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@ -217,7 +217,7 @@ DATA_FILE_PATH = 'datasets/stable_diffusion_prompts.csv'
OUTPUT_DIR = './trained'
```
*Note: You may also prepare your own dataset, for example, with Positive: and Negative: and even Sampler etc lines interleaving in csv.*
*Note: You may also prepare your own dataset, for example, with Prompt: and Negative: and even Sampler etc lines interleaving in csv.*
Then run the training, then after a long-long time, use something like this as a prompt for LLaMA to generate SD prompts:
@ -225,10 +225,10 @@ Then run the training, then after a long-long time, use something like this as a
batch = tokenizer("A portrait of a beautiful girl, ", return_tensors="pt")
```
*Note: If you have prepared and used own dataset with Positive: Negative: lines, the initial LLaMA prompt may look like:*
*Note: If you have prepared and used own dataset with Prompt: Negative prompt: lines, the initial LLaMA prompt may look like:*
```
batch = tokenizer("Positive: A warship flying thru the Wormhole, ", return_tensors="pt")
batch = tokenizer("Prompt: A warship flying thru the Wormhole, ", return_tensors="pt")
```
Run inference, this should return continued prompt for SD.

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