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[Docs] Fix UniPC docs (#2386)

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* fix typos in the doc

* restyle the code
This commit is contained in:
Wenliang Zhao
2023-02-17 14:10:56 +08:00
committed by GitHub
parent 568b73fdf8
commit d45bb937ab
1 changed files with 10 additions and 10 deletions
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src/diffusers/schedulers/scheduling_unipc_multistep.py
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@@ -12,7 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# DISCLAIMER: This file is strongly influenced by https://github.com/LuChengTHU/dpm-solver
# DISCLAIMER: check https://arxiv.org/abs/2302.04867 and https://github.com/wl-zhao/UniPC for more info
# The codebase is modified based on https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
import math
from typing import List, Optional, Tuple, Union
@@ -67,9 +68,8 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling.
We also support the "dynamic thresholding" method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space
diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to use the dynamic
thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as
stable-diffusion).
diffusion models, you can set both `predict_x0=True` and `thresholding=True` to use the dynamic thresholding. Note
that the thresholding method is unsuitable for latent-space diffusion models (such as stable-diffusion).
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
@@ -95,9 +95,9 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
https://imagen.research.google/video/paper.pdf)
thresholding (`bool`, default `False`):
whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487).
For pixel-space diffusion models, you can set both `algorithm_type=dpmsolver++` and `thresholding=True` to
use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion
models (such as stable-diffusion).
For pixel-space diffusion models, you can set both `predict_x0=True` and `thresholding=True` to use the
dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models
(such as stable-diffusion).
dynamic_thresholding_ratio (`float`, default `0.995`):
the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen
(https://arxiv.org/abs/2205.11487).
@@ -136,7 +136,7 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
dynamic_thresholding_ratio: float = 0.995,
sample_max_value: float = 1.0,
predict_x0: bool = True,
solver_type: str = "bh1",
solver_type: str = "bh2",
lower_order_final: bool = True,
disable_corrector: List[int] = [],
solver_p: SchedulerMixin = None,
@@ -237,7 +237,7 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
" `v_prediction` for the DPMSolverMultistepScheduler."
" `v_prediction` for the UniPCMultistepScheduler."
)
if self.config.thresholding:
@@ -269,7 +269,7 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
" `v_prediction` for the DPMSolverMultistepScheduler."
" `v_prediction` for the UniPCMultistepScheduler."
)
def multistep_uni_p_bh_update(