Instructions to use Avdpro/SoL-Refiner-LTX-2.3-MLX-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Avdpro/SoL-Refiner-LTX-2.3-MLX-BF16 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Avdpro/SoL-Refiner-LTX-2.3-MLX-BF16", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
SoL-Refiner LTX-2.3 MLX BF16
AI2Apps video upscaling checkpoint, derived from NVIDIA SoL-Refiner / Lightricks LTX-2.3. Original: Efficient-Large-Model/SoL-Refiner-LTX-2.3-One-Step at c69c2a543997fe12c1ae24c776df6188e5d2248a.
Modified: unused audio and vision tensors removed; remaining tensor payloads preserve original BF16 bytes. No quantization. Safetensors shards are repacked and are intended for the AI2Apps MLX implementation, not generic Diffusers loading.
Standard upscaling needs transformer/, vae/, latent_upsampler/, scheduler/, model_index.json, default-prompt-context.safetensors, context-metadata.json and META/. It uses the fixed prompt recorded in context-metadata.json (about 28.71 GB). Custom prompts additionally need text_encoder/, connectors/ and tokenizer/ (about 28.33 GB extra). All files together are about 57.04 GB. Existing video files can be reused by checksum.
9/33-frame 128-to-256 tests on one portrait video, with empty and default prompts, produced exactly equal decoded tensors to the original checkpoint. Broader quality and installation validation are separate.
See META/CHECKPOINT-TERMS.txt and META/NOTICE.md for original license terms and required attribution. Users must review applicable terms before downloading through AI2Apps.
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