Pipeline image-to-text task and Bitsandbytes error
### System Info

Python 3.10.6
Transformers 4.30.0
Bitsandbytes 0.39.1

Windows / Linux

### Who can help?

@nar

### Information

- [X] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

Using an 4 or 8bit quantised model such as:

https://huggingface.co/Mediocreatmybest/blip2-opt-2.7b_8bit




### Expected behavior

The Pipeline image processor to detect the model is running with a 4 or 8bit model with bitsandbytes.

I apologise if this should be a feature request or if it’s a bug, I couldn’t find any examples of what I was trying to do. 

When running through the pipeline examples from the hugging face website, if I try using an 8bit model, the model seems to be detected correctly and casts it to 8bit, but the Processor doesn’t seem to follow suit and runs at its default, throwing an error that they both should be set at the same floating point. 

I’ve uploaded a few models set at 8bit to save on size and memory, as BLIP2 is pretty heavy, using it on consumer devices is oviously challenging. 

The models I’ve uploaded to HuggingFace are:

Mediocreatmybest/blip2-opt-2.7b_8bit
Mediocreatmybest/blip2-opt-6.7b_8bit
Mediocreatmybest/blip2-flan-t5-xxl_8bit

I can get them working with regular methods, but as I’m a beginner it’s obviously challenging. Thanks again for all the great work!




Based on this document, it should be possible, but maybe this is just an issue with multimodal or image processors with pipeline?

https://huggingface.co/docs/transformers/main/pipeline_tutorial

_# pip install accelerate bitsandbytes
import torch
from transformers import pipeline

pipe = pipeline(model="facebook/opt-1.3b", device_map="auto", model_kwargs={"load_in_8bit": True})
output = pipe("This is a cool example!", do_sample=True, top_p=0.95)_<|||||>Also I did create a huggingface.co spaces using pipeline with the ability to try load in 8bit (obviously errors)

https://huggingface.co/spaces/Mediocreatmybest/PipelineImageCaption

Thanks. <|||||>Adding the stack trace from google colab.

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-4-ca06d49da534> in <cell line: 19>()
     17 captioner
     18 # caption
---> 19 caption = captioner(image)[0]['generated_text']
     20 print(caption)

16 frames
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight, bias)
    457                             weight, bias, self.stride,
    458                             _pair(0), self.dilation, self.groups)
--> 459         return F.conv2d(input, weight, bias, self.stride,
    460                         self.padding, self.dilation, self.groups)
    461 

RuntimeError: Input type (float) and bias type (c10::Half) should be the same<|||||>cc @younesbelkada <|||||>Hi @mediocreatmybest 
Thanks for the issue, it seems the input image needs to be converted into half-precision (`torch.float16`), can you share a small handy reproducible snippet that leads to your bug? <|||||>Thanks for the fast response!

The snippet I was using to test on google colab and on my personal device was:

```
from transformers import pipeline
import torch


image = "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png"
model = "Salesforce/blip-image-captioning-base"

model_kwargs = {"load_in_8bit": True, "torch_dtype": torch.float16}
captioner = pipeline(task="image-to-text",
model=model,
max_new_tokens=30,
model_kwargs=model_kwargs, use_fast=True
)
# load model
captioner
# caption
caption = captioner(image)[0]['generated_text']
print(caption)

```

(Copy and pasted from my mobile device, hopefully this formatted correctly)

Thanks 🙏 <|||||>I encountered similar errors while using Blip/Blip2/Git models in an image_to_text pipeline. In my case, I was working with float16 instead of 8bit precision, as under my setup I was encountering additional issues with 8bit. I think there's a very good chance that the fix I've made in #24947 might also fix your issue (for the three models I've implemented the fix for). If you're able to give it a try I'd be interested in hearing if it fixes your issue too.<|||||>> I encountered similar errors while using Blip/Blip2/Git models in an image_to_text pipeline. In my case, I was working with float16 instead of 8bit precision, as under my setup I was encountering additional issues with 8bit. I think there's a very good chance that the fix I've made in #24947 might also fix your issue (for the three models I've implemented the fix for). If you're able to give it a try I'd be interested in hearing if it fixes your issue too.

Thanks @JimAllanson, happy to try test, but I'm pretty new to Python, what is the best way to test this for you? editing the site-packages with the change? 