Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
name: string
style: int64
content: int64
base_model: string
type: string
url: string
trigger_word: string
trigger_from_websit: bool
version: struct<id: int64, url: string, name: string, files: list<item: struct<id: int64, url: string, name: (... 304 chars omitted)
child 0, id: int64
child 1, url: string
child 2, name: string
child 3, files: list<item: struct<id: int64, url: string, name: string, type: string, sizeKB: double, hashes: list<i (... 42 chars omitted)
child 0, item: struct<id: int64, url: string, name: string, type: string, sizeKB: double, hashes: list<item: struct (... 30 chars omitted)
child 0, id: int64
child 1, url: string
child 2, name: string
child 3, type: string
child 4, sizeKB: double
child 5, hashes: list<item: struct<type: string, hash: string>>
child 0, item: struct<type: string, hash: string>
child 0, type: string
child 1, hash: string
child 4, trigger_words: list<item: string>
child 0, item: string
child 5, type: string
child 6, stats: struct<downloads: string, creations: string, buzz_earned: string>
child 0, downloads: string
child 1, creations: string
child 2, buzz_earned: string
child 7, reviews: string
child 8, published: string
child 9, base_model: string
child 10, usage_tips: list<item: null>
child 0, item: null
id: int64
images: list<item: struct<resource_used: list<item: struct<imageId: int64, modelVersionId: int64, strength: (... 280 chars omitted)
child 0, item: struct<resource_used: list<item: struct<imageId: int64, modelVersionId: int64, strength: null, model (... 268 chars omitted)
child 0, resource_used: list<item: struct<imageId: int64, modelVersionId: int64, strength: null, modelId: int64, modelName: (... 98 chars omitted)
child 0, item: struct<imageId: int64, modelVersionId: int64, strength: null, modelId: int64, modelName: string, mod (... 86 chars omitted)
child 0, imageId: int64
child 1, modelVersionId: int64
child 2, strength: null
child 3, modelId: int64
child 4, modelName: string
child 5, modelType: string
child 6, versionId: int64
child 7, versionName: string
child 8, baseModel: string
child 9, url: string
child 1, prompt: string
child 2, Other metadata: struct<cfgScale: double, steps: int64, sampler: string, seed: int64>
child 0, cfgScale: double
child 1, steps: int64
child 2, sampler: string
child 3, seed: int64
child 3, id: int64
child 4, url: string
child 5, storage_url: string
description: string
to
{'id': Value('int64'), 'name': Value('string'), 'url': Value('string'), 'images': List({'resource_used': List({'imageId': Value('int64'), 'modelVersionId': Value('int64'), 'strength': Value('null'), 'modelId': Value('int64'), 'modelName': Value('string'), 'modelType': Value('string'), 'versionId': Value('int64'), 'versionName': Value('string'), 'baseModel': Value('string'), 'url': Value('string')}), 'prompt': Value('string'), 'Other metadata': {'cfgScale': Value('float64'), 'steps': Value('int64'), 'sampler': Value('string'), 'seed': Value('int64')}, 'id': Value('int64'), 'url': Value('string'), 'storage_url': Value('string')}), 'version': {'id': Value('int64'), 'url': Value('string'), 'name': Value('string'), 'files': List({'id': Value('int64'), 'url': Value('string'), 'name': Value('string'), 'type': Value('string'), 'sizeKB': Value('float64'), 'hashes': List({'type': Value('string'), 'hash': Value('string')})}), 'trigger_words': List(Value('string')), 'type': Value('string'), 'stats': {'downloads': Value('string'), 'creations': Value('string'), 'buzz_earned': Value('string')}, 'reviews': Value('string'), 'published': Value('string'), 'base_model': Value('string'), 'usage_tips': List(Value('null'))}, 'description': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 310, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
name: string
style: int64
content: int64
base_model: string
type: string
url: string
trigger_word: string
trigger_from_websit: bool
version: struct<id: int64, url: string, name: string, files: list<item: struct<id: int64, url: string, name: (... 304 chars omitted)
child 0, id: int64
child 1, url: string
child 2, name: string
child 3, files: list<item: struct<id: int64, url: string, name: string, type: string, sizeKB: double, hashes: list<i (... 42 chars omitted)
child 0, item: struct<id: int64, url: string, name: string, type: string, sizeKB: double, hashes: list<item: struct (... 30 chars omitted)
child 0, id: int64
child 1, url: string
child 2, name: string
child 3, type: string
child 4, sizeKB: double
child 5, hashes: list<item: struct<type: string, hash: string>>
child 0, item: struct<type: string, hash: string>
child 0, type: string
child 1, hash: string
child 4, trigger_words: list<item: string>
child 0, item: string
child 5, type: string
child 6, stats: struct<downloads: string, creations: string, buzz_earned: string>
child 0, downloads: string
child 1, creations: string
child 2, buzz_earned: string
child 7, reviews: string
child 8, published: string
child 9, base_model: string
child 10, usage_tips: list<item: null>
child 0, item: null
id: int64
images: list<item: struct<resource_used: list<item: struct<imageId: int64, modelVersionId: int64, strength: (... 280 chars omitted)
child 0, item: struct<resource_used: list<item: struct<imageId: int64, modelVersionId: int64, strength: null, model (... 268 chars omitted)
