Another amateur photo LoRA aimed at making images feel more realistic and closer to everyday smartphone or social media photos. Expect sharper details, brighter colors, and a more casual, natural look overall.<hr />Krea V3:Similar to V2 but the captions used a tag-style instead of full sentences.Krea V2:<span style="background-color:rgb(26, 27, 30);color:rgb(193, 194, 197);font-family:-apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, Roboto, Helvetica, Arial, sans-serif, &quot;Apple Color Emoji&quot;, &quot;Segoe UI Emoji&quot;;font-size:16px">More steps, more images, completely new captionsKrea V1:Initial release trained with basic captions.<hr />Z-Image Exp 1:Experimental version trained with chinese captions.Only 2000 steps and a small dataset but produces pretty good images.Trigger:这张照片是用iPhone 13拍摄的。Z-Image V2:More than doubled the steps compared to V1Stronger effectCan be used with higher strengthZ-Image V1:A new version for Z-Image trained on a small dataset.<hr />V4 Beta 2:Trained on more images.V4 Beta 1:I have started to create a completely new dataset with only high quality images. For Beta 1 I used 111 images, though I plan to significantly increase it for Beta 2. All images only have the caption "43stet1c", but it isn't needed to mention it in your prompt.This combination seems to produce the best and most consistent results so far.Increase the strength if the effect isn't strong enough.V3:Same dataset as V2 but completely recaptioned with Claude 3.5 Sonnet and upscaled to 1024 with Gigapixel.V2:Trained on 124 images with no captions at all. Let me know if this works better than V1. Results can be inconsistent, use HiRes fix and Beta scheduler.V1:This was trained on a dataset of 61 "aesthetic" images captioned with Claude 3.5 Sonnet.

Aesthetic Amateur Photo

Aesthetic_Amateur_Photo_V4_Beta_2.safetensors

Aesthetic Amateur Photo [Flux Dev]

V4 Beta 2

Krea V4

Krea V3

Krea V2

Krea V1

Z-Image Exp 1

Z-Image V2

Z-Image V1

V4 Beta 1

Yet another amateur photo LoRA to improve realism.Z-Image V1:A new version for Z-Image trained on a small dataset.V4 Beta 2:Trained on more images.V4 Beta 1:I have started to create a completely new dataset with only high quality images. For Beta 1 I used 111 images, though I plan to significantly increase it for Beta 2. All images only have the caption "43stet1c", but it isn't needed to mention it in your prompt.This combination seems to produce the best and most consistent results so far.Increase the strength if the effect isn't strong enough.V3:Same dataset as V2 but completely recaptioned with Claude 3.5 Sonnet and upscaled to 1024 with Gigapixel.V2:Trained on 124 images with no captions at all. Let me know if this works better than V1. Results can be inconsistent, use HiRes fix and Beta scheduler.V1:This was trained on a dataset of 61 "aesthetic" images captioned with Claude 3.5 Sonnet.

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