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---
license: other
base_model: "black-forest-labs/FLUX.1-dev"
tags:
- flux
- flux-diffusers
- text-to-image
- diffusers
- simpletuner
- safe-for-work
- lora
- template:sd-lora
- lycoris
inference: true
widget:
- text: 'unconditional (blank prompt)'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_0_0.png
- text: 'A rocky Maine coastline with bold, geometric shapes representing cliffs and waves. Strong colors and simplified forms dominate the composition, in the style of MRSDN'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_1_0.png
- text: 'An abstract composition inspired by Berlin''s urban life. Fragmented shapes, numbers, and symbols arranged in a Cubist-influenced style, in the style of MRSDN'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_2_0.png
- text: 'A still life of flowers in a vase, rendered with thick brushstrokes and vibrant, non-naturalistic colors. Simplified forms show Cubist influence, in the style of MRSDN'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_3_0.png
- text: 'A stark New Mexico landscape with stylized mountains and desert flora. Bold outlines and earthy colors capture the essence of the Southwest, in the style of MRSDN'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_4_0.png
- text: 'A portrait of a WWI German soldier, composed of geometric shapes and military symbols. Strong, emotive use of color and form, in the style of MRSDN'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_5_0.png
- text: 'Mount Katahdin in Maine, depicted with sharp angles and bold colors. The landscape is reduced to its essential forms, emphasizing its rugged nature, in the style of MRSDN'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_6_0.png
- text: 'Modern New York skyscrapers rendered in Hartley''s style. Geometric shapes and bold colors create a dynamic urban composition, in the style of MRSDN'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_7_0.png
- text: 'An orbiting space station viewed through a Modernist lens. Fragmented forms and symbolic elements represent the futuristic structure, in the style of MRSDN'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_8_0.png
- text: 'An electric car charging station, depicted with Cubist-inspired fragmentation. Bold colors and geometric shapes represent energy and technology, in the style of MRSDN'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_9_0.png
- text: 'A composition of social media icons and symbols, arranged in a Modernist style reminiscent of Hartley''s German officer paintings, in the style of MRSDN'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_10_0.png
- text: 'An abstract representation of climate change, using Hartley''s bold style to depict melting ice caps, rising seas, and changing weather patterns, in the style of MRSDN'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_11_0.png
- text: 'A person wearing a VR headset, surrounded by fragmented, Cubist-inspired virtual elements. Bold colors and geometric forms dominate the composition, in the style of MRSDN'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_12_0.png
- text: 'hamster, in the style of MRSDN'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_13_0.png
- text: 'hamster in the style of MRSDN'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_14_0.png
---
# Flux-Marsden-Hartley-LoKr-SimpleTuner-03
This is a LyCORIS adapter derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev).
The main validation prompt used during training was:
```
hamster in the style of MRSDN
```
## Validation settings
- CFG: `3.0`
- CFG Rescale: `0.0`
- Steps: `20`
- Sampler: `None`
- Seed: `42`
- Resolution: `1024x1024`
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: 11
- Training steps: 7000
- Learning rate: 0.0004
- Effective batch size: 2
- Micro-batch size: 2
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: Pure BF16
- Quantised: Yes: int8-quanto
- Xformers: Not used
- LyCORIS Config:
```json
{
"algo": "lokr",
"multiplier": 1.0,
"linear_dim": 10000,
"linear_alpha": 1,
"factor": 16,
"apply_preset": {
"target_module": [
"Attention",
"FeedForward"
],
"module_algo_map": {
"Attention": {
"factor": 16
},
"FeedForward": {
"factor": 8
}
}
}
}
```
## Datasets
### marsden-hartley-Flux-CC-512
- Repeats: 10
- Total number of images: 25
- Total number of aspect buckets: 4
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### marsden-hartley-Flux-CC-1024
- Repeats: 10
- Total number of images: 25
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### marsden-hartley-Flux-CC-512-crop
- Repeats: 10
- Total number of images: 25
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
### marsden-hartley-Flux-CC-1024-crop
- Repeats: 10
- Total number of images: 25
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
## Inference
```python
import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer)
wrapper.merge_to()
prompt = "hamster in the style of MRSDN"
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
prompt=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(1641421826),
width=1024,
height=1024,
guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")
```
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