sd3-reality-mix
This is a full rank finetune derived from stabilityai/stable-diffusion-3-medium-diffusers.
The main validation prompt used during training was:
ethnographic photography of teddy bear at a picnic holding a sign that says SOON, sitting next to a red sphere which is inside a capsule
Validation settings
- CFG:
5.5 - CFG Rescale:
0.0 - Steps:
30 - Sampler:
euler - Seed:
42 - Resolutions:
512x512,1024x1024,1280x768,960x1152
Note: The validation settings are not necessarily the same as the training settings.
You can find some example images in the following gallery:
The text encoder was not trained. You may reuse the base model text encoder for inference.
Training settings
- Training epochs: 0
- Training steps: 7000
- Learning rate: 1e-06
- Effective batch size: 96
- Micro-batch size: 6
- Gradient accumulation steps: 2
- Number of GPUs: 8
- Prediction type: epsilon
- Rescaled betas zero SNR: False
- Optimizer: AdamW, stochastic bf16
- Precision: Pure BF16
- Xformers: Not used
Datasets
photo-concept-bucket
- Repeats: 0
- Total number of images: ~557568
- Total number of aspect buckets: 38
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
dalle3
- Repeats: 0
- Total number of images: ~984960
- Total number of aspect buckets: 44
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
Inference
import torch
from diffusers import StableDiffusion3Pipeline
model_id = "sd3-reality-mix"
prompt = "ethnographic photography of teddy bear at a picnic holding a sign that says SOON, sitting next to a red sphere which is inside a capsule"
negative_prompt = "malformed, disgusting, overexposed, washed-out"
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
prompt=prompt,
negative_prompt='blurry, cropped, ugly',
num_inference_steps=30,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
width=1152,
height=768,
guidance_scale=5.5,
guidance_rescale=0.0,
).images[0]
image.save("output.png", format="PNG")