# Diffusers guide: inpainting (with image-to-image chaining)

- id: src_01M569CCB796RF4AENTFEQ3K9C
- type: source · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=metadata_checked, execution=not_run, review=unreviewed
- categories: T05 · tags: inpainting, img2img, masks, diffusers, beginner, controlnet-inpaint, level:beginner
- updated: 2026-10-18T01:17:53.856Z
- url: https://veyrul.com/sources/src_01M569CCB796RF4AENTFEQ3K9C

Source: https://huggingface.co/docs/diffusers/using-diffusers/inpaint

Diffusers library repo itself is already in the seed catalog (huggingface--diffusers); this record covers the inpaint/img2img teaching content only.

```json
{
  "archived": null,
  "checked_at": "2026-10-17T00:00:00.000Z",
  "parent_url": null,
  "usefulness": "Foundational, runnable-code walkthrough of mask-based inpainting in Diffusers: mask conventions, strength, guidance_scale, padding_mask_crop, mask blur, apply_overlay to keep unmasked pixels, chaining text-to-image -> inpaint -> image-to-image, and ControlNet inpaint conditioning. The sibling img2img guide covers strength and chaining for whole-image edits.",
  "source_type": "documentation",
  "availability": "available",
  "dependencies": [
    "diffusers",
    "torch"
  ],
  "source_owner": "Hugging Face",
  "next_check_at": "2026-10-24T00:00:00.000Z",
  "successor_url": null,
  "hardware_notes": "Guide recommends enable_model_cpu_offload and PyTorch 2 SDPA or xFormers to reduce memory; no VRAM figures given.",
  "known_limitations": [
    "Examples target SD 1.5 / SDXL / Kandinsky 2.2 class models; instruction-edit and flow-transformer models (FLUX, Qwen-Image) have different editing interfaces.",
    "Docs snapshot is versioned (v0.41.0 at check time); parameters may change."
  ],
  "upstream_updated_at": null
}
```

Evidence claims:
- [sourced_fact] White mask pixels are filled from the prompt and black pixels are kept; page documents strength, guidance_scale, negative_prompt and padding_mask_crop parameters. · source: https://huggingface.co/docs/diffusers/using-diffusers/inpaint
- [sourced_fact] Inpaint-specific checkpoints give more natural transitions but are more likely to change unmasked areas; VaeImageProcessor.apply_overlay() can force unmasked pixels to remain unchanged at the cost of less natural transitions. · source: https://huggingface.co/docs/diffusers/using-diffusers/inpaint
- [sourced_fact] Image-to-image guide: strength sets how much noise is added (1.0 means the initial image is more or less ignored) and relates to num_inference_steps; chaining pipelines with output_type=latent only works when pipelines share a VAE. · source: https://huggingface.co/docs/diffusers/using-diffusers/img2img
- [inference] The pages render docs for diffusers v0.41.0 at the check date; examples use SD 1.5, SDXL and Kandinsky 2.2, not newer DiT/flow models. · source: https://huggingface.co/docs/diffusers/using-diffusers/inpaint
- [upstream_claim] Research review verdict at export: reviewed (as of 2026-10-17). Recorded by the research pipeline, not a hub review outcome; evidence.review stays unreviewed until an independent reviewer records one.

Rights:
- documentation: license=unknown hosting=unknown redistribution=unknown modification=unknown training=unknown · Docs-specific terms not checked. The huggingface/diffusers repository LICENSE is Apache-2.0 (fetched https://github.com/huggingface/diffusers/blob/main/LICENSE) but that was not confirmed to cover the rendered documentation site. · evidence: https://huggingface.co/docs/diffusers/index
- code: license=unknown hosting=unknown redistribution=unknown modification=unknown training=unknown · Example code snippets reference third-party checkpoints (SD 1.5, SDXL, Kandinsky, community style models) with their own licenses; not evaluated here. · evidence: https://huggingface.co/docs/diffusers/using-diffusers/inpaint

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