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docs/source/inference.mdx

@@ -96,15 +96,15 @@ tokenizer.save_pretrained(save_directory)
 
 ### Weight only quantization
 
-You can also apply INT8 quantization on your models weights when exporting your model by adding `--int8`:
+You can also apply INT8 quantization on your models weights when exporting your model with the CLI:
 
 ```bash
 optimum-cli export openvino --model gpt2 --int8 ov_model
 ```
 
-This will results in the exported model linear and embedding layers to be quanrtized to INT8, while the activations will be kept in floating point precision.
+This will results in the exported model linear and embedding layers to be quanrtized to INT8, the activations will be kept in floating point precision.
 
-This can also be done when loading your model by setting `load_in_8bit=True`:
+This can also be done when loading your model by setting the `load_in_8bit` argument when calling the `from_pretrained()` method.
 
 ```python
 from optimum.intel import OVModelForCausalLM
@@ -360,13 +360,6 @@ image.save("fantasy_landscape.png")
 | `image-to-image`                     | `OVStableDiffusionXLImg2ImgPipeline` |
 
 
-Before using `OVtableDiffusionXLPipeline` make sure to have `diffusers` and `invisible_watermark` installed. You can install the libraries as follows:
-
-```bash
-pip install diffusers
-pip install invisible-watermark>=0.2.0
-```
-
 #### Text-to-Image
 
 Here is an example of how you can load a SDXL OpenVINO model from [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and run inference using OpenVINO Runtime: