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--- |
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license: apache-2.0 |
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datasets: |
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- trollek/ImagePromptHelper-v02 |
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- Gustavosta/Stable-Diffusion-Prompts |
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- k-mktr/improved-flux-prompts |
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- Falah/image_generation_prompts_SDXL |
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- ChrisGoringe/flux_prompts |
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language: |
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- en |
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base_model: |
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- HuggingFaceTB/SmolLM2-135M |
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library_name: transformers |
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tags: |
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- llama-factory |
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- full |
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--- |
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# Smol Image Prompt Helper |
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This is meant to be a drop-in replacement for [my last image prompt helper](https://huggingface.co/trollek/ImagePromptHelper-danube3-500M) but with a new trick and a much smaller size. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0077 |
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## Model description |
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Lets say you have a node in ComfyUI to parse JSON and send the appropriate prompt to the text encoders. Tadaaa: |
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``` |
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You are an AI assistant tasked with expanding and formatting image prompts. You are given an input that you will need to write image prompts for different text encoders. |
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Always respond with the following format: |
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{ |
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"clip_l": "<keywords from image analysis>", |
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"clip_g": "<simple descriptions of the image>", |
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"t5xxl": "<complex semanticly rich description of the image>", |
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"negative": "<contrasting keywords for what is not in the image>" |
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} |
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``` |
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## Intended uses & limitations |
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Have a look at the dataset that I created \([ImagePromptHelper-v02](https://huggingface.co/datasets/trollek/ImagePromptHelper-v02) \(CC BY 4.0\)\) and you will see whaaaaat I've doooone. |
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## Training procedure |
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I continued the pretraining with SDXL and Flux prompts and then SFT'd it on my own dataset. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 1 |
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- seed: 443 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.1631 | 0.3966 | 500 | 1.2816 | |
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| 1.019 | 0.7932 | 1000 | 1.1431 | |
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| 0.9857 | 1.1896 | 1500 | 1.0818 | |
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| 1.0436 | 1.5862 | 2000 | 1.0459 | |
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| 0.9918 | 1.9827 | 2500 | 1.0235 | |
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| 0.9287 | 2.3791 | 3000 | 1.0114 | |
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| 0.9205 | 2.7757 | 3500 | 1.0079 | |
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### Framework versions |
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- Transformers 4.50.0 |
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- Pytorch 2.6.0+cu126 |
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- Datasets 3.4.1 |
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- Tokenizers 0.21.0 |
