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Update CLIPFeatureExtractor to CLIPImageProcessor and DPTFeatureExtractor to DPTImageProcessor (#9002)

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* fix: update `CLIPFeatureExtractor` to `CLIPImageProcessor` in codebase

* `make style && make quality`

* Update `DPTFeatureExtractor` to `DPTImageProcessor` in codebase

* `make style`

---------

Co-authored-by: Aryan <aryan@huggingface.co>
This commit is contained in:
Tolga Cangöz
2024-08-05 22:20:29 +03:00
committed by GitHub
parent 6d32b29239
commit 3dc97bd148
30 changed files with 73 additions and 77 deletions
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4
docs/source/en/using-diffusers/custom_pipeline_overview.md
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@@ -289,9 +289,9 @@ scheduler = DPMSolverMultistepScheduler.from_pretrained(pipe_id, subfolder="sche
3. Load an image processor:
```python
from transformers import CLIPFeatureExtractor
from transformers import CLIPImageProcessor
feature_extractor = CLIPFeatureExtractor.from_pretrained(pipe_id, subfolder="feature_extractor")
feature_extractor = CLIPImageProcessor.from_pretrained(pipe_id, subfolder="feature_extractor")
```
<Tip warning={true}>

4
docs/source/en/using-diffusers/inference_with_tcd_lora.md
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@@ -212,14 +212,14 @@ TCD-LoRA is very versatile, and it can be combined with other adapter types like
import torch
import numpy as np
from PIL import Image
from transformers import DPTFeatureExtractor, DPTForDepthEstimation
from transformers import DPTImageProcessor, DPTForDepthEstimation
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
from diffusers.utils import load_image, make_image_grid
from scheduling_tcd import TCDScheduler
device = "cuda"
depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device)
feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
def get_depth_map(image):
image = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device)