text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
if time_compression_ratio == 4:
add_spatial_upsample = bool(i < num_spatial_upsample_layers)
add_time_upsample = bool(
i >= len(block_out_channels) - 1 - num_time_upsample_layers and not is_final_block
)
else:
raise ValueErr... | 1,249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
up_block = HunyuanVideoUpBlock3D(
num_layers=self.layers_per_block + 1,
in_channels=prev_output_channel,
out_channels=output_channel,
add_upsample=bool(add_spatial_upsample or add_time_upsample),
upsample_scale_factor=upsample_scale_factor,... | 1,249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
if torch.is_grad_enabled() and self.gradient_checkpointing:
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
else:
... | 1,249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
for up_block in self.up_blocks:
hidden_states = up_block(hidden_states)
# post-process
hidden_states = self.conv_norm_out(hidden_states)
hidden_states = self.conv_act(hidden_states)
hidden_states = self.conv_out(hidden_states)
return hidden_states | 1,249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
class AutoencoderKLHunyuanVideo(ModelMixin, ConfigMixin):
r"""
A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos.
Introduced in [HunyuanVideo](https://huggingface.co/papers/2412.03603).
This model inherits from [`ModelMixin`]. Check the superclass... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
latent_channels: int = 16,
down_block_types: Tuple[str, ...] = (
"HunyuanVideoDownBlock3D",
"HunyuanVideoDownBlock3D",
"HunyuanVideoDownBlock3D",
... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
self.encoder = HunyuanVideoEncoder3D(
in_channels=in_channels,
out_channels=latent_channels,
down_block_types=down_block_types,
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
norm_num_groups=norm_num_groups,
a... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
self.decoder = HunyuanVideoDecoder3D(
in_channels=latent_channels,
out_channels=out_channels,
up_block_types=up_block_types,
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
norm_num_groups=norm_num_groups,
act_... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
# When decoding a batch of video latents at a time, one can save memory by slicing across the batch dimension
# to perform decoding of a single video latent at a time.
self.use_slicing = False
# When decoding spatially large video latents, the memory requirement is very high. By breaking the vi... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
# The minimal tile height and width for spatial tiling to be used
self.tile_sample_min_height = 256
self.tile_sample_min_width = 256
self.tile_sample_min_num_frames = 16
# The minimal distance between two spatial tiles
self.tile_sample_stride_height = 192
self.tile_sampl... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
def enable_tiling(
self,
tile_sample_min_height: Optional[int] = None,
tile_sample_min_width: Optional[int] = None,
tile_sample_min_num_frames: Optional[int] = None,
tile_sample_stride_height: Optional[float] = None,
tile_sample_stride_width: Optional[float] = None,
... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
Args:
tile_sample_min_height (`int`, *optional*):
The minimum height required for a sample to be separated into tiles across the height dimension.
tile_sample_min_width (`int`, *optional*):
The minimum width required for a sample to be separated into tiles across ... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
artifacts produced across the width dimension.
tile_sample_stride_num_frames (`int`, *optional*):
The stride between two consecutive frame tiles. This is to ensure that there are no tiling artifacts
produced across the frame dimension.
"""
self.use_tiling = Tr... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
def disable_tiling(self) -> None:
r"""
Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
decoding in one step.
"""
self.use_tiling = False
def enable_slicing(self) -> None:
r"""
Enable sliced VAE deco... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
if self.use_framewise_decoding and num_frames > self.tile_sample_min_num_frames:
return self._temporal_tiled_encode(x)
if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):
return self.tiled_encode(x)
x = self.encoder(x)
en... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
Returns:
The latent representations of the encoded videos. If `return_dict` is True, a
[`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
"""
if self.use_slicing and x.shape[0] > 1:
encoded_slices = [self._encode... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
def _decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
batch_size, num_channels, num_frames, height, width = z.shape
tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
tile_latent_min_width = self.tile_sample_stride... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
@apply_forward_hook
def decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
r"""
Decode a batch of images.
