Remove legacy examples from inference doc, fix links in main readme

Signed-off-by: Daniel Socek <daniel.socek@intel.com>

Author: dsocek

README.md

@@ -289,11 +289,11 @@ The following model architectures, tasks and device distributions have been vali
 
 | Architecture        | Training | Inference | Tasks |
 |:--------------------|:--------:|:---------:|:------|
-| Stable Diffusion    | :heavy_check_mark: | :heavy_check_mark: | <li>[text-to-image generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion#stable-diffusion)</li><li>[image-to-image generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion#stable-diffusion-based-image-to-image)</li> |
+| Stable Diffusion    | :heavy_check_mark: | :heavy_check_mark: | <li>[text-to-image generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion)</li><li>[image-to-image generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion)</li> |
 | Stable Diffusion XL | :heavy_check_mark: | :heavy_check_mark: | <li>[text-to-image generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion#stable-diffusion-xl-sdxl)</li><li>[image-to-image generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion#stable-diffusion-xl-refiner)</li> |
-| Stable Diffusion Depth2img |         | <li>Single card</li> | <li>[depth-to-image generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion#depth-to-image-generation)</li> |
-| Stable Diffusion 3  |            | <li>Single card</li> | <li>[text-to-image generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion#stable-diffusion-3-sd3)</li> |
-| LDM3D            |               | <li>Single card</li> | <li>[text-to-image generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion#latent-diffusion-model-for-3d-ldm3d)</li> |
+| Stable Diffusion Depth2img |         | <li>Single card</li> | <li>[depth-to-image generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion)</li> |
+| Stable Diffusion 3  |            | <li>Single card</li> | <li>[text-to-image generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion#stable-diffusion-3-and-35-sd3)</li> |
+| LDM3D            |               | <li>Single card</li> | <li>[text-to-image generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion)</li> |
 | FLUX.1           | <li>LoRA</li> | <li>Single card</li> | <li>[text-to-image generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion#flux1)</li><li>[image-to-image generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion#flux1-image-to-image)</li> |
 | Text to Video    |               | <li>Single card</li> | <li>[text-to-video generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion#text-to-video-generation)</li> |
 | Image to Video   |               | <li>Single card</li> | <li>[image-to-video generation](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion#image-to-video-generation)</li> |

examples/stable-diffusion/README.md

@@ -141,6 +141,37 @@ FLUX in quantization mode by setting runtime variable `QUANT_CONFIG=quantization
 
 To run with FLUX.1-schnell model, a distilled version of FLUX.1 (which is not gated), use `--model_name_or_path black-forest-labs/FLUX.1-schnell`.
 
+## ControlNet
+
+ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543)
+by Lvmin Zhang and Maneesh Agrawala, enables conditioning the Stable Diffusion model with an additional input image.
+This allows for precise control over the composition of generated images using various features such as edges,
+pose, depth, and more.
+
+Here is how to generate images conditioned by Canny edge model:
+
+```bash
+python text_to_image_generation.py \
+    --model_name_or_path stable-diffusion-v1-5/stable-diffusion-v1-5 \
+    --controlnet_model_name_or_path lllyasviel/sd-controlnet-canny \
+    --prompts "futuristic-looking woman" \
+    --control_image https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png \
+    --num_images_per_prompt 28 \
+    --batch_size 7 \
+    --image_save_dir /tmp/controlnet_images \
+    --use_habana \
+    --use_hpu_graphs \
+    --gaudi_config Habana/stable-diffusion \
+    --sdp_on_bf16 \
+    --bf16
+```
+
+You can run inference on multiple HPUs by replacing `python text_to_image_generation.py` with
+`python ../gaudi_spawn.py --world_size <number-of-HPUs> text_to_image_generation.py` and adding option `--distributed`.
+
+This ControlNet example will preprocess the input image to derive Canny edges. Alternatively, you can use `--control_preprocessing_type none`
+to supply a preprocessed control image directly, enabling many additional use cases.
+
 ## Inpainting
 
 Inpainting replaces or edits specific areas of an image. For more details,