Text-to-Image
Diffusers
Safetensors
StableDiffusionXLPipeline
stable-diffusion
sdxl
flash
sdxl-flash
lightning
turbo
lcm
hyper
fast
fast-sdxl
sd-community
Instructions to use sd-community/sdxl-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sd-community/sdxl-flash with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("sd-community/sdxl-flash", 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
Update README.md
Browse files
README.md
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@@ -53,7 +53,7 @@ import torch
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from diffusers import StableDiffusionXLPipeline, DPMSolverSinglestepScheduler
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# Load model.
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pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash", torch_dtype=torch.float16
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# Ensure sampler uses "trailing" timesteps.
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pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
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from diffusers import StableDiffusionXLPipeline, DPMSolverSinglestepScheduler
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# Load model.
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pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash", torch_dtype=torch.float16).to("cuda")
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# Ensure sampler uses "trailing" timesteps.
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pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
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