growwithdaisy/001-dsymmlnznll_20250214_024629_4e-4_polynomial
This is a standard PEFT LoRA derived from FLUX.1-dev.
The main validation prompt used during training was:
dsymmlnznll style
Validation settings
- CFG:
3.5 - CFG Rescale:
0.0 - Steps:
20 - Sampler:
FlowMatchEulerDiscreteScheduler - Seed:
69 - Resolution:
1024x1024 - Skip-layer guidance:
Note: The validation settings are not necessarily the same as the training settings.
You can find some example images in the following gallery:
- Prompt
- unconditional (blank prompt)
- Negative Prompt
- blurry, cropped, ugly
- Prompt
- dsymmlnznll style
- Negative Prompt
- blurry, cropped, ugly
- Prompt
- dsymmlnznll style
- Negative Prompt
- blurry, cropped, ugly
- Prompt
- dsymmlnznll style
- Negative Prompt
- blurry, cropped, ugly
- Prompt
- dsymmlnznll style
- Negative Prompt
- blurry, cropped, ugly
- Prompt
- dsymmlnznll style
- Negative Prompt
- blurry, cropped, ugly
The text encoder was not trained. You may reuse the base model text encoder for inference.
Training settings
Training epochs: 41
Training steps: 1000
Learning rate: 0.0004
- Learning rate schedule: polynomial
- Warmup steps: 100
Max grad norm: 2.0
Effective batch size: 8
- Micro-batch size: 2
- Gradient accumulation steps: 1
- Number of GPUs: 4
Gradient checkpointing: True
Prediction type: flow-matching (extra parameters=['flow_schedule_auto_shift', 'shift=0.0', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flow_matching_loss=compatible', 'flux_lora_target=all+ffs'])
Optimizer: optimi-stableadamwweight_decay=1e-2
Trainable parameter precision: Pure BF16
Caption dropout probability: 5.0%
LoRA Rank: 16
LoRA Alpha: 16.0
LoRA Dropout: 0.1
LoRA initialisation style: default
Datasets
001-dsymmlnznll-512
- Repeats: 0
- Total number of images: ~48
- Total number of aspect buckets: 5
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
001-dsymmlnznll-768
- Repeats: 0
- Total number of images: ~48
- Total number of aspect buckets: 6
- Resolution: 0.589824 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
001-dsymmlnznll-1024
- Repeats: 0
- Total number of images: ~48
- Total number of aspect buckets: 6
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
Inference
import torch
from diffusers import DiffusionPipeline
model_id = 'FLUX.1-dev'
adapter_id = 'playerzer0x/growwithdaisy/001-dsymmlnznll_20250214_024629_4e-4_polynomial'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)
prompt = "dsymmlnznll style"
## Optional: quantise the model to save on vram.
## Note: The model was not quantised during training, so it is not necessary to quantise it 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(69),
width=1024,
height=1024,
guidance_scale=3.5,
).images[0]
image.save("output.png", format="PNG")
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