Repository: huggingface/diffusers

Issue:
issue with DDIM scheduler for google/ddpm-celebahq-256 example
### Describe the bug

Referring to the example "Unconditional Diffusion with discrete scheduler" at https://github.com/huggingface/diffusers#running-code
The comment says you can use `DDIMPipeline` but this fails to run on
(got an unexpected keyword argument `eta`).
Also, the `PNDMPipeline` runs but just returns noise.

### Reproduction

```

# !pip install diffusers["torch"]
from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline

model_id = "google/ddpm-celebahq-256"
device = "cuda"

# load model and scheduler

#ddpm = DDPMPipeline.from_pretrained(model_id)
ddpm = DDIMPipeline.from_pretrained(model_id)
#ddpm = PNDMPipeline.from_pretrained(model_id)

# you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
ddpm.to(device)

# run pipeline in inference (sample random noise and denoise)
image = ddpm().images[0]
image
```

### Logs

```shell
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Input In [21], in <cell line: 17>()
     14 ddpm.to(device)
     16 # run pipeline in inference (sample random noise and denoise)
---> 17 image = ddpm().images[0]
     18 image

File /usr/local/lib/python3.9/dist-packages/torch/autograd/grad_mode.py:27, in _DecoratorContextManager.__call__.<locals>.decorate_context(*args, **kwargs)
     24 @functools.wraps(func)
     25 def decorate_context(*args, **kwargs):
     26     with self.clone():
---> 27         return func(*args, **kwargs)

File /usr/local/lib/python3.9/dist-packages/diffusers/pipelines/ddim/pipeline_ddim.py:114, in DDIMPipeline.__call__(self, batch_size, generator, eta, num_inference_steps, use_clipped_model_output, output_type, return_dict, **kwargs)
    109     model_output = self.unet(image, t).sample
    111     # 2. predict previous mean of image x_t-1 and add variance depending on eta
    112     # eta corresponds to η in paper and should be between [0, 1]
    113     # do x_t -> x_t-1
--> 114     image = self.scheduler.step(
    115         model_output, t, image, eta=eta, use_clipped_model_output=use_clipped_model_output, generator=generator
    116     ).prev_sample
    118 image = (image / 2 + 0.5).clamp(0, 1)
    119 image = image.cpu().permute(0, 2, 3, 1).numpy()

File /usr/local/lib/python3.9/dist-packages/diffusers/schedulers/scheduling_ddpm.py:259, in DDPMScheduler.step(self, model_output, timestep, sample, generator, return_dict, **kwargs)
    237 """
    238 Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
    239 process from the learned model outputs (most often the predicted noise).
   (...)
    253 
    254 """
    255 message = (
    256     "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
    257     " DDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
    258 )
--> 259 predict_epsilon = deprecate("predict_epsilon", "0.11.0", message, take_from=kwargs)
    260 if predict_epsilon is not None:
    261     new_config = dict(self.config)

File /usr/local/lib/python3.9/dist-packages/diffusers/utils/deprecation_utils.py:43, in deprecate(take_from, standard_warn, *args)
     41     function = call_frame.function
     42     key, value = next(iter(deprecated_kwargs.items()))
---> 43     raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`")
     45 if len(values) == 0:
     46     return

TypeError: step in /usr/local/lib/python3.9/dist-packages/diffusers/schedulers/scheduling_ddpm.py line 258 got an unexpected keyword argument `eta`

```
```


### System Info



- `diffusers` version: 0.10.2
- Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31
- Python version: 3.9.13
- PyTorch version (GPU?): 1.12.0+cu116 (True)
- Huggingface_hub version: 0.11.1
- Transformers version: 4.20.1
- Using GPU in script?: Yes. P6000
- Using distributed or parallel set-up in script?: No


Discussion:
The unexpected keyword `eta` error will be fixed in #1932.
Hey @willdalh,

Can we close this one or is there still an issue? 

Resolving patch:
diff --git a/src/diffusers/pipelines/pndm/pipeline_pndm.py b/src/diffusers/pipelines/pndm/pipeline_pndm.py
--- a/src/diffusers/pipelines/pndm/pipeline_pndm.py
+++ b/src/diffusers/pipelines/pndm/pipeline_pndm.py
@@ -39,6 +39,9 @@ class PNDMPipeline(DiffusionPipeline):
 
     def __init__(self, unet: UNet2DModel, scheduler: PNDMScheduler):
         super().__init__()
+
+        scheduler = PNDMScheduler.from_config(scheduler.config)
+
         self.register_modules(unet=unet, scheduler=scheduler)
 
     @torch.no_grad()


Test patch: