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v2-aesthetic
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#playground

Playground v2 – 1024px Aesthetic Model

This repository contains a model that generates highly aesthetic images of resolution 1024x1024. You can use the model with Hugging Face 🧨 Diffusers.

Playground v2 is a diffusion-based text-to-image generative model. The model was trained from scratch by the research team at Playground.

Images generated by Playground v2 are favored 2.5 times more than those produced by Stable Diffusion XL, according to Playground’s user study.

We are thrilled to release [intermediate checkpoints](#intermediate-base-models) at different training stages, including evaluation metrics, to the community. We hope this will encourage further research into foundational models for image generation.

Lastly, we introduce a new benchmark, MJHQ-30K, for automatic evaluation of a model’s aesthetic quality.

Please see our blog for more details.

Model Description

- Developed by: Playground

- Model type: Diffusion-based text-to-image generative model

- License: Playground v2 Community License

Using the model with 🧨 Diffusers

Install diffusers >= 0.24.0 and some dependencies:

pip install transformers accelerate safetensors

To use the model, run the following snippet.

Note: It is recommend to use `guidance_scale=3.0`.

from diffusers import DiffusionPipeline

import torch

pipe = DiffusionPipeline.from_pretrained(

    "playgroundai/playground-v2-1024px-aesthetic",

    torch_dtype=torch.float16,

    use_safetensors=True,

    add_watermarker=False,

    variant="fp16"

)

pipe.to("cuda")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"

image  = pipe(prompt=prompt, guidance_scale=3.0).images[0]

Using the model with Automatic1111/ComfyUI

In order to use the model with software such as Automatic1111 or ComfyUI you can use playground-v2.fp16.safetensors file.

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2024-02-16
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2024-02-16
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Detalles del modelo
Tipo
Checkpoint
Fecha de lanzamiento
2024-02-16
Modelo básico
SDXL 1.0
Alcance de la licencia
Fuente: civitai

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Alcance de la licencia de creación
Transmisión en vivo
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Las imágenes generadas se pueden vender o usar con fines comerciales
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