Instructions to use stabilityai/stable-cascade with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stabilityai/stable-cascade with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-cascade", 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
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README.md
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<img height="300" src="figures/comparison.png"/>
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According to our evaluation, Stable Cascade performs best in both prompt alignment and aesthetic quality in almost all
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comparisons. The above picture shows the results from a human evaluation using a mix of parti-prompts (link) and
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aesthetic prompts. Specifically,
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## Code Example
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```python
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<img height="300" src="figures/comparison.png"/>
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According to our evaluation, Stable Cascade performs best in both prompt alignment and aesthetic quality in almost all
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comparisons. The above picture shows the results from a human evaluation using a mix of parti-prompts (link) and
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aesthetic prompts. Specifically, Stable Cascade (30 inference steps) was compared against Playground v2 (50 inference
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steps), SDXL (50 inference steps), SDXL Turbo (1 inference step) and Würstchen v2 (30 inference steps).
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## Code Example
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```python
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