text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
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|---|---|---|
extra_loras = unet._load_ip_adapter_loras(state_dicts)
if extra_loras != {}:
if not USE_PEFT_BACKEND:
logger.warning("PEFT backend is required to load these weights.")
else:
# apply the IP Adapter Face ID LoRA weights
peft_config = getattr(... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
```py
# To use original IP-Adapter
scale = 1.0
pipeline.set_ip_adapter_scale(scale)
# To use style block only
scale = {
"up": {"block_0": [0.0, 1.0, 0.0]},
}
pipeline.set_ip_adapter_scale(scale)
# To use style+layout blocks
scale = {
... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
for attn_name, attn_processor in unet.attn_processors.items():
if isinstance(
attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0, IPAdapterXFormersAttnProcessor)
):
if len(scale_configs) != len(attn_processor.scale):
raise Value... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
def unload_ip_adapter(self):
"""
Unloads the IP Adapter weights
Examples:
```python
>>> # Assuming `pipeline` is already loaded with the IP Adapter weights.
>>> pipeline.unload_ip_adapter()
>>> ...
```
"""
# remove CLIP image encoder
... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
# remove hidden encoder
self.unet.encoder_hid_proj = None
self.unet.config.encoder_hid_dim_type = None
# Kolors: restore `encoder_hid_proj` with `text_encoder_hid_proj`
if hasattr(self.unet, "text_encoder_hid_proj") and self.unet.text_encoder_hid_proj is not None:
self.unet.... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
# restore original Unet attention processors layers
attn_procs = {}
for name, value in self.unet.attn_processors.items():
attn_processor_class = (
AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnProcessor()
)
attn_procs[name] ... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
class FluxIPAdapterMixin:
"""Mixin for handling Flux IP Adapters."""
@validate_hf_hub_args
def load_ip_adapter(
self,
pretrained_model_name_or_path_or_dict: Union[str, List[str], Dict[str, torch.Tensor]],
weight_name: Union[str, List[str]],
subfolder: Optional[Union[str, Lis... | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.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`].
... | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
Can be either: | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
- A string, the *model id* (for example `openai/clip-vit-large-patch14`) 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`].
... | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.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`):
... | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
Speed up model loading only loading the pretrained weights and not initializing the weights. This also
tries to not use more than 1x model size in CPU memory (including peak memory) while l... | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
# handle the list inputs for multiple IP Adapters
if not isinstance(weight_name, list):
weight_name = [weight_name]
if not isinstance(pretrained_model_name_or_path_or_dict, list):
pretrained_model_name_or_path_or_dict = [pretrained_model_name_or_path_or_dict]
if len(pret... | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
# Load the main state dict first.
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 = kwar... | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
raise NotImplementedError(
"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
" `low_cpu_mem_usage=False`."
) | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
user_agent = {
"file_type": "attn_procs_weights",
"framework": "pytorch",
}
state_dicts = []
for pretrained_model_name_or_path_or_dict, weight_name, subfolder in zip(
pretrained_model_name_or_path_or_dict, weight_name, subfolder
):
if not i... | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
with safe_open(model_file, framework="pt", device="cpu") as f:
image_proj_keys = ["ip_adapter_proj_model.", "image_proj."]
ip_adapter_keys = ["double_blocks.", "ip_adapter."]
for key in f.keys():
if any(key.startswith(pr... | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
)
state_dict["ip_adapter"][diffusers_name] = f.get_tensor(key)
else:
state_dict = load_state_dict(model_file)
else:
state_dict = pretrained_model_name_or_path_or_dict | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
keys = list(state_dict.keys())
if keys != ["image_proj", "ip_adapter"]:
raise ValueError("Required keys are (`image_proj` and `ip_adapter`) missing from the state dict.")
state_dicts.append(state_dict) | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
# load CLIP image encoder here if it has not been registered to the pipeline yet
if hasattr(self, "image_encoder") and getattr(self, "image_encoder", None) is None:
if image_encoder_pretrained_model_name_or_path is not None:
if not isinstance(pretrained_model_name_or_path... | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
.eval()
)
self.register_modules(image_encoder=image_encoder)
else:
raise ValueError(
"`image_encoder` cannot be loaded because `pretrained_model_name_or_path_or_dict` is a state dict."
... | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
# create feature extractor if it has not been registered to the pipeline yet
if hasattr(self, "feature_extractor") and getattr(self, "feature_extractor", None) is None:
# FaceID IP adapters don't need the image encoder so it's not present, in this case we default to 224
defau... | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
def set_ip_adapter_scale(self, scale: Union[float, List[float], List[List[float]]]):
"""
Set IP-Adapter scales per-transformer block. Input `scale` could be a single config or a list of configs for
granular control over each IP-Adapter behavior. A config can be a float or a list.
`float... | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
ip_strengths = LinearStrengthModel(0.3, 0.92, 19)
pipeline.set_ip_adapter_scale(ip_strengths)
```
"""
transformer = self.transformer
if not isinstance(scale, list):
scale = [[scale] * transformer.config.num_layers]
elif isinstance(scale, list) and isinstance(s... | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
key_id = 0
for attn_name, attn_processor in transformer.attn_processors.items():
if isinstance(attn_processor, (FluxIPAdapterJointAttnProcessor2_0)):
if len(scale_configs) != len(attn_processor.scale):
raise ValueError(
f"Cannot assign {len... | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
```python
>>> # Assuming `pipeline` is already loaded with the IP Adapter weights.
>>> pipeline.unload_ip_adapter()
>>> ...
```
"""
# remove CLIP image encoder
if hasattr(self, "image_encoder") and getattr(self, "image_encoder", None) is not None:
self... | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
# restore original Transformer attention processors layers
attn_procs = {}
for name, value in self.transformer.attn_processors.items():
attn_processor_class = FluxAttnProcessor2_0()
attn_procs[name] = (
attn_processor_class if isinstance(value, (FluxIPAdapterJoint... | 1,274 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
class SD3IPAdapterMixin:
"""Mixin for handling StableDiffusion 3 IP Adapters."""
@property
def is_ip_adapter_active(self) -> bool:
"""Checks if IP-Adapter is loaded and scale > 0.
IP-Adapter scale controls the influence of the image prompt versus text prompt. When this value is set to 0,
... | 1,275 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
@validate_hf_hub_args
def load_ip_adapter(
self,
pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
weight_name: str = "ip-adapter.safetensors",
subfolder: Optional[str] = None,
image_encoder_folder: Optional[str] = "image_encoder",
**kwargs,
... | 1,275 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
weight_name (`str`, defaults to "ip-adapter.safetensors"):
The name of the weight file to load. If a list is passed, it should have the same length as
`subfolder`.
subfolder (`str`, *optional*):
The subfolder location of a model file within a larger model repo... | 1,275 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
`subfolder`, you should pass the path to the folder that contains image encoder weights, for example,
`image_encoder_folder="different_subfolder/image_encoder"`.
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory where a downloaded pretrained model configu... | 1,275 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
Whether to only load local model weights and configuration files or not. If set to `True`, the model
won't be downloaded from the Hub.
token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
... | 1,275 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
argument to `True` will raise an error.
"""
# Load the main state dict first
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
... | 1,275 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
if low_cpu_mem_usage and not is_accelerate_available():
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 r... | 1,275 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
if not isinstance(pretrained_model_name_or_path_or_dict, dict):
model_file = _get_model_file(
pretrained_model_name_or_path_or_dict,
weights_name=weight_name,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
... | 1,275 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
else:
state_dict = load_state_dict(model_file)
else:
state_dict = pretrained_model_name_or_path_or_dict | 1,275 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
keys = list(state_dict.keys())
if "image_proj" not in keys and "ip_adapter" not in keys:
raise ValueError("Required keys are (`image_proj` and `ip_adapter`) missing from the state dict.")
# Load image_encoder and feature_extractor here if they haven't been registered to the pipeline yet
... | 1,275 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
# Commons args for loading image encoder and image processor
kwargs = {
"low_cpu_mem_usage": low_cpu_mem_usage,
"cache_dir": cache_dir,
"local_files_only": local_files_only,
} | 1,275 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
self.register_modules(
feature_extractor=SiglipImageProcessor.from_pretrained(image_encoder_subfolder, **kwargs).to(
self.device, dtype=self.dtype
),
image_encoder=SiglipVisionModel.from_pretrained(image_encoder_subfolde... | 1,275 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
# Load IP-Adapter into transformer
self.transformer._load_ip_adapter_weights(state_dict, low_cpu_mem_usage=low_cpu_mem_usage)
def set_ip_adapter_scale(self, scale: float) -> None:
"""
Set IP-Adapter scale, which controls image prompt conditioning. A value of 1.0 means the model is only
... | 1,275 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
"""
for attn_processor in self.transformer.attn_processors.values():
if isinstance(attn_processor, SD3IPAdapterJointAttnProcessor2_0):
attn_processor.scale = scale
def unload_ip_adapter(self) -> None:
"""
Unloads the IP Adapter weights.
