Text-to-Image
Diffusers
TensorBoard
Safetensors
stable-diffusion
stable-diffusion-diffusers
diffusers-training
lora
Instructions to use ACROSS-Lab/PromptTo3D_sd_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ACROSS-Lab/PromptTo3D_sd_finetuned 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-2-1", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ACROSS-Lab/PromptTo3D_sd_finetuned") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
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# LoRA text2image fine-tuning - remi349/sd_trained_3D_lora
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These are LoRA adaption weights are for stabilityai/stable-diffusion-2-1. The weights were fine-tuned on the remi349/finetuning_dataset_for_3D_training dataset.
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## Intended uses & limitations
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# LoRA text2image fine-tuning - remi349/sd_trained_3D_lora
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These are LoRA adaption weights are for stabilityai/stable-diffusion-2-1. The weights were fine-tuned on the remi349/finetuning_dataset_for_3D_training dataset thanks to the library [diffusers](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py).
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## Intended uses & limitations
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