Args:
z (`torch.Tensor`): Input batch of latent vectors.
return_dict (`bool`, *optional*, defaults to `True`):
... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
blend_extent = min(a.shape[-2], b.shape[-2], blend_extent)
for y in range(blend_extent):
b[:, :, :, y, :] = a[:, :, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, :, y, :] * (
... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
def blend_t(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
blend_extent = min(a.shape[-3], b.shape[-3], blend_extent)
for x in range(blend_extent):
b[:, :, x, :, :] = a[:, :, -blend_extent + x, :, :] * (1 - x / blend_extent) + b[:, :, x, :, :] * (
... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
tile_latent_stride_height = self.tile_sample_stride_height // self.spatial_compression_ratio
tile_latent_stride_width = self... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
# Split x into overlapping tiles and encode them separately.
# The tiles have an overlap to avoid seams between tiles.
rows = []
for i in range(0, height, self.tile_sample_stride_height):
row = []
for j in range(0, width, self.tile_sample_stride_width):
ti... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
result_rows = []
for i, row in enumerate(rows):
result_row = []
for j, tile in enumerate(row):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
tile = se... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
Args:
z (`torch.Tensor`): Input batch of latent vectors.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
Returns:
[`~models.vae.DecoderOutput`] or `tuple`:
... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
tile_latent_stride_height = self.tile_sample_stride_height // self.spatial_compression_ratio
tile_latent_stride_width = self... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
# Split z into overlapping tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
rows = []
for i in range(0, height, tile_latent_stride_height):
row = []
for j in range(0, width, tile_latent_stride_width):
tile = z[:, ... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
result_rows = []
for i, row in enumerate(rows):
result_row = []
for j, tile in enumerate(row):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
tile = se... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
def _temporal_tiled_encode(self, x: torch.Tensor) -> AutoencoderKLOutput:
batch_size, num_channels, num_frames, height, width = x.shape
latent_num_frames = (num_frames - 1) // self.temporal_compression_ratio + 1
tile_latent_min_num_frames = self.tile_sample_min_num_frames // self.temporal_compr... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
row = []
for i in range(0, num_frames, self.tile_sample_stride_num_frames):
tile = x[:, :, i : i + self.tile_sample_min_num_frames + 1, :, :]
if self.use_tiling and (height > self.tile_sample_min_height or width > self.tile_sample_min_width):
tile = self.tiled_encode(tile... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
def _temporal_tiled_decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
batch_size, num_channels, num_frames, height, width = z.shape
num_sample_frames = (num_frames - 1) * self.temporal_compression_ratio + 1
tile_latent_min_height = self.tile_sample_m... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
row = []
for i in range(0, num_frames, tile_latent_stride_num_frames):
tile = z[:, :, i : i + tile_latent_min_num_frames + 1, :, :]
if self.use_tiling and (tile.shape[-1] > tile_latent_min_width or tile.shape[-2] > tile_latent_min_height):
decoded = self.tiled_decode(tile... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
def forward(
self,
sample: torch.Tensor,
sample_posterior: bool = False,
return_dict: bool = True,
generator: Optional[torch.Generator] = None,
) -> Union[DecoderOutput, torch.Tensor]:... | 1,250 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
class EnvironmentCommand(BaseDiffusersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser) -> None:
download_parser = parser.add_parser("env")
download_parser.set_defaults(func=info_command_factory)
def run(self) -> dict:
hub_version = huggingface_hub.__version... | 1,251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/commands/env.py |
flax_version = flax.__version__
jax_version = jax.__version__
jaxlib_version = jaxlib.__version__
jax_backend = jax.lib.xla_bridge.get_backend().platform
transformers_version = "not installed"
if is_transformers_available():
import transformers
... | 1,251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/commands/env.py |
xformers_version = xformers.__version__
platform_info = platform.platform()
is_google_colab_str = "Yes" if is_google_colab() else "No"
accelerator = "NA"
if platform.system() in {"Linux", "Windows"}:
try:
sp = subprocess.Popen(
["nvidia-... | 1,251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/commands/env.py |
if len(out_str) > 0:
accelerator = out_str.strip()
except FileNotFoundError:
pass
elif platform.system() == "Darwin": # Mac OS
try:
sp = subprocess.Popen(
["system_profiler", "SPDisplaysDataType"],
... | 1,251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/commands/env.py |
start = out_str.find("VRAM (Total):")
if start != -1:
start += len("VRAM (Total):")
end = out_str.find("\n", start)
accelerator += " VRAM: " + out_str[start:end].strip()
except FileNotFoundError:
pass... | 1,251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/commands/env.py |
info = {
"🤗 Diffusers version": version,
"Platform": platform_info,
"Running on Google Colab?": is_google_colab_str,
"Python version": platform.python_version(),
"PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})",
"Flax version (CPU?/... | 1,251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/commands/env.py |
print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n")
print(self.format_dict(info))
return info
@staticmethod
def format_dict(d: dict) -> str:
return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n" | 1,251 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/commands/env.py |
class BaseDiffusersCLICommand(ABC):
@staticmethod
@abstractmethod
def register_subcommand(parser: ArgumentParser):
raise NotImplementedError()
@abstractmethod
def run(self):
raise NotImplementedError() | 1,252 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/commands/__init__.py |
class FP16SafetensorsCommand(BaseDiffusersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
conversion_parser = parser.add_parser("fp16_safetensors")
conversion_parser.add_argument(
"--ckpt_id",
type=str,
help="Repo id of the checkpoi... | 1,253 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/commands/fp16_safetensors.py |
def __init__(self, ckpt_id: str, fp16: bool, use_safetensors: bool):
self.logger = logging.get_logger("diffusers-cli/fp16_safetensors")
self.ckpt_id = ckpt_id
self.local_ckpt_dir = f"/tmp/{ckpt_id}"
self.fp16 = fp16
self.use_safetensors = use_safetensors
if not self.use... | 1,253 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/commands/fp16_safetensors.py |
model_index = hf_hub_download(repo_id=self.ckpt_id, filename="model_index.json")
with open(model_index, "r") as f:
pipeline_class_name = json.load(f)["_class_name"]
pipeline_class = getattr(import_module("diffusers"), pipeline_class_name)
self.logger.info(f"Pipeline class imported: {... | 1,253 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/commands/fp16_safetensors.py |