Example:
... | 1,275 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
# Remove image projection
self.transformer.image_proj = None
# Restore original attention processors layers
attn_procs = {
name: (
JointAttnProcessor2_0() if isinstance(value, SD3IPAdapterJointAttnProcessor2_0) else value.__class__()
)
for nam... | 1,275 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
class AttnProcsLayers(torch.nn.Module):
def __init__(self, state_dict: Dict[str, torch.Tensor]):
super().__init__()
self.layers = torch.nn.ModuleList(state_dict.values())
self.mapping = dict(enumerate(state_dict.keys()))
self.rev_mapping = {v: k for k, v in enumerate(state_dict.keys(... | 1,276 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/utils.py |
def remap_key(key, state_dict):
for k in self.split_keys:
if k in key:
return key.split(k)[0] + k
raise ValueError(
f"There seems to be a problem with the state_dict: {set(state_dict.keys())}. {key} has to have one of {self.split_keys}."
... | 1,276 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/utils.py |
class FluxTransformer2DLoadersMixin:
"""
Load layers into a [`FluxTransformer2DModel`].
"""
def _convert_ip_adapter_image_proj_to_diffusers(self, state_dict, low_cpu_mem_usage=False):
if low_cpu_mem_usage:
if is_accelerate_available():
from accelerate import init_emp... | 1,277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/transformer_flux.py |
if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
raise NotImplementedError(
"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
" `low_cpu_mem_usage=False`."
)
updated_state_dict = {}
... | 1,277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/transformer_flux.py |
with init_context():
image_projection = ImageProjection(
cross_attention_dim=cross_attention_dim,
image_embed_dim=clip_embeddings_dim,
num_image_text_embeds=num_image_text_embeds,
)
for key, value in state_dict.item... | 1,277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/transformer_flux.py |
if low_cpu_mem_usage:
if is_accelerate_available():
from accelerate import init_empty_weights
else:
low_cpu_mem_usage = False
logger.warning(
"Cannot initialize model with low cpu memory usage because `accelerate` was not found... | 1,277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/transformer_flux.py |
# set ip-adapter cross-attention processors & load state_dict
attn_procs = {}
key_id = 0
init_context = init_empty_weights if low_cpu_mem_usage else nullcontext
for name in self.attn_processors.keys():
if name.startswith("single_transformer_blocks"):
attn_proc... | 1,277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/transformer_flux.py |
# IP-Adapter
num_image_text_embeds += [num_image_text_embed] | 1,277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/transformer_flux.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,
dtype=s... | 1,277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/transformer_flux.py |
if not low_cpu_mem_usage:
attn_procs[name].load_state_dict(value_dict)
else:
device = self.device
dtype = self.dtype
load_model_dict_into_meta(attn_procs[name], value_dict, device=device, dtype=dtype)
key_id... | 1,277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/transformer_flux.py |
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
)
image_projection_layers.append(image_projection_layer)
... | 1,277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/transformer_flux.py |
class SASolverScheduler(SchedulerMixin, ConfigMixin):
"""
`SASolverScheduler` is a fast dedicated high-order solver for diffusion SDEs.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers s... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
Args:
num_train_timesteps (`int`, defaults to 1000):
The number of diffusion steps to train the model.
beta_start (`float`, defaults to 0.0001):
The starting `beta` value of inference.
beta_end (`float`, defaults to 0.02):
The final `beta` value.
beta_... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
The corrector order which can be `1` or `2` or `3` or '4'. It is recommended to use `corrector_order=2` for
guided sampling, and `corrector_order=3` for unconditional sampling.
prediction_type (`str`, defaults to `epsilon`, *optional*):
Prediction type of the scheduler function; can be `... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
thresholding (`bool`, defaults to `False`):
Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
as Stable Diffusion.
dynamic_thresholding_ratio (`float`, defaults to 0.995):
The ratio for the dynamic thresholding method.... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
use_karras_sigmas (`bool`, *optional*, defaults to `False`):
Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,
the sigmas are determined according to a sequence of noise levels {σi}.
