Instructions to use Andyrasika/avatar_diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Andyrasika/avatar_diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Andyrasika/avatar_diffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Transformers
How to use Andyrasika/avatar_diffusion with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Andyrasika/avatar_diffusion", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Commit ·
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Parent(s): f504abb
Update README.md
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README.md
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Dreambooth style: Avatar
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Dreambooth finetuning of Stable Diffusion (v1.5.1) on Avatar art style
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About
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This text-to-image stable diffusion model was trained with dreambooth.
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pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
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pipeline.to("cuda")
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pipeline.enable_vae_tiling()
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pipeline.enable_xformers_memory_efficient_attention()
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---
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Dreambooth style: Avatar
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Dreambooth finetuning of Stable Diffusion (v1.5.1) on Avatar art style.
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About
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This text-to-image stable diffusion model was trained with dreambooth.
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)
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pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
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pipeline.to("cuda")
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pipeline.enable_vae_tiling()
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pipeline.enable_xformers_memory_efficient_attention()
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