# Fetch all the paths.
if self.fp16:
modified_paths = glob.glob(f"{self.local_ckpt_dir}/*/*.fp16.*")
elif self.use_safetensors:
modified_paths = glob.glob(f"{self.local_ckpt_dir}/*/*.safetensors")
# Prepare for the PR.
commit_message = f"Serialize variables with ... | 1,253 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/commands/fp16_safetensors.py |
# Open the PR.
commit_description = (
"Variables converted by the [`diffusers`' `fp16_safetensors`"
" CLI](https://github.com/huggingface/diffusers/blob/main/src/diffusers/commands/fp16_safetensors.py)."
)
hub_pr_url = create_commit(
repo_id=self.ckpt_id,
... | 1,253 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/commands/fp16_safetensors.py |
class SD3Transformer2DLoadersMixin:
"""Load IP-Adapters and LoRA layers into a `[SD3Transformer2DModel]`."""
def _load_ip_adapter_weights(self, state_dict: Dict, low_cpu_mem_usage: bool = _LOW_CPU_MEM_USAGE_DEFAULT) -> None:
"""Sets IP-Adapter attention processors, image projection, and loads state_dic... | 1,254 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/transformer_sd3.py |
Args:
state_dict (`Dict`):
State dict with keys "ip_adapter", which contains parameters for attention processors, and
"image_proj", which contains parameters for image projection net.
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.... | 1,254 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/transformer_sd3.py |
timesteps_emb_dim = state_dict["ip_adapter"]["0.norm_ip.linear.weight"].shape[1] | 1,254 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/transformer_sd3.py |
# Dict where key is transformer layer index, value is attention processor's state dict
# ip_adapter state dict keys example: "0.norm_ip.linear.weight"
layer_state_dict = {idx: {} for idx in range(len(self.attn_processors))}
for key, weights in state_dict["ip_adapter"].items():
idx, n... | 1,254 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/transformer_sd3.py |
if not low_cpu_mem_usage:
attn_procs[name].load_state_dict(layer_state_dict[idx], strict=True)
else:
load_model_dict_into_meta(
attn_procs[name], layer_state_dict[idx], device=self.device, dtype=self.dtype
)
self.set_attn_processor... | 1,254 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/transformer_sd3.py |
# Image projection
self.image_proj = IPAdapterTimeImageProjection(
embed_dim=embed_dim,
output_dim=output_dim,
hidden_dim=hidden_dim,
heads=heads,
num_queries=num_queries,
timestep_in_dim=timestep_in_dim,
).to(device=self.device, dt... | 1,254 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/transformer_sd3.py |
class FromSingleFileMixin:
"""
Load model weights saved in the `.ckpt` format into a [`DiffusionPipeline`].
"""
@classmethod
@validate_hf_hub_args
def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
r"""
Instantiate a [`DiffusionPipeline`] from pretrained pipelin... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
Parameters:
pretrained_model_link_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A link to the `.ckpt` file (for example
`"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt"`) on the Hub.
- A path to a... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.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`):
... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
The path to the original config file that was used to train the model. If not provided, the config file
will be inferred from the checkpoint file.
config (`str`, *optional*):
Can be either:
- A string, the *repo id* (for example `CompVis/ldm-text2im-large-... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
class). The overwritten components are passed directly to the pipelines `__init__` method. See example
below for more information. | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
Examples:
```py
>>> from diffusers import StableDiffusionPipeline
>>> # Download pipeline from huggingface.co and cache.
