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kamillaova/
epiCRealism

Name: epiCRealism
Availability: InStock

diffusers

safetensors

text-to-image

runwayml/stable-diffusion-v1-5

finetune

creativeml-openrail-m

autotrain_compatible

endpoints_compatible

StableDiffusionPipeline

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Examples

Natural Sin Final and last of epiCRealism

~~Since SDXL is right around the corner~~, let's say it is the final version for now since I put a lot effort into it and probably cannot do much more.

I tried to refine the understanding of the Prompts, Hands and of course the Realism.
Let's see what you guys can do with it.

Advices:

use simple prompts
no need to use keywords like "masterpiece, best quality, 8k, intricate, high detail" or "(extremely detailed face), ( extremely detailed hands), (extremely detailed hair)" since it doesn't produce appreciable change
use simple negatives or small negative embeddings. gives most realistic look (check samples to get an idea of negatives i used)
add "asian, chinese" to negative if you're looking for ethnicities other than Asian
Light, shadows, and details are excellent without extra keywords
If you're looking for a natural effect, avoid "cinematic"
avoid using "1girl" since it pushes things to render/anime style
to much description of the face will turn out bad mostly
no extra noise-offset needed, but u can if you like to 😉

How to use?

Prompt: simple explanation of the image (try first without extra keywords)
Negative: "cartoon, painting, illustration, (worst quality, low quality, normal quality:2)"
Steps: >50 (if image has errors or artefacts use higher steps)
CFG Scale: 5.0 (higher config scale can lose realism, depends on prompt, sampler and steps)
Size: 512x768 or 768x512

Use it with 🧨 diffusers

This model can be used just like any other Stable Diffusion model. For more information, please have a look at the Diffusers.

python

import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
  'kamillaova/epiCRealism',
  torch_dtype=torch.float16
).to('cuda')

img = pipe(
  prompt='positive prompt',
  negative_prompt='negative prompt',

  guidance_scale=5.0,
  num_inference_steps=50,
  width=512, height=768,
).images[0]

img.save('image.png')