stabilityai/stable-diffusion-2-base DDIM config is not compatible with current DDIM implementation

### Describe the bug

Assume 30 inference steps.

There are two 925, and the number of inference steps is 31.
tensor([958, 925, 925, 892, 859, 826, 793, 760, 727, 694, 661, 628, 595, 562,
529, 496, 463, 430, 397, 364, 331, 298, 265, 232, 199, 166, 133, 100,
67, 34, 1], device='cuda:0')
```
{
"_class_name": "DDIMScheduler",
"_diffusers_version": "0.8.0",
"beta_end": 0.012,
"beta_schedule": "scaled_linear",
"beta_start": 0.00085,
"clip_sample": false,
"num_train_timesteps": 1000,
"set_alpha_to_one": false,
"skip_prk_steps": true,
"steps_offset": 1,
"trained_betas": null
}
```

The correct one should be.
tensor([958, 925, 892, 859, 826, 793, 760, 727, 694, 661, 628, 595, 562, 529,
496, 463, 430, 397, 364, 331, 298, 265, 232, 199, 166, 133, 100, 67,
34, 1], device='cuda:0')
```

{
"_class_name": "DDIMScheduler",
"_diffusers_version": "0.25.0",
"beta_end": 0.012,
"beta_schedule": "scaled_linear",
"beta_start": 0.00085,
"clip_sample": false,
"clip_sample_range": 1.0,
"dynamic_thresholding_ratio": 0.995,
"num_train_timesteps": 1000,
"prediction_type": "epsilon",
"rescale_betas_zero_snr": false,
"sample_max_value": 1.0,
"set_alpha_to_one": false,
"steps_offset": 1,
"thresholding": false,
"timestep_spacing": "leading",
"trained_betas": null
}
```

### Reproduction

See above

### Logs

_No response_

### System Info

Version: 0.26.3

### Who can help?

_No response_

Start with the DDIMScheduler implementation and the supplied scheduler configurations, comparing timestep setup for 30 inference steps. Reproduce the two tensor outputs against the stable-diffusion-2-base configuration; done means the current implementation produces the expected 30-step sequence without breaking the stated scheduler behavior.

Escrito por el modelo de indexación a partir del texto del issue.