Text-to-Image
Diffusers
English
stable-diffusion-xl
stable-diffusion-xl-diffusers
diffusers-training
lora
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("keyurhirpara/text_to_WebPage")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]text2webpage fine-tuning - keyurhirpara/Text_to_WebPage
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the keyurhirpara/Website_Img_5 dataset. You can find some example images in the following.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
Intended uses & limitations
How to use
from diffusers import DiffusionPipeline
import torch
model_path = "keyurhirpara/text_to_WebPage"
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True,)
pipe.to("cuda")
pipe.load_lora_weights(model_path)
prompt = "Create a English website page for Travel Agency: A visually immersive design with high-resolution images, a center-aligned logo, and a top navigation menu with links for vacation packages, destinations, and customer reviews. The design includes scrolling sections for showcasing destinations and highlighting the latest discount offers."
images = pipe(prompt=prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
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 keyurhirpara/text_to_WebPage
Base model
stabilityai/stable-diffusion-xl-base-1.0
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