chore: fix help messages in advanced diffusion examples (#10923)

Author: wonderfan

examples/advanced_diffusion_training/README_flux.md

@@ -79,13 +79,13 @@ This command will prompt you for a token. Copy-paste yours from your [settings/t
 ### Target Modules
 When LoRA was first adapted from language models to diffusion models, it was applied to the cross-attention layers in the Unet that relate the image representations with the prompts that describe them. 
 More recently, SOTA text-to-image diffusion models replaced the Unet with a diffusion Transformer(DiT). With this change, we may also want to explore 
-applying LoRA training onto different types of layers and blocks. To allow more flexibility and control over the targeted modules we added `--lora_layers`- in which you can specify in a comma seperated string
+applying LoRA training onto different types of layers and blocks. To allow more flexibility and control over the targeted modules we added `--lora_layers`- in which you can specify in a comma separated string
 the exact modules for LoRA training. Here are some examples of target modules you can provide: 
 - for attention only layers: `--lora_layers="attn.to_k,attn.to_q,attn.to_v,attn.to_out.0"`
 - to train the same modules as in the fal trainer: `--lora_layers="attn.to_k,attn.to_q,attn.to_v,attn.to_out.0,attn.add_k_proj,attn.add_q_proj,attn.add_v_proj,attn.to_add_out,ff.net.0.proj,ff.net.2,ff_context.net.0.proj,ff_context.net.2"`
 - to train the same modules as in ostris ai-toolkit / replicate trainer: `--lora_blocks="attn.to_k,attn.to_q,attn.to_v,attn.to_out.0,attn.add_k_proj,attn.add_q_proj,attn.add_v_proj,attn.to_add_out,ff.net.0.proj,ff.net.2,ff_context.net.0.proj,ff_context.net.2,norm1_context.linear, norm1.linear,norm.linear,proj_mlp,proj_out"`
 > [!NOTE]
-> `--lora_layers` can also be used to specify which **blocks** to apply LoRA training to. To do so, simply add a block prefix to each layer in the comma seperated string:
+> `--lora_layers` can also be used to specify which **blocks** to apply LoRA training to. To do so, simply add a block prefix to each layer in the comma separated string:
 > **single DiT blocks**: to target the ith single transformer block, add the prefix `single_transformer_blocks.i`, e.g. - `single_transformer_blocks.i.attn.to_k`
 > **MMDiT blocks**: to target the ith MMDiT block, add the prefix `transformer_blocks.i`, e.g. - `transformer_blocks.i.attn.to_k` 
 > [!NOTE]

examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py

@@ -378,7 +378,7 @@ def parse_args(input_args=None):
         default=None,
         help="the concept to use to initialize the new inserted tokens when training with "
         "--train_text_encoder_ti = True. By default, new tokens (<si><si+1>) are initialized with random value. "
-        "Alternatively, you could specify a different word/words whos value will be used as the starting point for the new inserted tokens. "
+        "Alternatively, you could specify a different word/words whose value will be used as the starting point for the new inserted tokens. "
         "--num_new_tokens_per_abstraction is ignored when initializer_concept is provided",
     )
     parser.add_argument(
@@ -662,7 +662,7 @@ def parse_args(input_args=None):
         type=str,
         default=None,
         help=(
-            "The transformer modules to apply LoRA training on. Please specify the layers in a comma seperated. "
+            "The transformer modules to apply LoRA training on. Please specify the layers in a comma separated. "
             'E.g. - "to_k,to_q,to_v,to_out.0" will result in lora training of attention layers only. For more examples refer to https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/README_flux.md'
         ),
     )

examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py

@@ -662,7 +662,7 @@ def parse_args(input_args=None):
         action="store_true",
         default=False,
         help=(
-            "Wether to train a DoRA as proposed in- DoRA: Weight-Decomposed Low-Rank Adaptation https://arxiv.org/abs/2402.09353. "
+            "Whether to train a DoRA as proposed in- DoRA: Weight-Decomposed Low-Rank Adaptation https://arxiv.org/abs/2402.09353. "
             "Note: to use DoRA you need to install peft from main, `pip install git+https://github.com/huggingface/peft.git`"
         ),
     )

examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py

@@ -773,7 +773,7 @@ def parse_args(input_args=None):
         action="store_true",
         default=False,
         help=(
-            "Wether to train a DoRA as proposed in- DoRA: Weight-Decomposed Low-Rank Adaptation https://arxiv.org/abs/2402.09353. "
+            "Whether to train a DoRA as proposed in- DoRA: Weight-Decomposed Low-Rank Adaptation https://arxiv.org/abs/2402.09353. "
             "Note: to use DoRA you need to install peft from main, `pip install git+https://github.com/huggingface/peft.git`"
         ),
     )
@@ -1875,7 +1875,7 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers, clip_skip):
     # pack the statically computed variables appropriately here. This is so that we don't
     # have to pass them to the dataloader.
 
-    # if --train_text_encoder_ti we need add_special_tokens to be True fo textual inversion
+    # if --train_text_encoder_ti we need add_special_tokens to be True for textual inversion
     add_special_tokens = True if args.train_text_encoder_ti else False
 
     if not train_dataset.custom_instance_prompts: