Realistic Vision

I am excited to present my latest Realistic checkpoint model based on SD3M. This model has undergone over 100k+ training steps, ensuring high-quality output.

About This Model:

This is a Photo Realistic model, capable of generating photorealistic images. No trigger words are needed. The model is designed to produce high-detail, high-resolution images that closely mimic real-life photographs.

Configuration Used for Training:

GPU: A6000x2
Dataset: A mix of 5k stock photos and my own dataset
Batch Size: 8
Optimizer: AdamW
Scheduler: Cosine with restarts
Learning Rate (LR): 1e-05
Epoch: Target of 300 epochs
Captioning: WD14 and BLIP mix

Important: Avoid including NSFW-related/mature words in your prompts. Doing so may result in unreliable image outcomes. Also, avoid using too long prompts as smaller prompts work better on SD3.

Quick Guide and Parameters:

Clip Encoder: Not required
VAE: Not required
Sampler: dpmpp_2m
Scheduler: sgm_uniform
Sampling Steps: 25+
CFG Scale: 3+

For better results, try using ComfyUI

Workflow

If you download the version without CLIP, please follow these guidelines:

This version won't work like a normal SD3M model. You must load the model using 'Load Diffusion Model.'
You can use all SD3M text encoders that come with it.
You need a VAE. Download it and place it in the VAE folder: ComfyUI\models\vae.
Place the model in the UNet folder: ComfyUI\models\unet.

Note:

This is not a merged or modified model. It is the original Realistic Vision fine-tuned model. Some users have been spreading incorrect information in the model's comment section. If you have any questions or want to know more, join my Discord server or share your thoughts in the comment section. Thank you for your time.

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