The density_for_timestep_sampling and loss_weighting for SD3 Training！！！

Thanks to Rafie Walker's code we can try to train SD3 models with flow-matching!
But some places don't seem to match what's in the paper.
Rafie Walker's code is below:
```
def compute_density_for_timestep_sampling(
weighting_scheme: str, batch_size: int, logit_mean: float = None, logit_std: float = None, mode_scale: float = None
):
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):
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
```
My question is below:

1. when weighting_scheme == "mode“, the code only compute the f_mode. If you need to compute 'u', you should some additional operation？
2. Cos-map seems to compute the weight of timesteps, not the weight of loss?
3. when we use logit_normal, it based on the RF-setting. So the weight of the loss should be t/(1-t), but the code doesn't compute the weight instead of torch.ones_like(sigmas)?

So I think there need some modify to correctly compute the loss of SD3!
Thanks for discussion together!

Start with compute_density_for_timestep_sampling and compute_loss_weighting_for_sd3, then compare their behavior with the SD3 paper and the RF setting described in the issue. Trace where these functions are used during training and determine whether timestep sampling and loss weighting are intentionally separate. Done means the expected formulas are resolved and the implementation reflects that decision.

Vom Indexierungsmodell aus dem Issue-Text verfasst.