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Running on Zero
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3718945 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | # Copyright 2024 Stability AI, Katherine Crowson and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import numpy as np
import torch
def compute_density_for_timestep_sampling(
weighting_scheme: str, batch_size: int, logit_mean: float = None, logit_std: float = None, mode_scale: float = None
):
"""
Compute the density for sampling the timesteps when doing SD3 training.
Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
"""
if weighting_scheme == "logit_normal":
# See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$).
u = torch.normal(mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu")
u = torch.nn.functional.sigmoid(u)
elif weighting_scheme == "mode":
u = torch.rand(size=(batch_size,), device="cpu")
u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u)
else:
u = torch.rand(size=(batch_size,), device="cpu")
return u
def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None):
"""
Computes loss weighting scheme for SD3 training.
Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
"""
if weighting_scheme == "sigma_sqrt":
weighting = (sigmas**-2.0).float()
elif weighting_scheme == "cosmap":
bot = 1 - 2 * sigmas + 2 * sigmas**2
weighting = 2 / (math.pi * bot)
else:
weighting = torch.ones_like(sigmas)
return weighting
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