Text-to-Image
Diffusers
flux
flux-diffusers
simpletuner
Not-For-All-Audiences
lora
template:sd-lora
standard
File size: 4,324 Bytes
17069dc 96a4c30 17069dc 96a4c30 17069dc 96a4c30 17069dc 4e2c0a9 15c1c73 17069dc 15c1c73 17069dc 2299578 17069dc 2299578 17069dc 2299578 17069dc 96a4c30 17069dc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 |
---
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
---
# maver1chh/jazzy2312
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:
```
A girl in light blue sits at the bar counter, holding an ice-cold wine glass and drinking alone on top of the Eiffel Tower, with a night view outside the window.. It features a close-up shot of her sitting by herself. She has long hair, wears glasses, faces away from the camera, and is wearing white shoes, black pants, a gray jacket, and a green scarf. with bright colors and a Paris night background featuring the Eiffel Tower. The composition is elegant, with the woman sitting on a high stool.
```
## Validation settings
- CFG: `3.0`
- CFG Rescale: `0.0`
- Steps: `20`
- Sampler: `FlowMatchEulerDiscreteScheduler`
- Seed: `42`
- Resolution: `1080x1920`
- Skip-layer guidance:
Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
<Gallery />
The text encoder **was not** trained.
You may reuse the base model text encoder for inference.
## Training settings
- Training epochs: 7
- Training steps: 6000
- Learning rate: 0.0003
- Learning rate schedule: polynomial
- Warmup steps: 100
- Max grad norm: 1.5
- 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: 32
- LoRA Alpha: 32.0
- LoRA Dropout: 0.1
- LoRA initialisation style: default
## Datasets
### jazzy-512
- Repeats: 10
- Total number of images: 23
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### jazzy-800
- Repeats: 10
- Total number of images: 23
- Total number of aspect buckets: 1
- Resolution: 0.64 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### jazzy-1024
- Repeats: 10
- Total number of images: 23
- 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/jazzy2312'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)
prompt = "A girl in light blue sits at the bar counter, holding an ice-cold wine glass and drinking alone on top of the Eiffel Tower, with a night view outside the window.. It features a close-up shot of her sitting by herself. She has long hair, wears glasses, faces away from the camera, and is wearing white shoes, black pants, a gray jacket, and a green scarf. with bright colors and a Paris night background featuring the Eiffel Tower. The composition is elegant, with the woman sitting on a high stool."
## 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=1920,
guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")
```
|