metadata
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
- Retro
- Pixel
widget:
- text: >-
Retro Pixel, A pixelated image of a german shepherd dog. The dogs fur is a
vibrant shade of brown, with a black stripe running down its back. The
background is a light green, and the dogs shadow is cast on the ground.
output:
url: images/RP1.png
- text: >-
Retro Pixel, A pixelated image of a man surfing on a surfboard. The mans
body is covered in a red shirt and blue shorts. His arms are out to the
sides of his body. The surfboard is a vibrant blue color. The water is a
light blue color with white splashes. The sun is shining on the right side
of the image.
output:
url: images/RP2.png
- text: >-
Retro Pixel, pixel art of a Hamburger in the style of an old video game,
hero, pixelated 8bit, final boss
output:
url: images/RP3.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Retro Pixel
license: creativeml-openrail-m
Retro-Pixel-Flux-LoRA
The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.
Model description
prithivMLmods/Retro-Pixel-Flux-LoRA
Image Processing Parameters
Parameter | Value | Parameter | Value |
---|---|---|---|
LR Scheduler | constant | Noise Offset | 0.03 |
Optimizer | AdamW | Multires Noise Discount | 0.1 |
Network Dim | 64 | Multires Noise Iterations | 10 |
Network Alpha | 32 | Repeat & Steps | 24 & 2340 |
Epoch | 15 | Save Every N Epochs | 1 |
Labeling: florence2-en(natural language & English)
Total Images Used for Training : 16 [ Hi-RES ]
Best Dimensions
- 1024 x 1024 (Default)
Setting Up
import torch
from pipelines import DiffusionPipeline
base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
lora_repo = "prithivMLmods/Retro-Pixel-Flux-LoRA"
trigger_word = "Retro Pixel"
pipe.load_lora_weights(lora_repo)
device = torch.device("cuda")
pipe.to(device)
Trigger words
You should use Retro Pixel
to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.