Text-to-Image
Diffusers
Safetensors
stable-diffusion-xl
stable-diffusion-xl-diffusers
t2iadapter
diffusers-training
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("coscotuff/SDXL-LOGO-CHECKPOINTS", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]t2iadapter-SAGI-1/output
These are t2iadapter weights trained on stabilityai/stable-diffusion-xl-base-1.0 with new type of conditioning.
You can find some example images below.
prompt: Design a circular logo for a boutique featuring rose gold, brush strokes, and glitter elements, exuding a feminine and luxurious atmosphere
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]
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Model tree for coscotuff/SDXL-LOGO-CHECKPOINTS
Base model
stabilityai/stable-diffusion-xl-base-1.0