use_exponential_sigmas (`bool`, *optional*, defau... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
Set to "learned" or "learned_range" for diffusion models that predict variance. If set, the model's output
contains the predicted Gaussian variance.
timestep_spacing (`str`, defaults to `"linspace"`):
The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
order = 1 | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.0001,
beta_end: float = 0.02,
beta_schedule: str = "linear",
trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
predictor_order: int = 2,
correc... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
timestep_spacing: str = "linspace",
steps_offset: int = 0,
):
if self.config.use_beta_sigmas and not is_scipy_available():
raise ImportError("Make sure to install scipy if you want to use beta sigmas.")
if sum([self.config.use_beta_sigmas, self.config.use_exponential_sigmas, self... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
beta_end**0.5,
num_train_timesteps,
dtype=torch.float32,
)
** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
self.betas = betas_for_alpha_bar(num_train_timesteps)
else:
... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
self.alphas = 1.0 - self.betas
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
# Currently we only support VP-type noise schedule
self.alpha_t = torch.sqrt(self.alphas_cumprod)
self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)
self.lambda_t = torch.log(self.alpha_t) - to... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
# setable values
self.num_inference_steps = None
timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy()
self.timesteps = torch.from_numpy(timesteps)
self.timestep_list = [None] * max(predictor_order, corrector_order - 1)
self.model... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
@property
def begin_index(self):
"""
The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
"""
return self._begin_index
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
def... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
Args:
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model.
device (`str` or `torch.device`, *optional*):
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
elif self.config.timestep_spacing == "leading":
step_ratio = last_timestep // (num_inference_steps + 1)
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
timesteps = (np.arange(0, num_inference_steps... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."
) | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
log_sigmas = np.log(sigmas)
if self.config.use_karras_sigmas:
sigmas = np.flip(sigmas).copy()
sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
timest... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
sigmas = self._convert_to_beta(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
sigmas = np.concatenate([sigmas, sigmas[-1:]]).astype(np.float32)
elif self.config.use_flow_sigmas:
alphas... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
self.sigmas = torch.from_numpy(sigmas)
self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.int64)
self.num_inference_steps = len(timesteps)
self.model_outputs = [
None,
] * max(self.config.predictor_order, self.config.corrector_order - 1)
self.... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:
"""
"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
prediction of x_0 at timest... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
if dtype not in (torch.float32, torch.float64):
sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half
# Flatten sample for doing quantile calculation along each image
sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))
... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
def _sigma_to_t(self, sigma, log_sigmas):
# get log sigma
log_sigma = np.log(np.maximum(sigma, 1e-10))
# get distribution
dists = log_sigma - log_sigmas[:, np.newaxis]
# get sigm... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._sigma_to_alpha_sigma_t
def _sigma_to_alpha_sigma_t(self, sigma):
if self.config.use_flow_sigmas:
alpha_t = 1 - sigma
sigma_t = sigma
else:
alpha_t = 1 / ((sigma**2 + 1) ... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
if hasattr(self.config, "sigma_max"):
sigma_max = self.config.sigma_max
else:
sigma_max = None
sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()
sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()
rho = 7.0 # 7.0 is th... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
# Hack to make sure that other schedulers which copy this function don't break
# TODO: Add this logic to the other schedulers
if hasattr(self.config, "sigma_min"):
sigma_min = self.config.sigma_min
else:
sigma_min = None
if hasattr(self.config, "sigma_max"):
... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_beta
def _convert_to_beta(
self, in_sigmas: torch.Tensor, num_inference_steps: int, alpha: float = 0.6, beta: float = 0.6
) -> torch.Tensor:
"""From "Beta Sampling is All You Need" [arXiv:2407.12173] ... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
sigmas = np.array(
[
sigma_min + (ppf * (sigma_max - sigma_min))
for ppf in [
scipy.stats.beta.ppf(timestep, alpha, beta)
for timestep in 1 - np.linspace(0, 1, num_inference_steps)
]
]
)
retur... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
</Tip>
Args:
model_output (`torch.Tensor`):
The direct output from the learned diffusion model.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
Returns:
`torch.Tensor`:
The con... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
sigma = self.sigmas[self.step_index]
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
# SA-Solver_data_prediction needs to solve an integral of the data prediction model.
if self.config.algorithm_type in ["data_prediction"]:
if self.config.prediction_type == "epsilon":
... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, "
"`v_prediction`, or `flow_prediction` for the SASolverScheduler."
) | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
if self.config.thresholding:
x0_pred = self._threshold_sample(x0_pred)
return x0_pred | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
# SA-Solver_noise_prediction needs to solve an integral of the noise prediction model.
elif self.config.algorithm_type in ["noise_prediction"]:
if self.config.prediction_type == "epsilon":
# SA-Solver only needs the "mean" output.
if self.config.variance_type in ["lea... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
if self.config.thresholding:
alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
x0_pred = (sample - sigma_t * epsilon) / alpha_t
x0_pred = self._threshold_sample(x0_pred)
epsilon = (sample - alpha_t * x0_pred) / sigma_t
return e... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
if order == 0:
return torch.exp(-interval_end) * (torch.exp(interval_end - interval_start) - 1)
elif order == 1:
return torch.exp(-interval_end) * (
(interval_start + 1) * torch.exp(interval_end - interval_start) - (interval_end + 1)
)
elif order == 2:... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
def get_coefficients_exponential_positive(self, order, interval_start, interval_end, tau):
"""
Calculate the integral of exp(x(1+tau^2)) * x^order dx from interval_start to interval_end
"""
assert order in [0, 1, 2, 3], "order is only supported for 0, 1, 2 and 3"
# after change ... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
if order == 0:
return (
torch.exp(interval_end_cov) * (1 - torch.exp(-(interval_end_cov - interval_start_cov))) / (1 + tau**2)
)
elif order == 1:
return (
torch.exp(interval_end_cov)
* (
(interval_end_cov - 1... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
(interval_end_cov**3 - 3 * interval_end_cov**2 + 6 * interval_end_cov - 6)
- (interval_start_cov**3 - 3 * interval_start_cov**2 + 6 * interval_start_cov - 6)
* torch.exp(-(interval_end_cov - interval_start_cov))
)
/ ((1 + tau**2) ** 4)
... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
def lagrange_polynomial_coefficient(self, order, lambda_list):
"""
Calculate the coefficient of lagrange polynomial
""" | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
assert order in [0, 1, 2, 3]
assert order == len(lambda_list) - 1
if order == 0:
return [[1]]
elif order == 1:
return [
[
1 / (lambda_list[0] - lambda_list[1]),
-lambda_list[1] / (lambda_list[0] - lambda_list[1]),
... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
lambda_list[1] * lambda_list[2] / denominator1,
],
[
1 / denominator2,
(-lambda_list[0] - lambda_list[2]) / denominator2,
lambda_list[0] * lambda_list[2] / denominator2,
],
[
1... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
(lambda_list[2] - lambda_list[0])
* (lambda_list[2] - lambda_list[1])
* (lambda_list[2] - lambda_list[3])
)
denominator4 = (
(lambda_list[3] - lambda_list[0])
* (lambda_list[3] - lambda_list[1])
* (lambda_list[3] - l... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
(-lambda_list[0] - lambda_list[2] - lambda_list[3]) / denominator2,
(
lambda_list[0] * lambda_list[2]
+ lambda_list[0] * lambda_list[3]
+ lambda_list[2] * lambda_list[3]
)
/ denominator2,
... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
(-lambda_list[0] - lambda_list[1] - lambda_list[2]) / denominator4,
(
lambda_list[0] * lambda_list[1]
+ lambda_list[0] * lambda_list[2]
+ lambda_list[1] * lambda_list[2]
)
/ denominator4,
... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
def get_coefficients_fn(self, order, interval_start, interval_end, lambda_list, tau):
assert order in [1, 2, 3, 4]
assert order == len(lambda_list), "the length of lambda list must be equal to the order"
coefficients = []
lagrange_coefficient = self.lagrange_polynomial_coefficient(order ... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
assert len(coefficients) == order, "the length of coefficients does not match the order"
return coefficients | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
def stochastic_adams_bashforth_update(
self,
model_output: torch.Tensor,
*args,
sample: torch.Tensor,
noise: torch.Tensor,
order: int,
tau: torch.Tensor,
**kwargs,
) -> torch.Tensor:
"""
One step for the SA-Predictor.
Args:
... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
Returns:
`torch.Tensor`:
The sample tensor at the previous timestep.
"""
prev_timestep = args[0] if len(args) > 0 else kwargs.pop("prev_timestep", None)
if sample is None:
if len(args) > 1:
sample = args[1]
else:
... | 1,278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py |
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