sarman/dgx-spark-playbooks
1
0
Fork 0
mirror of https://github.com/NVIDIA/dgx-spark-playbooks.git synced 2026-04-23 10:33:51 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

chore: Regenerate all playbooks

Browse Source
This commit is contained in:
GitLab CI 2025-10-07 18:19:27 +00:00
parent 4ba49c5342
commit a4e233f6b6
2 changed files with 5 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
nvidia/flux-finetuning/README.md
View File

@ -110,7 +110,7 @@ Find the workflow section on the left-side panel of ComfyUI (or press `w`). Upon
Provide your prompt in the `CLIP Text Encode (Prompt)` block. For example, we will use `Toy Jensen holding a DGX Spark in a datacenter`. You can expect the generation to take ~3 mins since it is compute intesive to create high-resolution 1024px images. Provide your prompt in the `CLIP Text Encode (Prompt)` block. For example, we will use `Toy Jensen holding a DGX Spark in a datacenter`. You can expect the generation to take ~3 mins since it is compute intesive to create high-resolution 1024px images.
After playing around with the base model, you have 2 possible next steps. After playing around with the base model, you have 2 possible next steps.
* If you already have fine-tuned LoRAs placed inside `models/loras/`, please skip to `Step 7. Fine-tuned Model Inference` section. * If you already have fine-tuned LoRAs placed inside `models/loras/`, please skip to `Step 7. Fine-tuned model inference` section.
* If you wish to train a LoRA for your custom concepts, first make sure that the ComfyUI inference container is brought down before proceeding to train. You can bring it down by interrupting the terminal with `Ctrl+C` keystroke. * If you wish to train a LoRA for your custom concepts, first make sure that the ComfyUI inference container is brought down before proceeding to train. You can bring it down by interrupting the terminal with `Ctrl+C` keystroke.
> **Note**: To clear out any extra occupied memory from your system, execute the following command outside the container after interrupting the ComfyUI server. > **Note**: To clear out any extra occupied memory from your system, execute the following command outside the container after interrupting the ComfyUI server.
@ -120,7 +120,7 @@ sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'
## Step 5. Dataset preparation ## Step 5. Dataset preparation
Let's prepare our dataset to perform Dreambooth LoRA finetuning 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. 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. 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.
@ -136,7 +136,7 @@ You will need to prepare a dataset of all the concepts you would like to generat
Create a folder for each concept with its corresponding name and place it inside the `flux_data` directory. In our case, we have used `sparkgpu` and `tjtoy` as our concepts, and placed a few images inside each of them. Create a folder for each concept with its corresponding name and place it inside the `flux_data` directory. In our case, we have used `sparkgpu` and `tjtoy` as our concepts, and placed a few images inside each of them.
Now, let's modify the `flux_data/data.toml` file to reflect the concepts chosen. Ensure that you update/create entries for each of your concept by modifying the `image_dir` and `class_tokens` fields under `[[datasets.subsets]]`. For better performance in finetuning, it is good practice to append a class token to your concept name (like `toy` or `gpu`). Now, let's modify the `flux_data/data.toml` file to reflect the concepts chosen. Ensure that you update/create entries for each of your concept by modifying the `image_dir` and `class_tokens` fields under `[[datasets.subsets]]`. For better performance in fine-tuning, it is good practice to append a class token to your concept name (like `toy` or `gpu`).
## Step 6. Training ## Step 6. Training
@ -165,6 +165,4 @@ Find the workflow section on the left-side panel of ComfyUI (or press `w`). Upon
Provide your prompt in the `CLIP Text Encode (Prompt)` block. Now let's incorporate our custom concepts into our prompt for the fine-tuned model. For example, we will use `tjtoy toy holding sparkgpu gpu in a datacenter`. You can expect the generation to take ~3 mins since it is compute intesive to create high-resolution 1024px images. Provide your prompt in the `CLIP Text Encode (Prompt)` block. Now let's incorporate our custom concepts into our prompt for the fine-tuned model. For example, we will use `tjtoy toy holding sparkgpu gpu in a datacenter`. You can expect the generation to take ~3 mins since it is compute intesive to create high-resolution 1024px images.
For the provided prompt and random seed, the fine-tuned Flux model generated the following image. Unlike the base model, we can see that the fine-tuned model can generate multiple concepts in a single image. Additionally, ComfyUI exposes several fields to tune and change the look and feel of the generated images.
Unlike the base model, we can see that the fine-tuned model can generate multiple concepts in a single image.
Additionally, ComfyUI exposes several fields to tune and change the look and feel of the generated images.

2
nvidia/vlm-finetuning/README.md
View File

@ -183,7 +183,7 @@ hf download OpenGVLab/InternVL3-8B
#### 6.3. Base model inference #### 6.3. Base model inference
Before going ahead to finetune our video VLM for this task, let's see how the base InternVL3-8B does. Before going ahead to fine-tune our video VLM for this task, let's see how the base InternVL3-8B does.
```bash ```bash
## cd into vlm_finetuning/assets/ui_video if you haven't already ## cd into vlm_finetuning/assets/ui_video if you haven't already