neonforestmist/Clover-Image-Tiny-Inpaint

Clover Image Tiny Inpaint HQ

Clover Image Tiny Inpaint HQ is the high-quality, context-aware inpainting pipeline for Clover. It combines the complete Stable Diffusion 1.5 inpainting U-Net with Clover Image Tiny's tokenizer, text encoder, VAE, and scheduler. This preserves Clover compatibility while replacing the compact inpainting denoiser that frequently produced blurry or unrecognizable masked objects.

The pipeline uses the standard nine-channel inpainting contract:

[noisy latent (4), mask (1), masked-image latent (4)]

Diffusers example

import torch
from diffusers import AutoPipelineForInpainting, DPMSolverMultistepScheduler
from diffusers.utils import load_image

pipe = AutoPipelineForInpainting.from_pretrained(
    "neonforestmist/Clover-Image-Tiny-Inpaint",
    torch_dtype=torch.float16,
).to("cuda")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)

image = pipe(
    prompt="a glossy red enamel kettle resting naturally on the countertop",
    negative_prompt="blurry, distorted, low detail",
    image=load_image("source.png"),
    mask_image=load_image("mask.png"),
    num_inference_steps=20,
    guidance_scale=6.0,
    padding_mask_crop=96,
).images[0]
image.save("clover-inpaint.png")

Recommended interactive defaults are DPM-Solver++, 20 steps, CFG 6.0, and a 96-pixel context crop. Composite the generated result through the exact binary mask when unchanged source pixels must remain byte-for-byte untouched.

Quality gate

The release was evaluated on 24 deterministic, held-out, human-rated InpaintCOCO edits. Every output was also reviewed in three visual contact sheets before release.

Metric	Previous Clover inpaint	HQ release	SD 1.5 inpaint teacher
Masked prompt CLIP similarity (higher)	0.2642	0.2768	0.2820
Masked target MAE (lower)	0.2510	0.2231	0.2156
Changed pixels outside the mask	0	0	0

The HQ release improves prompt alignment by 4.8% and reduces masked target error by 11.1% relative to the previous Clover inpainting release. The visual gate showed recognizable buses, dogs, trains, furniture, signs, and scene-consistent lighting where the compact candidates often collapsed into amorphous fills.

Selection provenance

The release process compared the existing checkpoint, a 30,000-step full-U-Net distillation run, two fused context-LoRA refinements, partial weight blends, the full Stable Diffusion inpainting reference, and this Clover-component hybrid. The 30,000-step and context-LoRA candidates were rejected because they did not beat the existing release across both visual and quantitative gates. The published HQ architecture was the only Clover-compatible candidate that materially improved both prompt alignment and reconstruction.

• Inpainting U-Net revision: stable-diffusion-v1-5/stable-diffusion-inpainting@8a4288a76071f7280aedbdb3253bdb9e9d5d84bb
• Clover components: neonforestmist/Clover-Image-Tiny
• Evaluation dataset: phiyodr/InpaintCOCO@1ffac84be2dfc5ad9afccad868522fad64457435
• Selection platform: Modal H100
• Evaluation seed: 20260813

Core ML and style mixing

The companion iOS resources are published at neonforestmist/Clover-Image-Tiny-Inpaint-CoreML. Its batch-one stateful U-Net supports up to three Clover styles simultaneously with independent strengths. The style tensors remain separate downloads and are composed exactly at runtime; they are not fused into three full 1.6 GB models.

Limitations

Small text, hands, faces, exact logos, and masks below latent resolution can still fail. Output quality depends on the source, mask, prompt, scheduler, guidance, seed, and step count. This release inherits the limitations and license obligations of Clover Image Tiny and Stable Diffusion 1.5 inpainting.

Citation

@software{lozadaperez2026cloverimagetinyinpaint,
  author = {Lukas Lozada Perez},
  title = {Clover Image Tiny Inpaint HQ: Local Context-Aware Image Inpainting},
  year = {2026},
  url = {https://huggingface.co/neonforestmist/Clover-Image-Tiny-Inpaint}
}

Designed and developed independently by Lukas Lozada Perez. Open weights under the CreativeML Open RAIL-M license; complete local inference is supported.