>>> pipeline = StableDiffusionPipeline.from_single_file(
... "https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrang... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
"""
original_config_file = kwargs.pop("original_config_file", None)
config = kwargs.pop("config", None)
original_config = kwargs.pop("original_config", None)
if original_config_file is not None:
deprecation_message = (
"`original_config_file` argument is depr... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
is_legacy_loading = False
# We shouldn't allow configuring individual models components through a Pipeline creation method
# These model kwargs should be deprecated
scaling_factor = kwargs.get("scaling_factor", None)
if scaling_factor is not None:
deprecation_message = (
... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
checkpoint = load_single_file_checkpoint(
pretrained_model_link_or_path,
force_download=force_download,
proxies=proxies,
token=token,
cache_dir=cache_dir,
local_files_only=local_files_only,
revision=revision,
disable_mmap=di... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
if not os.path.isdir(default_pretrained_model_config_name):
# Provided config is a repo_id
if default_pretrained_model_config_name.count("/") > 1:
raise ValueError(
f'The provided config "{config}"'
" is neither a valid local path nor a val... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
except LocalEntryNotFoundError:
# `local_files_only=True` but a local diffusers format model config is not available in the cache
# If `original_config` is not provided, we need override `local_files_only` to False
# to fetch the config files from the hub so that we have ... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
if original_config is None:
logger.warning(
"`local_files_only` is True but no local configs were found for this checkpoint.\n"
"Attempting to download the necessary config files for this pipeline.\n"
)
cached_mo... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
else:
# For backwards compatibility
# If `original_config` is provided, then we need to assume we are using legacy loading for pipeline components
logger.warning(
"Detected legacy `from_single_file` loading behavior. Attempting to creat... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
is_legacy_loading = True
cached_model_config_path = None | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
config_dict = _infer_pipeline_config_dict(pipeline_class)
config_dict["_class_name"] = pipeline_class.__name__
else:
# Provided config is a path to a local directory attempt to load directly.
cached_model_config_path = default_pretrained_model_config_name
... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
init_dict, unused_kwargs, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
init_kwargs = {k: init_dict.pop(k) for k in optional_kwargs if k in init_dict}
init_kwargs = {**init_kwargs, **passed_pipe_kwargs}
from diffusers import pipelines
# remove `null` components
de... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
if name in passed_class_obj:
loaded_sub_model = passed_class_obj[name] | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
else:
try:
loaded_sub_model = load_single_file_sub_model(
library_name=library_name,
class_name=class_name,
name=name,
checkpoint=checkpoint,
is_pipeline_module=is_... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
f"Please load the component before passing it in as an argument to `from_single_file`.\n"
f"\n"
f"{name} = {class_name}.from_pretrained('...')\n"
f"pipe = {pipeline_class.__name__}.from_single_file(<checkpoint path>, {name}={name})\n"
... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
init_kwargs[name] = loaded_sub_model
missing_modules = set(expected_modules) - set(init_kwargs.keys())
passed_modules = list(passed_class_obj.keys())
optional_modules = pipeline_class._optional_components
if len(missing_modules) > 0 and missing_modules <= set(passed_modules + optional_... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
# deprecated kwargs
load_safety_checker = kwargs.pop("load_safety_checker", None)
if load_safety_checker is not None:
deprecation_message = (
"Please pass instances of `StableDiffusionSafetyChecker` and `AutoImageProcessor`"
"using the `safety_checker` and `fe... | 1,255 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py |
class PeftAdapterMixin:
"""
A class containing all functions for loading and using adapters weights that are supported in PEFT library. For
more details about adapters and injecting them in a base model, check out the PEFT
[documentation](https://huggingface.co/docs/peft/index).
Install the latest ... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
Returns:
tuple:
A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.
"""
return _func_optionally_disable_offloading(_pipeline=_pipeline)
def load_lora_adapter(self, pretrained_model_name_or_path_or_dict, prefix="transformer", **kwargs):
... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
prefix (`str`, *optional*): Prefix to filter the state dict. | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)dow... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
`diffusers-cli login` (stored in `~/.huggingface`) is used.
