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--- |
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license: creativeml-openrail-m |
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library_name: diffusers |
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tags: |
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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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- diffusers-training |
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- lora |
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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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- diffusers-training |
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- lora |
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base_model: stabilityai/stable-diffusion-2-1 |
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inference: true |
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--- |
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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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This model aims at generating images of isolated objects, compatible with 2D_to_3D models like [Triposr](https://github.com/VAST-AI-Research/TripoSR) or [CRM](https://huggingface.co/Zhengyi/CRM). |
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It was finetuned in order to create after a pipeline of prompt-to-3D model. |
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#### How to use |
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```python |
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# First load the basic architecture and everything |
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import torch |
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from diffusers import StableDiffusionPipeline |
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pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16) |
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# Then add the lora weights to the model stable diffusion 2 |
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pipe.unet.load_attn_procs('ACROSS-Lab/PromptTo3D_sd_finetuned') |
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pipe.to("cuda") |
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# Then you can begin the inference process on a prompt and save the image generated |
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prompt = 'a rabbit with a yellow jacket' |
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image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0] |
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image.save("my_image.png") |
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``` |
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#### Limitations and bias |
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This model is a first try some hyperparameters tuning should be done, but for that we would need a solid automated benchmark. |
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## Training details |
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The model finetuned model is [Stable Diffusion 2](https://huggingface.co/stabilityai/stable-diffusion-2). |
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The data used to train this model is the dataset available on uggingface at 'remi349/finetuning_dataset_for_3D_training'. |
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you can download it thanks to the command |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("ACROSS-Lab/PromptTo3D_sd_dataset", split = 'train') |
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``` |
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This dataset is a subset of the dataset [Objaverse](https://objaverse.allenai.org/). |
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## Collaboration |
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This model and dataset has been made in collaboration by [Josué ADOSSEHOUN](https://huggingface.co/josh007) and [Rémi DUCOTTET](https://huggingface.co/remi349) |
