Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
Instructions to use CompVis/stable-diffusion-v1-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CompVis/stable-diffusion-v1-4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", dtype=torch.bfloat16, device_map="cuda") prompt = "A high tech solarpunk utopia in the Amazon rainforest" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
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README.md
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# Stable Diffusion v1-4 Model Card
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## Model Details
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- **Developed by:** Robin Rombach, Patrick Esser
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# Stable Diffusion v1-4 Model Card
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Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
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For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion with D🧨iffusers blog](hf.co/blog/stable_diffusion).
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The **Stable-Diffusion-v1-4** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-3](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-3)
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checkpoint and subsequently fine-tuned on X steps on Y with Z.
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## Model Details
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- **Developed by:** Robin Rombach, Patrick Esser
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