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[Docs] Fix typos (#7451)
* Fix typos * Fix typos * Fix typos --------- Co-authored-by: Sayak Paul <[email protected]>
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docs/source/en/training/controlnet.md

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Or if your environment doesn't support an interactive shell, like a notebook, you can use:
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```bash
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```py
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from accelerate.utils import write_basic_config
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write_basic_config()

docs/source/en/training/custom_diffusion.md

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Or if your environment doesn't support an interactive shell, like a notebook, you can use:
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```py
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from accelerate.utils import write_basic_config
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- `--freeze_model`: freezes the key and value parameters in the cross-attention layer; the default is `crossattn_kv`, but you can set it to `crossattn` to train all the parameters in the cross-attention layer
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- `--concepts_list`: to learn multiple concepts, provide a path to a JSON file containing the concepts
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- `--modifier_token`: a special word used to represent the learned concept
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- `--initializer_token`:
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- `--initializer_token`: a special word used to initialize the embeddings of the `modifier_token`
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### Prior preservation loss
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docs/source/en/training/dreambooth.md

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```py
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revision=args.revision,
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use_fast=False,
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)
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# Load scheduler and models
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noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
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text_encoder = text_encoder_cls.from_pretrained(

docs/source/en/training/instructpix2pix.md

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As with the script parameters, a walkthrough of the training script is provided in the [Text-to-image](text2image#training-script) training guide. Instead, this guide takes a look at the InstructPix2Pix relevant parts of the script.
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The script begins by modifing the [number of input channels](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L445) in the first convolutional layer of the UNet to account for InstructPix2Pix's additional conditioning image:
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The script begins by modifying the [number of input channels](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L445) in the first convolutional layer of the UNet to account for InstructPix2Pix's additional conditioning image:
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```py
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in_channels = 8

docs/source/en/training/kandinsky.md

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--validation_prompts="A robot pokemon, 4k photo" \
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--report_to="wandb" \
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--push_to_hub \
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--output_dir="kandi2-prior-pokemon-model"
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--output_dir="kandi2-prior-pokemon-model"
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```
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</hfoption>
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--validation_prompts="A robot pokemon, 4k photo" \
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--report_to="wandb" \
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--push_to_hub \
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--output_dir="kandi2-decoder-pokemon-model"
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--output_dir="kandi2-decoder-pokemon-model"
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```
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</hfoption>

docs/source/en/training/lcm_distill.md

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Congratulations on distilling a LCM model! To learn more about LCM, the following may be helpful:
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- Learn how to use [LCMs for inference](../using-diffusers/lcm) for text-to-image, image-to-image, and with LoRA checkpoints.
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- Read the [SDXL in 4 steps with Latent Consistency LoRAs](https://huggingface.co/blog/lcm_lora) blog post to learn more about SDXL LCM-LoRA's for super fast inference, quality comparisons, benchmarks, and more.
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- Read the [SDXL in 4 steps with Latent Consistency LoRAs](https://huggingface.co/blog/lcm_lora) blog post to learn more about SDXL LCM-LoRA's for super fast inference, quality comparisons, benchmarks, and more.

docs/source/en/training/sdxl.md

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docs/source/en/training/t2i_adapters.md

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docs/source/en/training/text2image.md

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docs/source/en/training/text_inversion.md

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