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prithivMLmods/PhotoCleanser-i2i

sourceHugging Faceotherupdated 1y agoView on Hugging Face

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Model Card

PhotoCleanser-i2i [Image-to-Image] (experimental)

PhotoCleanser-i2i is an adapter for black-forest-lab's FLUX.1-Kontext-dev. It is an experimental LoRA designed for removing specified object(s) while preserving the remaining content in the image. The model was trained on 36 image pairs (18 start images, 18 end images). Synthetic result nodes were generated using NanoBanana from Google and labeled with DeepCaption-VLA-7B. The adapter is triggered with the following prompt:

[!note]

[photo content], remove the specified object(s) from the image while preserving the background and remaining elements, maintaining realism and original details.

Some use cases

[photo content], remove all humans from the image while preserving the background and remaining elements, maintaining realism and original details.

[photo content], remove the ball from the image while preserving the background and remaining elements, maintaining realism and original details.

[photo content], remove the cat from the image while preserving the background and remaining elements, maintaining realism and original details.

Sample Inference Comparing the Base Model with the Adapter

Note: In over 25 inferences across various scale settings, the base model struggles to properly reconstruct the basketball net after the ball is removed.

FLUX.1-Kontext-dev	PhotoCleanser-i2i

No desired effect was achieved with the base model after testing many settings, but PhotoCleanser-i2i performed its best to remove the human character from the image.

FLUX.1-Kontext-dev	PhotoCleanser-i2i

No desired effect was achieved with the base model after testing many settings, but PhotoCleanser-i2i performed its best to remove the human/animal character from the image.

FLUX.1-Kontext-dev	PhotoCleanser-i2i

Parameter Settings

Setting	Value
Module Type	Adapter
Base Model	FLUX.1 Kontext Dev - fp8
Trigger Words	[photo content], remove the specified object(s) from the image while preserving the background and remaining elements, maintaining realism and original details.
Image Processing Repeats	50
Epochs	32
Save Every N Epochs	1

Labeling: DeepCaption-VLA-7B(natural language & English)

Total Images Used for Training : 36 Image Pairs (18 Start, 18 End)

Synthetic Result Node generated by NanoBanana from Google (Image Result Sets Dataset)

Training Parameters

Setting	Value
Seed	-
Clip Skip	-
Text Encoder LR	0.00001
UNet LR	0.00005
LR Scheduler	constant
Optimizer	AdamW8bit
Network Dimension	64
Network Alpha	32
Gradient Accumulation Steps	-

Label Parameters

Setting	Value
Shuffle Caption	-
Keep N Tokens	-

Advanced Parameters

Setting	Value
Noise Offset	0.03
Multires Noise Discount	0.1
Multires Noise Iterations	10
Conv Dimension	-
Conv Alpha	-
Batch Size	-
Steps	4370 (Low(700))
Sampler	euler

Trigger words

You should use [photo content] to trigger the image generation.

You should use remove the specified object(s) from the image while preserving the background and remaining elements to trigger the image generation.

You should use maintaining realism and original details. to trigger the image generation.

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