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a ta... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
"""
from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict
from peft.tuners.tuners_utils im... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", None)
token = kwargs.pop("token", None)
revision = kwargs.pop("revision", None)
subfolder... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
user_agent = {
"file_type": "attn_procs_weights",
"framework": "pytorch",
}
state_dict = _fetch_state_dict(
pretrained_model_name_or_path_or_dict=pretrained_model_name_or_path_or_dict,
weight_name=weight_name,
use_safetensors=use_safetensors,
... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
if prefix is not None:
keys = list(state_dict.keys())
model_keys = [k for k in keys if k.startswith(f"{prefix}.")]
if len(model_keys) > 0:
state_dict = {k.replace(f"{prefix}.", ""): v for k, v in state_dict.items() if k in model_keys}
if len(state_dict) > 0:
... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
rank = {}
for key, val in state_dict.items():
# Cannot figure out rank from lora layers that don't have atleast 2 dimensions.
# Bias layers in LoRA only have a single dimension
if "lora_B" in key and val.ndim > 1:
rank[key] = val.shape[1]
... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
if "use_dora" in lora_config_kwargs:
if lora_config_kwargs["use_dora"]:
if is_peft_version("<", "0.9.0"):
raise ValueError(
"You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
lora_config = LoraConfig(**lora_config_kwargs)
# adapter_name
if adapter_name is None:
adapter_name = get_adapter_name(self)
# <Unsafe code
# We can be sure that the following works as it just sets attention processors, lora layers and puts all in the sam... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
# To handle scenarios where we cannot successfully set state dict. If it's unsucessful,
# we should also delete the `peft_config` associated to the `adapter_name`.
try:
inject_adapter_in_model(lora_config, self, adapter_name=adapter_name, **peft_kwargs)
incompatib... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
self.peft_config.pop(adapter_name)
logger.error(f"Loading {adapter_name} was unsucessful with the following error: \n{e}")
raise
warn_msg = ""
if incompatible_keys is not None:
# Check only for unexpected keys.
unexpected_keys = ge... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.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:
... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
def save_lora_adapter(
self,
save_directory,
adapter_name: str = "default",
upcast_before_saving: bool = False,
safe_serialization: bool = True,
weight_name: Optional[str] = None,
):
"""
Save the LoRA parameters corresponding to the underlying model. | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to save LoRA parameters to. Will be created if it doesn't exist.
adapter_name: (`str`, defaults to "default"): The name of the adapter to serialize. Useful when the
underlying model has multiple adapter... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
weight_name: (`str`, *optional*, defaults to `None`): Name of the file to serialize the state dict with.
"""
from peft.utils import get_peft_model_state_dict | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
from .lora_base import LORA_WEIGHT_NAME, LORA_WEIGHT_NAME_SAFE
if adapter_name is None:
adapter_name = get_adapter_name(self)
if adapter_name not in getattr(self, "peft_config", {}):
raise ValueError(f"Adapter name {adapter_name} not found in the model.")
lora_layers_t... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
if weight_name is None:
if safe_serialization:
weight_name = LORA_WEIGHT_NAME_SAFE
else:
weight_name = LORA_WEIGHT_NAME
# TODO: we could consider saving the `peft_config` as well.
save_path = Path(save_directory, weight_name).as_posix()
sa... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
Example:
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights(
"jbil... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
# Expand weights into a list, one entry per adapter
# examples for e.g. 2 adapters: [{...}, 7] -> [7,7] ; None -> [None, None]
if not isinstance(weights, list):
weights = [weights] * len(adapter_names)
if len(adapter_names) != len(weights):
raise ValueError(
... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
def add_adapter(self, adapter_config, adapter_name: str = "default") -> None:
r"""
Adds a new adapter to the current model for training. If no adapter name is passed, a default name is assigned
to the adapter to follow the convention of the PEFT library.
If you are not familiar with ada... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
if not is_peft_available():
raise ImportError("PEFT is not available. Please install PEFT to use this function: `pip install peft`.")
from peft import PeftConfig, inject_adapter_in_model
if not self._hf_peft_config_loaded:
self._hf_peft_config_loaded = True
elif adapter... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
# Unlike transformers, here we don't need to retrieve the name_or_path of the unet as the loading logic is
# handled by the `load_lora_layers` or `StableDiffusionLoraLoaderMixin`. Therefore we set it to `None` here.
adapter_config.base_model_name_or_path = None
inject_adapter_in_model(adapter_co... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
if not self._hf_peft_config_loaded:
raise ValueError("No adapter loaded. Please load an adapter first.")
if isinstance(adapter_name, str):
adapter_name = [adapter_name]
missing = set(adapter_name) - set(self.peft_config)
if len(missing) > 0:
raise ValueError... | 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, "set_adapter"):
module.set_adapter(adapter_name)
# Previous versions of PEFT does not support multi-adapter inference
elif not hasattr(module, ... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
if not _adapters_has_been_set:
raise ValueError(
"Did not succeeded in setting the adapter. Please make sure you are using a model that supports adapters."
)
def disable_adapters(self) -> None:
r"""
Disable all adapters attached to the model and fallback to i... | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
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