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 ·
a5830b4
1
Parent(s): 9bd2b48
Update pipeline output (#5)
Browse files- Update pipeline output (b98f201ac244617f0e4bf4cfe890dcfc12311787)
Co-authored-by: Pedro Cuenca <pcuenq@users.noreply.huggingface.co>
README.md
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@@ -33,7 +33,7 @@ ldm = DiffusionPipeline.from_pretrained(model_id)
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# run pipeline in inference (sample random noise and denoise)
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prompt = "A painting of a squirrel eating a burger"
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images = ldm([prompt], num_inference_steps=50, eta=0.3, guidance_scale=6)
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# save images
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for idx, image in enumerate(images):
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# run pipeline in inference (sample random noise and denoise)
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prompt = "A painting of a squirrel eating a burger"
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images = ldm([prompt], num_inference_steps=50, eta=0.3, guidance_scale=6).images
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# save images
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for idx, image in enumerate(images):
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