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
Update README.md
#190
by GalaticGod66 - opened
README.md
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@@ -140,7 +140,7 @@ pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
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prompt = "a photo of an astronaut riding a horse on mars"
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prng_seed = jax.random.PRNGKey(0)
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num_inference_steps =
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num_samples = jax.device_count()
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prompt = num_samples * [prompt]
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prompt = "a photo of an astronaut riding a horse on mars"
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prng_seed = jax.random.PRNGKey(0)
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num_inference_steps =
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num_samples = jax.device_count()
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prompt = num_samples * [prompt]
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prompt = "a photo of an astronaut riding a horse on mars"
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prng_seed = jax.random.PRNGKey(0)
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num_inference_steps = 150
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num_samples = jax.device_count()
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prompt = num_samples * [prompt]
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prompt = "a photo of an astronaut riding a horse on mars"
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prng_seed = jax.random.PRNGKey(0)
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num_inference_steps = 150
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num_samples = jax.device_count()
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prompt = num_samples * [prompt]
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