Instructions to use faaj888/lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use faaj888/lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("undefined,lkzd7/WAN2.2_LoraSet_NSFW", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("faaj888/lora") prompt = "TOT" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png") image = pipe(image=input_image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
Update README.md
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README.md
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- lora
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- diffusers
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- fal
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base_model:
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instance_prompt: TOT
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license: other
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---
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# lora
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## Training at fal.ai
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Training was done using [fal.ai/models/fal-ai/turbo-flux-trainer](https://fal.ai/models/fal-ai/turbo-flux-trainer).
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- lora
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- diffusers
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- fal
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base_model:
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- undefined
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- lkzd7/WAN2.2_LoraSet_NSFW
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instance_prompt: TOT
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license: other
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datasets:
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- mess2735/wan2.2_lora
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language:
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- en
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metrics:
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- character
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new_version: lkzd7/WAN2.2_LoraSet_NSFW
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pipeline_tag: image-to-video
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library_name: diffusers
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---
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# lora
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## Training at fal.ai
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Training was done using [fal.ai/models/fal-ai/turbo-flux-trainer](https://fal.ai/models/fal-ai/turbo-flux-trainer).
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