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Creates videos in a stylized physically based rendering animation aesthetic, comparable to the kind seen in major animated films.
Source du modèle
Description de la source
Creates videos in a stylized physically based rendering animation aesthetic, comparable to the kind seen in major animated films.
The primary trigger word for this style is " PBRSty1e ," and there is no strict prompting pattern required; anything in natural language that conveys the concept "the final video is PBRSty1e" should be sufficient.
Out of the box it should support all input types and workflows available to the base model. If you are attempting to convert an existing video, the following boilerplate prompt can be used to do so:
subject_definitions: is the source video containing the video that will be converted to PBRSty1e. is audio for
summary: [reference generation] Convert to PBRSty1e. Keep the exact motion, timing, camera path, scene composition, subject descriptions, audio, and environment from .
Sources
1 sourceArtefacts du modèle
1 artefactMiniMaxH3_Ref2V_PBRStyle_v1.0.safetensors
safetensors · BF16 · 148 MB · SHA-256 2e72edac3412…a4c2 · Civitai
TéléchargerExtraits de sources
1 extraitSource context: 642 downloads · Type LORA · Base model MiniMax H3
retention_analysis: : attribute_transfer for motion, posture sequence, camera, and environment only.
detailed_description: [Shot 1] DESCRIPTION OF SHOT 1 [Shot 2] DESCRIPTION OF SHOT 2
California AB 2013 Training Data Disclosure
This LoRA was fine-tuned using visual data consisting of both clips and still frames, which were AI-generated or extracted from publicly available clips and renderings. The training data includes copyrighted material owned by third parties, including both film studios and individual artists. No training data was licensed or purchased. This LoRA is provided for non-commercial use only under the terms of its distribution.
The dataset consists of approximately 2000 images and 50 video clips collected from publicly accessible sources and generated; dataset assembled in 2025-2026. Data was processed through standard resizing, cropping, normalization, frame extraction, and labeling steps. Synthetic images were included as part of the training dataset.
This model is intended for non-commercial, experimental, and educational use. Generated outputs may reflect copyrighted visual styles or themes associated with the underlying training data. Users are responsible for ensuring compliance with applicable copyright law, other intellectual property laws, and all other applicable laws.
