Instructions to use CompVis/ldm-text2im-large-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CompVis/ldm-text2im-large-256 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/ldm-text2im-large-256", 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
- Local Apps
- Draw Things
- DiffusionBee
Commit ·
30de525
1
Parent(s): a5830b4
fix broken arxiv url (#8)
Browse files- fix broken arxiv url (9cfc8a06cc2cefd7b32e6080093b6ace79f73c7b)
Co-authored-by: Kevin Murphy <murphyk@users.noreply.huggingface.co>
README.md
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# High-Resolution Image Synthesis with Latent Diffusion Models (LDM)
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**Paper**: [High-Resolution Image Synthesis with Latent Diffusion Models (LDM)s](https://arxiv.org/abs/2112.
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**Abstract**:
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# High-Resolution Image Synthesis with Latent Diffusion Models (LDM)
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**Paper**: [High-Resolution Image Synthesis with Latent Diffusion Models (LDM)s](https://arxiv.org/abs/2112.10752)
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**Abstract**:
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