Instructions to use p1atdev/plat-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use p1atdev/plat-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("p1atdev/plat-diffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
metadata
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
Plat Diffusion v1.3.0
Plat Diffusion is a fine-tuned model based on Waifu Diffusion v1.4 Anime Epoch 1 with images generated with niji・journey.
masterpiece, best quality, 1girl, orange feather, blue crystals, blurry foreground
Recomended Negative Prompt
nsfw, worst quality, low quality, deleted, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry
🧨 Diffusers
from diffusers import StableDiffusionPipeline
import torch
model_id = "p1atdev/plat-diffusion"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "masterpiece, best quality, 1girl, orange feather, blue crystals, blurry foreground"
image = pipe(prompt).images[0]
image.save("girl.png")
