Add validated inpainting example and stable sampler guidance

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/result-cat.png filter=lfs diff=lfs merge=lfs -text
+examples/source-greenhouse.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -30,18 +30,20 @@ the VAE encoder needed to prepare the masked-image latent on device.
 ## Diffusers example
 
 ```python
-from diffusers import AutoPipelineForInpainting
+from diffusers import AutoPipelineForInpainting, DPMSolverMultistepScheduler
 from diffusers.utils import load_image
 
 pipe = AutoPipelineForInpainting.from_pretrained(
     "neonforestmist/Clover-Image-Tiny-Inpaint",
     torch_dtype="auto",
 )
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
 image = pipe(
     prompt="a tiny glass greenhouse glowing in a moonlit garden",
     image=load_image("input.png"),
     mask_image=load_image("mask.png"),
-    num_inference_steps=30,
+    num_inference_steps=20,
+    padding_mask_crop=64,
 ).images[0]
 image.save("clover-inpaint.png")
 ```
@@ -53,6 +55,21 @@ The mask is a grayscale image: white means “regenerate” and black means
 - `remove the person from the masked area and continue the background naturally`
 - `add a red enamel kettle on the masked countertop`
 
+## Inpainting example
+
+The example below uses DPM-Solver++, 20 steps, CFG 7.5, and seed 1337. The
+prompt is `a realistic orange cat sitting in the doorway, detailed photography`.
+Only the white doorway mask is replaced.
+
+| Source | White mask | Result |
+|:---:|:---:|:---:|
+| ![Greenhouse source](examples/source-greenhouse.png) | ![White doorway mask](examples/mask-doorway.png) | ![Cat inpainted into the doorway](examples/result-cat.png) |
+
+For small masks, `padding_mask_crop=64` gives the edit more useful latent
+resolution before Diffusers maps it back into the source image. DPM-Solver++
+is the recommended scheduler for this release; PNDM can become unstable at
+higher step counts. Start at 20 steps and keep interactive runs at or below 50.
+
 For repeatable experiments, keep the input image, mask, seed, scheduler, and
 step count together. Inpainting is local editing: the unmasked region is
 provided as the masked-image conditioning and is also preserved by the native
@@ -73,6 +90,14 @@ Conversion and the native iOS integration live in the source Clover repo:
 - [`Clover-iOS`](https://huggingface.co/neonforestmist/Clover-Image-Tiny/tree/main/Clover-iOS)
 - [`training/README-INPAINTING.md`](https://huggingface.co/neonforestmist/Clover-Image-Tiny/blob/main/training/README-INPAINTING.md)
 
+## LoRA compatibility
+
+Diffusers exposes LoRA loading for inpainting pipelines, but an adapter must be
+trained against this model's 9-channel U-Net. Existing Clover Image Tiny style
+LoRAs target the Regular 4-channel U-Net and are not interchangeable. The
+current Core ML export does not expose dynamic LoRA state; an inpainting-specific
+adapter can instead be fused and converted as a separate bundle.
+
 ## Training provenance
 
 Training uses synthetic rectangle, ellipse, and brush masks over the pinned