Text-to-Image
Diffusers
VersatileDiffusionPipeline
image-to-text
image-to-image
text-to-text
image-editing
image-variation
generation
vision
Instructions to use shi-labs/versatile-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use shi-labs/versatile-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("shi-labs/versatile-diffusion", 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 model_index.json
Browse filesfix: ValueError: text_unet/versatile_diffusion.py as defined in `model_index.json` does not exist in shi-labs/versatile-diffusion and is not a module in 'diffusers/pipelines'
- model_index.json +2 -2
model_index.json
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"CLIPTextModelWithProjection"
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],
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"text_unet": [
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"
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"tokenizer": [
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"transformers",
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"CLIPTextModelWithProjection"
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],
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"text_unet": [
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"diffusers",
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"UNet2DConditionModel"
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],
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"tokenizer": [
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"transformers",
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