child 0, resource_used: list<item: struct<imageId: int64, modelVersionId: int64, strength: null, modelId: int64, modelName: (... 98 chars omitted)
child 0, item: struct<imageId: int64, modelVersionId: int64, strength: null, modelId: int64, modelName: string, mod (... 86 chars omitted)
child 0, imageId: int64
child 1, modelVersionId: int64
child 2, strength: null
child 3, modelId: int64
child 4, modelName: string
child 5, modelType: string
child 6, versionId: int64
child 7, versionName: string
child 8, baseModel: string
child 9, url: string
child 1, prompt: string
child 2, Other metadata: struct<cfgScale: double, steps: int64, sampler: string, seed: int64>
child 0, cfgScale: double
child 1, steps: int64
child 2, sampler: string
child 3, seed: int64
child 3, id: int64
child 4, url: string
child 5, storage_url: string
description: string
to
{'id': Value('int64'), 'name': Value('string'), 'url': Value('string'), 'images': List({'resource_used': List({'imageId': Value('int64'), 'modelVersionId': Value('int64'), 'strength': Value('null'), 'modelId': Value('int64'), 'modelName': Value('string'), 'modelType': Value('string'), 'versionId': Value('int64'), 'versionName': Value('string'), 'baseModel': Value('string'), 'url': Value('string')}), 'prompt': Value('string'), 'Other metadata': {'cfgScale': Value('float64'), 'steps': Value('int64'), 'sampler': Value('string'), 'seed': Value('int64')}, 'id': Value('int64'), 'url': Value('string'), 'storage_url': Value('string')}), 'version': {'id': Value('int64'), 'url': Value('string'), 'name': Value('string'), 'files': List({'id': Value('int64'), 'url': Value('string'), 'name': Value('string'), 'type': Value('string'), 'sizeKB': Value('float64'), 'hashes': List({'type': Value('string'), 'hash': Value('string')})}), 'trigger_words': List(Value('string')), 'type': Value('string'), 'stats': {'downloads': Value('string'), 'creations': Value('string'), 'buzz_earned': Value('string')}, 'reviews': Value('string'), 'published': Value('string'), 'base_model': Value('string'), 'usage_tips': List(Value('null'))}, 'description': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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Check out the documentation for more information.
Free Style LoRA Meta
This dataset contains metadata and demo images for LoRA (Low-Rank Adaptation) models evaluated across three base model architectures. It serves as a reference for understanding LoRA training quality, visual style/content characteristics, and evaluation configurations.
Data Structure
free_style_lora_meta/
├── flux/ # FLUX-based LoRA evaluations
│ └── {lora_id}/
│ ├── {lora_id}.json # Main metadata (training config, trigger words, model info)
│ ├── {lora_id}_img{1-6}.json # Per-image generation parameters
│ └── demo_images/
│ └── {lora_id}_img{1-6}.jpeg # Generated demo images
├── illustrious/ # Illustrious-based LoRA evaluations
│ └── {lora_id}/
│ ├── {lora_id}.json
│ ├── {lora_id}_img{1-6}.json
│ └── demo_images/
│ └── {lora_id}_img{1-6}.jpeg
└── qwen/ # Qwen-based LoRA evaluations
└── {lora_id}/
├── {lora_id}.json
├── {lora_id}_img{1-6}.json
└── demo_images/
└── {lora_id}_img{1-6}.jpeg
Distribution
| Base Model | Category | Count | Description |
|---|---|---|---|
| FLUX | Content LoRA | 91 | LoRAs trained for specific content/subject reproduction |
| FLUX | Style LoRA | 1460 | LoRAs trained for artistic style transfer |
| Illustrious | Content LoRA | 799 | Content-focused LoRAs on Illustrious (anime/illustration model) |
| Illustrious | Style LoRA | 191 | Style-focused LoRAs on Illustrious |
| Qwen | Content LoRA | 19 | Content LoRAs on Qwen-based architecture |
| Qwen | Style LoRA | 53 | Style LoRAs on Qwen-based architecture |
| Total | 2613 |
File Descriptions
{lora_id}.json (Main Metadata)
Contains the primary information about each LoRA model:
- Model source and download URL
- Training trigger words / activation tokens
- Base model version and architecture
- LoRA rank, training steps, and hyperparameters
- Associated tags and categories
{lora_id}_img{N}.json (Per-Image Parameters)
Generation parameters used to produce each demo image:
- Prompt and negative prompt
- Sampling method, steps, CFG scale
- Seed and resolution
demo_images/ (Visual Demos)
Generated sample images (JPEG) that demonstrate the LoRA's effect. Typically 6 images per LoRA, showing the model's capability across different prompts.
Base Model Descriptions
- FLUX: High-quality text-to-image diffusion model known for prompt adherence and photorealistic output.
- Illustrious: A community-driven anime/illustration-focused model, excelling at stylized 2D artwork.
- Qwen: Qwen-based multimodal architecture adapted for image generation with instruction-following capabilities.
Use Cases
- LoRA Quality Evaluation: Compare generation quality across different LoRAs and base models.
- Style/Content Classification: Use metadata and demo images to build style or content classifiers.
- Triplet-based Similarity Judgment: This data supports triplet evaluation pipelines for measuring style/content similarity between LoRA outputs.
- Training Recipe Analysis: Study how different training configurations affect output quality.
License
This dataset is provided for research and evaluation purposes.
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