Instructions to use jcplus/waifu-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jcplus/waifu-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("jcplus/waifu-diffusion", 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 Settings
- Draw Things
- DiffusionBee
Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`.
#3
by patrickvonplaten - opened
- model_index.json +5 -5
model_index.json
CHANGED
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@@ -3,20 +3,20 @@
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"_diffusers_version": "0.2.4",
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"feature_extractor": [
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"transformers",
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"
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],
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"safety_checker": [
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"stable_diffusion",
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"StableDiffusionSafetyChecker"
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],
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-
"text_encoder": [
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"transformers",
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"CLIPTextModel"
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-
],
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"scheduler": [
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"diffusers",
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"DDIMScheduler"
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],
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"tokenizer": [
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"transformers",
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"CLIPTokenizer"
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"_diffusers_version": "0.2.4",
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"feature_extractor": [
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"transformers",
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+
"CLIPImageProcessor"
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],
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"safety_checker": [
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"stable_diffusion",
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"StableDiffusionSafetyChecker"
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],
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"scheduler": [
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"diffusers",
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"DDIMScheduler"
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],
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+
"text_encoder": [
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"transformers",
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"CLIPTextModel"
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],
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"tokenizer": [
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"transformers",
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"CLIPTokenizer"
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