veryVANYA/ps1-style-flux

PS1 Style Flux

Prompt

ps1 game screenshot, the flash in a wheelchair, winning the boston marathon, hot streaks behind him as his wheelchair drives across the finish line, there is a banner overhead at the finish line with text ps1 flux by veryvanya, the flashs wheelchair is sleek and high-tech, with glowing blue accents and a bold, red stripe running along the side, his iconic red and gold costume is torn and worn, but still vibrant, with a few loose threads and scorch marks, the finish line is crowded with cheering spectators, the sky above is a bright, sunny blue, with a few puffy white clouds, the flashs face is set in a determined expression, his eyes fixed intently on the finish line, his arms are muscular and toned, with a few scars and bruises visible, the wheelchairs wheels are blurred, conveying a sense of speed and motion, the hot streaks behind the flash are long and curved, like a comets tail, and are colored in shades of orange, yellow, and red, the banner overhead is made of a shiny, metallic material, and the text ps1 flux by veryvanya is written in bold, graffiti-style letters

Prompt

ps1 game screenshot, a little strawberry hamster floating on a large green leaf on a gentle river

Model description

works best when you include 'ps1 game screenshot' in prompt

late 90s/early 2000s ps1/n64 console graphics.

5000 steps

trained on 15 gpt4o captioned and adjusted ps1/n64 game screenshots using https://github.com/ostris/ai-toolkit/tree/main

Trigger words

You should use ps1 to trigger the image generation.

Download model

Weights for this model are available in Safetensors format.

Download them in the Files & versions tab.

Use it with the 🧨 diffusers library

from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('veryVANYA/ps1-style-flux', weight_name='ps1_style_flux_v1.safetensors')
image = pipeline('`ps1`').images[0]

For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers