Vanta XL — Premier Hyper-Realistic AI Influencer & Lifestyle Checkpoint
Vanta XL is a fine-tuned SDXL checkpoint engineered specifically for producing hyper-realistic digital creators, AI influencers, and luxury lifestyle imagery. Built to eliminate the glossy, "plastic" look common in AI photos, Vanta XL renders ultra-detailed skin textures, lifelike eyes, natural lighting, and high-fashion aesthetics with exceptional consistency.
Whether you are crafting a continuous digital persona for social media, generating editorial portraiture, or showcasing luxury street fashion, Vanta XL delivers studio-quality output with clean prompt adherence.
Key Features
Photorealistic Micro-Details: Renders natural skin pore structures, realistic freckles, fine facial hair, subtle imperfections, and realistic subsurface scattering.
Influencer & Social Media Aesthetic: Optimized for candid portraiture, street style fashion, mirror selfies, luxury travel environments, and high-end studio lighting.
Lighting Versatility: Handles complex lighting scenarios with ease—golden hour glow, harsh direct sunlight, softbox studio setups, flash photography, and neon nightscapes.
No Heavy Quality Tags Needed: Designed to work best with simple descriptive photographic language rather than word-salad quality prompts.
Recommended Generation Settings
Base Architecture: SDXL 1.0
Sampling Method:
DPM++ 2M KarrasorDPM++ SDE KarrasSampling Steps: 30 – 40 steps
CFG Scale: 4.5 – 6.5 (Lower CFG produces softer, more lifelike skin dynamics)
Native Resolutions:
896x1152(Portrait 3:4),832x1216(Story 9:16), or1024x1024(Square)Clip Skip: 1 or 2
Hi-Res Fix / Hires Pass:
Upscaler:
4x-UltraSharpor4x_NMKD-SuperscaleHires Steps: 10 – 15
Denoising Strength: 0.25 – 0.35
Prompting Guide
1. Example Positive Prompt
raw shot, photo of a female influencer in her 20s, wearing an oversized beige trench coat, standing in a modern architectural lobby, soft directional morning light, shot on 85mm f/1.8 lens, natural skin texture, detailed brown eyes, candid expression, realistic photography
2. Negative Prompt Recommendation
Keep your negative prompt light to avoid killing detail and dynamic range:
3d render, cartoon, illustration, anime, smooth skin, plastic skin, airbrushed, CGI, distorted eyes, bad anatomy, overexposed, oversaturated, watermark
Licensing & Usage
Image Generation: Commercial usage allowed for selling output images.
Merges: Allowed to share and iterate on merges using the same permissions.
Credit: Credit is appreciated but not strictly required.
Generative AI Resources
Description
Version 1
Limited free download
FAQ
Comments (8)
It gives the following errors even with external SDXL Vae loaded :
RuntimeError: Error(s) in loading state_dict for AutoencoderKL:
size mismatch for encoder.mid.attn_1.q.weight: copying a param with shape torch.Size([512, 512, 1, 1, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 512, 1, 1]).
size mismatch for encoder.mid.attn_1.k.weight: copying a param with shape torch.Size([512, 512, 1, 1, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 512, 1, 1]).
size mismatch for encoder.mid.attn_1.v.weight: copying a param with shape torch.Size([512, 512, 1, 1, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 512, 1, 1]).
size mismatch for encoder.mid.attn_1.proj_out.weight: copying a param with shape torch.Size([512, 512, 1, 1, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 512, 1, 1]).
size mismatch for decoder.mid.attn_1.q.weight: copying a param with shape torch.Size([512, 512, 1, 1, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 512, 1, 1]).
size mismatch for decoder.mid.attn_1.k.weight: copying a param with shape torch.Size([512, 512, 1, 1, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 512, 1, 1]).
size mismatch for decoder.mid.attn_1.v.weight: copying a param with shape torch.Size([512, 512, 1, 1, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 512, 1, 1]).
size mismatch for decoder.mid.attn_1.proj_out.weight: copying a param with shape torch.Size([512, 512, 1, 1, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 512, 1, 1]).
Hey, sorry that's strange for me it's working normally. Do you have the latent set to 1024x1024 ?
If not it may produce errors
@TensorVizion I made the empty latent as 1024x1024 (1.0) , still not solved, the error happens in the node loading this checkpoint, I tried different checkpoint loader & tried loading Clip & Vae separately, still not solved (btw I am using ComfyUi 0.34 & Python 3.12.10 Torch2.8 cu128) .
Maybe I am not lucky enough to use this model here, I wish others will have more luck with it than I did :)
Also getting this issue, seems to be a problem with the baked in VAE where the checkpoint is trying to use 6 dimensions but the VAE is giving only 4
@Maohime fixing now will be up later today
@TensorVizion Still getting errors sadly with this new upload.
In Forge Classic it shows large lists of missing and unexpected keys. The file appears to use Diffusers style naming for the VAE (encoder.down_blocks and encoder.mid_block) and UNet middle block while the loaders expect the standard CompVis LDM style keys.
The CLIP text encoder also has a naming mismatch (positional_embedding vs position_embedding).
same problem
My program is labeling the model as SSD 1B instead of SDXL. So it won’t load at all because it’s not actually that model.