Instructions to use YuCollection/sdxl-1.0-refiner-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use YuCollection/sdxl-1.0-refiner-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("YuCollection/sdxl-1.0-refiner-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Upload model_index.json with huggingface_hub
Browse files- model_index.json +35 -0
model_index.json
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{
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"_class_name": "StableDiffusionXLImg2ImgPipeline",
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"_diffusers_version": "0.19.0.dev0",
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"force_zeros_for_empty_prompt": false,
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"add_watermarker": null,
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"requires_aesthetics_score": true,
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"scheduler": [
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"diffusers",
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"EulerDiscreteScheduler"
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],
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"text_encoder": [
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null,
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],
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"text_encoder_2": [
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"transformers",
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"CLIPTextModelWithProjection"
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],
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"tokenizer": [
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null,
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null
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],
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"tokenizer_2": [
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"transformers",
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"CLIPTokenizer"
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],
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"unet": [
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"diffusers",
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"UNet2DConditionModel"
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],
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"vae": [
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"diffusers",
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"AutoencoderKL"
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]
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}
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