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Update basic_training.mdx (huggingface#2639)
Add 'import os'
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docs/source/en/tutorials/basic_training.mdx

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@@ -252,6 +252,7 @@ Then, you'll need a way to evaluate the model. For evaluation, you can use the [
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```py
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>>> from diffusers import DDPMPipeline
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>>> import math
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>>> import os
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>>> def make_grid(images, rows, cols):
@@ -411,4 +412,4 @@ Unconditional image generation is one example of a task that can be trained. You
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* [Textual Inversion](./training/text_inversion), an algorithm that teaches a model a specific visual concept and integrates it into the generated image.
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* [DreamBooth](./training/dreambooth), a technique for generating personalized images of a subject given several input images of the subject.
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* [Guide](./training/text2image) to finetuning a Stable Diffusion model on your own dataset.
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* [Guide](./training/lora) to using LoRA, a memory-efficient technique for finetuning really large models faster.
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* [Guide](./training/lora) to using LoRA, a memory-efficient technique for finetuning really large models faster.

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