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
remove text_unet
#9
by nirajan111 - opened
- model_index.json +0 -4
model_index.json
CHANGED
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@@ -21,10 +21,6 @@
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"transformers",
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"CLIPTextModelWithProjection"
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],
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"text_unet": [
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"versatile_diffusion",
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"UNetFlatConditionModel"
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],
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"tokenizer": [
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"transformers",
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"CLIPTokenizer"
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"transformers",
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"CLIPTextModelWithProjection"
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],
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"tokenizer": [
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"transformers",
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"CLIPTokenizer"
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