Training epochs: 0
Training steps: 7000
Learning rate: 0.0001
- Learning rate schedule: cosine
- Warmup steps: 200
Max grad value: 0.1
Effective batch size: 16
- Micro-batch size: 1
- Gradient accumulation steps: 4
- Number of GPUs: 4
Gradient checkpointing: True
Prediction type: flow_matching (extra parameters=['flow_schedule_auto_shift', 'shift=0.0'])
Optimizer: adamw_bf16
Trainable parameter precision: Pure BF16
Base model precision: no_change
Caption dropout probability: 0.05%

Datasets

images-512

Repeats: 1
Total number of images: ~417748
Total number of aspect buckets: 1
Resolution: 0.262144 megapixels
Cropped: True
Crop style: random
Crop aspect: square
Used for regularisation data: No

images-768

Repeats: 1
Total number of images: ~266740
Total number of aspect buckets: 1
Resolution: 0.589824 megapixels
Cropped: True
Crop style: random
Crop aspect: square
Used for regularisation data: No

images-1024

Repeats: 1
Total number of images: ~246816
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: square
Used for regularisation data: No

Inference

python

import torch
from diffusers import DiffusionPipeline

model_id = 'Minh-Ha/quilt-1m-finetuned-sd3.5-v2'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16

prompt = "A high-resolution photomicrograph of histopathology tissue showing cellular architecture"
negative_prompt = 'blurry, cropped, ugly'

pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
model_output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=20,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
    width=1024,
    height=1024,
    guidance_scale=3.0,
    skip_guidance_layers=[7, 8, 9],
).images[0]

model_output.save("output.png", format="PNG")