stable-diffusion text-to-image diffusers DiffusionPipeline

For best results, use the following command


License: CreativeML Open RAIL++-M License

You are free to:

Under the following terms:


Examples of prompts used

Prompt: melaura, girl, asian woman with long dark hair wearing a white dress, a photorealistic painting by Lü Ji, trending on cg society, photorealism, popular korean makeup, popular south korean makeup, clear makeup

Guidance_Scale: 7.5

Num_Inference_Steps: 50

scheduler type: EulerAncestralDiscreteScheduler Image description

Prompt: melaura, girl, a woman with a pearl necklace and pearl necklace, a photorealistic painting by Kim Deuk-sin, featured on cg society, photorealism, portrait of female korean idol, popular korean makeup, popular south korean makeup, shiny cheeks

Guidance_Scale: 7.5

Num_Inference_Steps: 50

scheduler type: EulerAncestralDiscreteScheduler Image description

Prompt: melaura, girl,

Guidance_Scale: 7.5

Num_Inference_Steps: 50

scheduler type: EulerAncestralDiscreteScheduler Image description


Gradio & Colab

We also support a Gradio Web UI and Colab with Diffusers to run Melaura: Open In Colab

🧨 Diffusers

Before running it, make sure you have the necessary pip libraries installed:

pip install -q diffusers transformers omegaconf

Running the pipeline (if you don't swap the scheduler, it will run with the default EulerDiscreteScheduler. In this example, we're swapping it to EulerAncestralDiscreteScheduler:

from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler
import torch
import os

repo_id = "DamarJati/melaura-v1"

pipe = StableDiffusionPipeline.from_pretrained(
    repo_id,
    torch_dtype=torch.float16,
    use_karras_sigmas=True,
    algorithm_type="sde-dpmsolver++"
    )
pipe.to('cuda')

#Safety Checker
pipe.safety_checker = None
pipe.requires_safety_checker = False

#Scheduler
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)


Prompt = "melaura, girl, hd, pink lips, detailed, age 16, Off-shoulder top, shiny cheeks"
Negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry"

image = pipe(
    prompt=Prompt, 
    negative_prompt=Negative_prompt, 
    width=512,
    height=512,
    guidance_scale=8,
    num_inference_steps=50
    ).images[0]
image.save("melaura.png")

Limitation

This model inherit Stable Diffusion 1.5 limitation