Code With Strange Logic in CogVideoX's Dynamic CFG

### Describe the bug

As shown at [pipeline_cogvideox_image2video.py L778](https://github.com/huggingface/diffusers/blob/38a3e4df926c59bc122191c0fc8066755e98b6d2/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py#L778C25-L778C118), [pipeline_cogvideox_video2video.py L778](https://github.com/huggingface/diffusers/blob/38a3e4df926c59bc122191c0fc8066755e98b6d2/src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py#L778), and [pipeline_cogvideox.py L697](https://github.com/huggingface/diffusers/blob/38a3e4df926c59bc122191c0fc8066755e98b6d2/src/diffusers/pipelines/cogvideo/pipeline_cogvideox.py#L697), the dynamic CFG is calculated in this way:

```python
self._guidance_scale = 1 + guidance_scale * (
(1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2
)
```
However:
- `num_inference_steps` is the number of inference denoising steps, which is default to 50.
- `t.item()` is the denoising timesteps, which range from 1 to 999.
- Therefore, `((num_inference_steps - t.item()) / num_inference_steps` is not from 1 to 0, but in fact goes to negative very fast. And after `** 5.0`, the `math.cos` will have very severe fluctuations.

I wonder: is this really the desired behavior of the CogVideoX pipeline? Shouldn't it be one of the following:
```python
self._guidance_scale = 1 + guidance_scale * (
# change t.item() to i, which is from 0 to num_inference_steps - 1
(1 - math.cos(math.pi * ((num_inference_steps - i) / num_inference_steps) ** 5.0)) / 2
)
```
```python
self._guidance_scale = 1 + guidance_scale * (
# change num_inference_steps to self.scheduler.num_train_timesteps, which is 1000
(1 - math.cos(math.pi * ((self.scheduler.num_train_timesteps - t.item()) / self.scheduler.num_train_timesteps) ** 5.0)) / 2
)
```
Both implementations will make the dynamic CFG like a cosine annealing.

Also, I think here `1 + guidance_scale * (...)` should be `1 + (guidance_scale - 1) * (...)`, otherwise its value will be 1 ~ 1 + CFG instead of 1 ~ CFG.

Please check it and fix it if it is really a bug, thank you very much.

### Reproduction

```python
import torch
from diffusers import CogVideoXImageToVideoPipeline
from diffusers.utils import export_to_video, load_image

prompt = "A little girl is riding a bicycle at high speed. Focused, detailed, realistic."
image = load_image(image="input.jpg")
pipe = CogVideoXImageToVideoPipeline.from_pretrained(
"THUDM/CogVideoX-5b-I2V",
torch_dtype=torch.bfloat16
)

pipe.enable_sequential_cpu_offload()
pipe.vae.enable_tiling()
pipe.vae.enable_slicing()

video = pipe(
prompt=prompt,
image=image,
num_videos_per_prompt=1,
num_inference_steps=50,
num_frames=49,
guidance_scale=6,
generator=torch.Generator(device="cuda").manual_seed(42),
use_dynamic_cfg=True, # Then you can print out the self._guidance_scale to see what happens.
).frames[0]

export_to_video(video, "output.mp4", fps=8)

```

### Logs

```shell
# In the following setting, `guidance_scale=4` is passed.
10/11/2024 01:36:51 - INFO - root - Denoising 1/50: cfg = 1.645743587275726
10/11/2024 01:36:55 - INFO - root - Denoising 2/50: cfg = 1.7717514333823159
10/11/2024 01:36:59 - INFO - root - Denoising 3/50: cfg = 3.9871759160877414
10/11/2024 01:37:04 - INFO - root - Denoising 4/50: cfg = 3.7101792115724193
10/11/2024 01:37:08 - INFO - root - Denoising 5/50: cfg = 1.8940487645793973
10/11/2024 01:37:13 - INFO - root - Denoising 6/50: cfg = 2.635970965321337
10/11/2024 01:37:17 - INFO - root - Denoising 7/50: cfg = 1.0187988588703782
10/11/2024 01:37:22 - INFO - root - Denoising 8/50: cfg = 2.5852000899340863
10/11/2024 01:37:26 - INFO - root - Denoising 9/50: cfg = 1.3089873683577653
10/11/2024 01:37:31 - INFO - root - Denoising 10/50: cfg = 3.9915635934173324
10/11/2024 01:37:35 - INFO - root - Denoising 11/50: cfg = 1.0023944806862168
10/11/2024 01:37:40 - INFO - root - Denoising 12/50: cfg = 1.935990650841663
10/11/2024 01:37:44 - INFO - root - Denoising 13/50: cfg = 3.9884025377098555
```

### System Info

This is a bug in the code agnostic to system.

### Who can help?

@DN6 @a-r-r-o-w @zRzRzRzRzRzRzR

Bắt đầu bằng việc kiểm tra các phép tính CFG động tại các dòng được nêu trong pipeline_cogvideox_image2video.py, pipeline_cogvideox_video2video.py và pipeline_cogvideox.py, sau đó chạy bản tái hiện CogVideoXImageToVideoPipeline được cung cấp. So sánh các giá trị guidance-scale được ghi log với việc chuẩn hóa timestep dự kiến và phạm vi CFG; hoàn tất khi hành vi đã được xác nhận hoặc sửa nhất quán trên cả ba pipeline.

Do mô hình lập chỉ mục viết ra từ nội dung của issue.