[Docs] add doc entry to explain lora fusion and use of different scales. (#4893)

* add doc entry to explain lora fusion and use of different scales.

* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

docs/source/en/training/lora.md

@@ -301,6 +301,42 @@ You can call [`~diffusers.loaders.LoraLoaderMixin.fuse_lora`] on a pipeline to m
 
 To undo `fuse_lora`, call [`~diffusers.loaders.LoraLoaderMixin.unfuse_lora`] on a pipeline.
 
+## Working with different LoRA scales when using LoRA fusion
+
+If you need to use `scale` when working with `fuse_lora()` to control the influence of the LoRA parameters on the outputs, you should specify `lora_scale` within `fuse_lora()`. Passing the `scale` parameter to `cross_attention_kwargs` when you call the pipeline won't work.  
+
+To use a different `lora_scale` with `fuse_lora()`, you should first call `unfuse_lora()` on the corresponding pipeline and call `fuse_lora()` again with the expected `lora_scale`.
+
+```python
+from diffusers import DiffusionPipeline
+import torch 
+
+pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16).to("cuda")
+lora_model_id = "hf-internal-testing/sdxl-1.0-lora"
+lora_filename = "sd_xl_offset_example-lora_1.0.safetensors"
+pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+
+# This uses a default `lora_scale` of 1.0.
+pipe.fuse_lora()
+
+generator = torch.manual_seed(0)
+images_fusion = pipe(
+    "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+).images
+
+# To work with a different `lora_scale`, first reverse the effects of `fuse_lora()`.
+pipe.unfuse_lora()
+
+# Then proceed as follows.
+pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+pipe.fuse_lora(lora_scale=0.5)
+
+generator = torch.manual_seed(0)
+images_fusion = pipe(
+    "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+).images
+```
+
 ## Supporting different LoRA checkpoints from Diffusers
 
 🤗 Diffusers supports loading checkpoints from popular LoRA trainers such as [Kohya](https://github.com/kohya-ss/sd-scripts/) and [TheLastBen](https://github.com/TheLastBen/fast-stable-diffusion). In this section, we outline the current API's details and limitations.