Text-to-Image
Diffusers
flux
flux-diffusers
simpletuner
Not-For-All-Audiences
lora
template:sd-lora
standard
File size: 4,856 Bytes
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---
license: other
base_model: "black-forest-labs/FLUX.1-dev"
tags:
- flux
- flux-diffusers
- text-to-image
- diffusers
- simpletuner
- not-for-all-audiences
- lora
- template:sd-lora
- standard
inference: true
widget:
- text: 'unconditional (blank prompt)'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_0_0.png
- text: 'An illustration of the beach in Malibu, California with palm trees and ocean view during sunset. A classic car is parked on an empty street next to two palm trees near a stop sign. There is a yellow line drawn across the road leading towards the beach. The sky casts long shadows over the scene, creating a warm glow that highlights the serene beauty of the landscape, illustration in classic vibes.'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_1_0.png
---
# maver1chh/jazzy0301
This is a standard PEFT LoRA 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:
```
An illustration of the beach in Malibu, California with palm trees and ocean view during sunset. A classic car is parked on an empty street next to two palm trees near a stop sign. There is a yellow line drawn across the road leading towards the beach. The sky casts long shadows over the scene, creating a warm glow that highlights the serene beauty of the landscape, illustration in classic vibes.
```
## Validation settings
- CFG: `3.0`
- CFG Rescale: `0.0`
- Steps: `20`
- Sampler: `FlowMatchEulerDiscreteScheduler`
- Seed: `42`
- Resolution: `1080x1980`
- Skip-layer guidance:
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: 3
- Training steps: 3500
- Learning rate: 0.0008
- Learning rate schedule: polynomial
- Warmup steps: 1000
- Max grad norm: 1.0
- Effective batch size: 1
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Gradient checkpointing: True
- Prediction type: flow-matching (extra parameters=['shift=3', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flow_matching_loss=compatible', 'flux_lora_target=all'])
- Optimizer: adamw_bf16
- Trainable parameter precision: Pure BF16
- Caption dropout probability: 10.0%
- LoRA Rank: 16
- LoRA Alpha: 16.0
- LoRA Dropout: 0.1
- LoRA initialisation style: default
## Datasets
### jazz512_0301
- Repeats: 10
- Total number of images: 28
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### jazz768_0301
- Repeats: 10
- Total number of images: 28
- Total number of aspect buckets: 1
- Resolution: 0.589824 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### jazz1024_0301
- Repeats: 10
- Total number of images: 28
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
## Inference
```python
import torch
from diffusers import DiffusionPipeline
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'maver1chh/maver1chh/jazzy0301'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)
prompt = "An illustration of the beach in Malibu, California with palm trees and ocean view during sunset. A classic car is parked on an empty street next to two palm trees near a stop sign. There is a yellow line drawn across the road leading towards the beach. The sky casts long shadows over the scene, creating a warm glow that highlights the serene beauty of the landscape, illustration in classic vibes."
## Optional: quantise the model to save on vram.
## Note: The model was quantised during training, and so it is recommended to do the same during inference time.
from optimum.quanto import quantize, freeze, qint8
quantize(pipeline.transformer, weights=qint8)
freeze(pipeline.transformer)
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,
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=1080,
height=1980,
guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")
```
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