Text-to-Image
Diffusers
English
Trained with AutoTrain
stable-diffusion
stable-diffusion-diffusers
lora
template:sd-lora
Instructions to use aztro/sdxl-maba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use aztro/sdxl-maba with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Yntec/DreamPhotoGASM", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("aztro/sdxl-maba") prompt = "tessy" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Update README.md
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README.md
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---
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tags:
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- autotrain
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- stable-diffusion
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- stable-diffusion-diffusers
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- text-to-image
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- diffusers
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- lora
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- template:sd-lora
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base_model: Yntec/DreamPhotoGASM
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instance_prompt: tessy
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license: openrail++
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language:
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- en
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---
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# AutoTrain LoRA DreamBooth - ovieyra21/autotrain-begg7-ozit5
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These are LoRA adaption weights for Yntec/DreamPhotoGASM. The weights were trained on tessy using [DreamBooth](https://dreambooth.github.io/).
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LoRA for the text encoder was enabled: False.
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