Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
diffusers-training
lora
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("RiddleHe/SD14_pathology_lora")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]LoRA text2image fine-tuning - RiddleHe/SD14_pathology_lora
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the None dataset. You can find some example images in the following.
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Intended uses & limitations
How to use
pipe = DiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16
)
pipe.load_lora_weights("RiddleHe/SD14_pathology_lora")
pipe.to('cuda')
prompt = "A histopathology image of breast cancer tissue"
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
This model is trained on 28216 breast cancer tissue images from the BRCA dataset.
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Model tree for RiddleHe/SD14_pathology_lora
Base model
CompVis/stable-diffusion-v1-4