README.md

---
license: apache-2.0
base_model: "kwai-kolors/kolors-diffusers"
tags:
  - kolors
  - kolors-diffusers
  - text-to-image
  - diffusers
  - simpletuner
  - safe-for-work
  - full


inference: true
widget:
- text: 'unconditional (blank prompt)'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_0_0.png
- text: 'gravitational lensing effects on galaxy'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_1_0.png
---

# gravlens-grayscale

This is a full rank finetune derived from [kwai-kolors/kolors-diffusers](https://huggingface.co/kwai-kolors/kolors-diffusers).


The main validation prompt used during training was:
```
gravitational lensing effects on galaxy
```


## Validation settings
- CFG: `5.0`
- CFG Rescale: `0.0`
- Steps: `20`
- Sampler: `None`
- Seed: `42`
- Resolution: `512x512`


Note: The validation settings are not necessarily the same as the [training settings](#training-settings).

You can find some example images in the following gallery:


<Gallery />

The text encoder **was not** trained.
You may reuse the base model text encoder for inference.


## Training settings

- Training epochs: 0
- Training steps: 6750
- Learning rate: 1e-06
  - Learning rate schedule: constant
  - Warmup steps: 675
- Max grad norm: 2.0
- Effective batch size: 8
  - Micro-batch size: 8
  - Gradient accumulation steps: 1
  - Number of GPUs: 1
- Gradient checkpointing: True
- Prediction type: epsilon (extra parameters=['training_scheduler_timestep_spacing=trailing', 'inference_scheduler_timestep_spacing=trailing'])
- Optimizer: optimi-lion
- Trainable parameter precision: Pure BF16
- Caption dropout probability: 10.0%




## Datasets

### grayscale-lensing-256
- Repeats: 15
- Total number of images: 3689
- Total number of aspect buckets: 1
- Resolution: 0.065536 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### grayscale-lensing-512
- Repeats: 15
- Total number of images: 1801
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No


## Inference


```python
import torch
from diffusers import DiffusionPipeline

model_id = 'GazTrab/gravlens-grayscale'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32) # loading directly in bf16

prompt = "gravitational lensing effects on galaxy"
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
image = 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=512,
    height=512,
    guidance_scale=5.0,
    guidance_rescale=0.0,
).images[0]
image.save("output.png", format="PNG")
```