Text-to-3D
Diffusers
Safetensors
English
StableDiffusionLDM3DPipeline
stable-diffusion
stable-diffusion-diffusers
text-to-image
Eval Results (legacy)
Instructions to use Intel/ldm3d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Intel/ldm3d with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Intel/ldm3d", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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### Limitations and bias
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For the image generation, limitations and bias are the same as the ones from [Stable diffusion](https://huggingface.co/CompVis/stable-diffusion-v1-4#limitations)
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For the depth map generation, limitations and bias are the same as the ones from [DPT](https://huggingface.co/Intel/dpt-large).
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## Training data
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### Limitations and bias
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For the image generation, limitations and bias are the same as the ones from [Stable diffusion](https://huggingface.co/CompVis/stable-diffusion-v1-4#limitations)
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For the depth map generation, a first limitiation is that we are using DPT-large to produce the ground truth, hence, other limitations and bias are the same as the ones from [DPT](https://huggingface.co/Intel/dpt-large).
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## Training data
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