Text-to-Image
Diffusers
Safetensors
StableDiffusion3Pipeline
diffusers-training
sd3
sd3-diffusers
template:sd-lora
lora
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("igorgoncharenko15/trained-sd3")
prompt = "A picture of sbercat looking straight"
image = pipe(prompt).images[0]SD3 DreamBooth - igorgoncharenko15/trained-sd3
- Prompt
- A picture of sbercat looking straight
- Prompt
- A picture of sbercat looking straight
- Prompt
- A picture of sbercat looking straight
- Prompt
- A picture of sbercat looking straight
Model description
These are igorgoncharenko15/trained-sd3 DreamBooth weights for stabilityai/stable-diffusion-3-medium-diffusers.
The weights were trained using DreamBooth with the SD3 diffusers trainer.
Was the text encoder fine-tuned? False.
Trigger words
You should use a picture of sbercat to trigger the image generation.
Use it with the 🧨 diffusers library
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('igorgoncharenko15/trained-sd3', torch_dtype=torch.float16).to('cuda')
image = pipeline('A picture of sbercat looking straight').images[0]
License
Please adhere to the licensing terms as described [here](https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/LICENSE).
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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