Text-to-Image
Diffusers
diffusers-training
lora
template:sd-lora
stable-diffusion-xl
stable-diffusion-xl-diffusers
Instructions to use hbiny/text2img with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hbiny/text2img with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("hbiny/text2img") prompt = "d0nar1dk, salute" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
End of training
Browse files- .gitattributes +4 -0
- README.md +74 -0
- checkpoint-150/optimizer.bin +3 -0
- checkpoint-150/pytorch_lora_weights.safetensors +3 -0
- checkpoint-150/random_states_0.pkl +3 -0
- checkpoint-150/scheduler.bin +3 -0
- checkpoint-300/optimizer.bin +3 -0
- checkpoint-300/pytorch_lora_weights.safetensors +3 -0
- checkpoint-300/random_states_0.pkl +3 -0
- checkpoint-300/scheduler.bin +3 -0
- checkpoint-450/optimizer.bin +3 -0
- checkpoint-450/pytorch_lora_weights.safetensors +3 -0
- checkpoint-450/random_states_0.pkl +3 -0
- checkpoint-450/scheduler.bin +3 -0
- checkpoint-600/optimizer.bin +3 -0
- checkpoint-600/pytorch_lora_weights.safetensors +3 -0
- checkpoint-600/random_states_0.pkl +3 -0
- checkpoint-600/scheduler.bin +3 -0
- checkpoint-750/optimizer.bin +3 -0
- checkpoint-750/pytorch_lora_weights.safetensors +3 -0
- checkpoint-750/random_states_0.pkl +3 -0
- checkpoint-750/scheduler.bin +3 -0
- checkpoint-900/optimizer.bin +3 -0
- checkpoint-900/pytorch_lora_weights.safetensors +3 -0
- checkpoint-900/random_states_0.pkl +3 -0
- checkpoint-900/scheduler.bin +3 -0
- image_0.png +3 -0
- image_1.png +3 -0
- image_2.png +3 -0
- image_3.png +3 -0
- pytorch_lora_weights.safetensors +3 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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image_0.png filter=lfs diff=lfs merge=lfs -text
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image_1.png filter=lfs diff=lfs merge=lfs -text
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image_2.png filter=lfs diff=lfs merge=lfs -text
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image_3.png filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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base_model: stabilityai/stable-diffusion-xl-base-1.0
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library_name: diffusers
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license: openrail++
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instance_prompt: d0nar1dk, blue cloth, red ribbon, yellow feet
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widget:
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- text: d0nar1dk, salute
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output:
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url: image_0.png
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- text: d0nar1dk, salute
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output:
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url: image_1.png
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- text: d0nar1dk, salute
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output:
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url: image_2.png
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- text: d0nar1dk, salute
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output:
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url: image_3.png
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tags:
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- text-to-image
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- text-to-image
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- diffusers-training
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- diffusers
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- lora
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- template:sd-lora
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- stable-diffusion-xl
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- stable-diffusion-xl-diffusers
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---
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<!-- This model card has been generated automatically according to the information the training script had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# SDXL LoRA DreamBooth - hbiny/text2img
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<Gallery />
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## Model description
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These are hbiny/text2img LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
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The weights were trained using [DreamBooth](https://dreambooth.github.io/).
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LoRA for the text encoder was enabled: False.
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Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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## Trigger words
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You should use d0nar1dk, blue cloth, red ribbon, yellow feet to trigger the image generation.
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## Download model
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Weights for this model are available in Safetensors format.
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[Download](hbiny/text2img/tree/main) them in the Files & versions tab.
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## Intended uses & limitations
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#### How to use
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```python
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# TODO: add an example code snippet for running this diffusion pipeline
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```
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#### Limitations and bias
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[TODO: provide examples of latent issues and potential remediations]
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## Training details
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[TODO: describe the data used to train the model]
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