Image-to-Image
Diffusers
Safetensors
Core ML
StableDiffusionInpaintPipeline
image-editing
local-ai
clover-image
inpainting
stable-diffusion
Instructions to use neonforestmist/Clover-Image-Tiny-Inpaint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use neonforestmist/Clover-Image-Tiny-Inpaint 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("neonforestmist/Clover-Image-Tiny-Inpaint", 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
Simplify headings and consolidate repeated project links
Browse files
README.md
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@@ -57,12 +57,12 @@ This published example uses the prompt **“add blue sunglasses.”**
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Inpaint HQ uses the **full Stable Diffusion 1.5 inpainting denoiser** with Clover's
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text encoder, tokenizer, VAE, and scheduler. It is a larger model than the compact
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On the release's 24-edit held-out evaluation, HQ improved masked prompt alignment by
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**4.8%** and reduced masked target error by **11.1%** versus the previous Clover inpainting
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release. These are evaluation-set results; individual edits still depend on the image,
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mask, and prompt. [See the
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## Run an edit with Python
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## Styles and Apple devices
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The
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Core ML resources are published [separately](https://huggingface.co/neonforestmist/Clover-Image-Tiny-Inpaint-CoreML).
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Published Clover attention-only LoRAs are mechanically compatible in Diffusers,
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but they were trained for text-to-image, so evaluate each style's editing quality.
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The shipping iOS inpainting screen does not enable dynamic styles.
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## Limits and license
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Review generated edits before sharing. The model inherits the limitations and
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**CreativeML Open RAIL-M** obligations of Clover and Stable Diffusion 1.5 inpainting.
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[**Technical reference, evaluation, provenance, and citation →**](https://huggingface.co/neonforestmist/Clover-Image-Tiny-Inpaint/blob/main/docs/MODEL_DETAILS.md)
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## Evaluation and provenance
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<details>
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Inpaint HQ uses the **full Stable Diffusion 1.5 inpainting denoiser** with Clover's
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text encoder, tokenizer, VAE, and scheduler. It is a larger model than the compact
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regular Clover text-to-image checkpoint. Regular Clover generates from text; Inpaint HQ requires an image and mask.
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On the release's 24-edit held-out evaluation, HQ improved masked prompt alignment by
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**4.8%** and reduced masked target error by **11.1%** versus the previous Clover inpainting
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release. These are evaluation-set results; individual edits still depend on the image,
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mask, and prompt. [See the quality gate and selection record below](#evaluation-and-provenance).
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## Run an edit with Python
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## Styles and Apple devices
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The Clover iOS app linked above provides a native masking workflow.
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Core ML resources are published [separately](https://huggingface.co/neonforestmist/Clover-Image-Tiny-Inpaint-CoreML).
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Published Clover attention-only LoRAs are mechanically compatible in Diffusers,
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but they were trained for text-to-image, so evaluate each style's editing quality.
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The shipping iOS inpainting screen does not enable dynamic styles.
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Adapter and deployment details are included in Evaluation and provenance below.
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## Limits and license
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Review generated edits before sharing. The model inherits the limitations and
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**CreativeML Open RAIL-M** obligations of Clover and Stable Diffusion 1.5 inpainting.
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## Evaluation and provenance
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<details>
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