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chore: Regenerate all playbooks

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4
nvidia/comfy-ui/README.md
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@ -99,7 +99,7 @@ Verify the virtual environment is active by checking the command prompt shows `(
Install PyTorch nightly build with CUDA 12.9 support optimized for ARM64 architecture.
```bash
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu129
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu129
```
This installation targets CUDA 12.9 compatibility with Blackwell architecture GPUs.
@ -187,7 +187,7 @@ Test the installation with a basic image generation workflow:
1. Access the web interface at `http://<SPARK_IP>:8188`
2. Load the default workflow (should appear automatically)
3. Click "Queue Prompt" to generate your first image
3. Click "Run" to generate your first image
4. Monitor GPU usage with `nvidia-smi` in a separate terminal
The image generation should complete within 30-60 seconds depending on your hardware configuration.

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nvidia/flux-finetuning/README.md
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@ -120,7 +120,7 @@ sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'
## Step 5. Dataset preparation
Let's prepare our dataset to perform Dreambooth LoRA fine-tuning on the FLUX.1-dev 12B model. However, if you wish to continue with the provided dataset of Toy Jensen and DGX Spark, feel free to skip to the Training section below. This dataset is a collection of public assets accessible via Google Images.
Let's prepare our dataset to perform Dreambooth LoRA fine-tuning on the FLUX.1-dev 12B model. However, if you wish to continue with the provided dataset of Toy Jensen and DGX Spark, feel free to skip to the [Training](#training) section. This dataset is a collection of public assets accessible via Google Images.
You will need to prepare a dataset of all the concepts you would like to generate and about 5-10 images for each concept. For this example, we would like to generate images with 2 concepts.