license: creativeml-openrail-m
language:
- en
pipeline_tag: text-to-image
tags:
- art
Overview πβοΈ
This is a Diffusers-compatible version of Yiffymix v51 by chilon249. See the original page for more information.
Keep in mind that this is SDXL-Lightning checkpoint model, so using fewer steps (around 12 to 25) and low guidance scale (around 4 to 6) is recommended for the best result. It's also recommended to use clip skip of 2.
This repo uses DPM++ 2M Karras as its sampler (Diffusers only).
Diffusers Installation π§¨
Dependencies Installation π
First, you'll need to install few dependencies. This is a one-time operation, you only need to run the code once.
!pip install -q diffusers transformers accelerate
Model Installation πΏ
After the installation, you can run SDXL with Yiffymix v51 model using the code below:
from diffusers import StableDiffusionXLPipeline
import torch
model = "IDK-ab0ut/Yiffymix_v51-XL"
pipeline = StableDiffusionXLPipeline.from_pretrained(
model, torch_dtype=torch.float16).to("cuda")
prompt = "a cat, detailed background, dynamic lighting"
negative_prompt = "low resolution, bad quality, deformed"
steps = 25
guidance_scale = 4
image = pipeline(prompt=prompt, negative_prompt=negative_prompt,
num_inference_steps=steps, guidance_scale=guidance_scale,
clip_skip=2).images[0]
image
Feel free to edit the image's configuration with your desire.
Scheduler's Customization βοΈ
γ €γ €γ €γ €π§¨For Diffusersπ§¨
You can see all available schedulers here.
To use scheduler other than DPM++ 2M Karras for this repo, make sure to import the corresponding pipeline for the scheduler you want to use. For example, we want to use Euler. First, import EulerDiscreteScheduler from Diffusers by adding this line of code.
from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
Next step is to load the scheduler.
model = "IDK-ab0ut/Yiffymix_v51"
euler = EulerDiscreteScheduler.from_pretrained(
model, subfolder="scheduler")
pipeline = StableDiffusionXLPipeline.from_pretrained(
model, scheduler=euler, torch.dtype=torch.float16
).to("cuda")
Now you can generate any images using the scheduler you want.
Another example is using DPM++ 2M SDE Karras. We want to import DPMSolverMultistepScheduler from Diffusers first.
from diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler
Next, load the scheduler into the model.
model = "IDK-ab0ut/Yiffymix_v51"
dpmsolver = DPMSolverMultistepScheduler.from_pretrained(
model, subfolder="scheduler", use_karras_sigmas=True,
algorithm_type="sde-dpmsolver++").to("cuda")
# 'use_karras_sigmas' is called to make the scheduler
# use Karras sigmas during sampling.
pipeline = StableDiffusionXLPipeline.from_pretrained(
model, scheduler=dpmsolver, torch.dtype=torch.float16,
).to("cuda")