torch.dtype`, *optional*):
... | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
local_files_only (`bool`, *optional*, defaults to `False`):
... | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
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