metadata
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
- lycoris
inference: true
widget:
- text: unconditional (blank prompt)
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_0_0.png
- text: >-
in the style of a Michelangelo sculpture, Three elderly women huddle
together, their robes intertwined as they share a scroll between them.
Their faces show deep concentration, with pronounced wrinkles and hollow
cheeks.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_1_0.png
- text: in the style of a Michelangelo sculpture, a hamster
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_2_0.png
- text: >-
in the style of a Michelangelo sculpture, A plump hamster sits upright on
its haunches, tiny paws clutching a seed with remarkable dignity. Its fur
is rendered in detailed marble ripples, while its alert ears are tilted
forward attentively. The creature's round cheeks suggest stored food, and
its whiskers are delicately carved. The base is decorated with miniature
carved leaves and fallen seeds, while the background remains unadorned and
shadowed.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_3_0.png
- text: >-
in the style of a Michelangelo sculpture, A Range Rover emerges from solid
marble, its commanding presence emphasized by strong angular lines and
bold proportions. The vehicle rests in a three-quarter pose, with its
distinctive grille and headlights carved in meticulous detail. Each wheel
arch suggests latent motion, while the smooth curves of the hood flow into
the upright windscreen. The base appears to ripple like terrain beneath
the wheels, suggesting the vehicle's adventurous nature. The background is
stark, drawing attention to the interplay of light and shadow across the
sculptured surfaces.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_4_0.png
- text: >-
in the style of a Michelangelo sculpture, A young girl stands on tiptoes
reaching upward, her hair falling in loose waves. A ribbon streams behind
her, caught in an invisible wind. The base beneath her feet shows carved
clouds, suggesting she floats between earth and sky.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_5_0.png
- text: a man holding a sign that says, 'this is a sign
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_6_0.png
- text: >-
a pig, in a post apocalyptic world, with a shotgun, in a leather jacket,
in a desert, with a motorcycle
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_7_0.png
- text: >-
in the style of a Michelangelo sculpture, woman holding a sign that says
'I LOVE PROMPTS!'.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_8_0.png
- text: >-
in the style of a Michelangelo sculpture, A nude male figure stands tall
on a pedestal, his left arm is raised, while his right arm hangs freely by
his side. The figure has curly hair and a focused, determined expression
on his face looking slightly to his left. In the background, there are
large panels with rectangular details on the walls, suggesting an indoor
setting.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_9_0.png
- text: >-
in the style of a Michelangelo sculpture, a bearded man sits down dressed
in long garments. The background is plain.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_10_0.png
- text: >-
in the style of a Michelangelo sculpture, a woman cradling a lifeless man
on her lap. The woman wears draped clothing with a hood, and has a
sorrowful expression. The man is depicted naked except for a cloth around
his waist, his arms and legs extended lifelessly. The background is an
intricate marble wall with a mixed pattern of colors (brown, green,
beige).
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_11_0.png
michelangelo-phase2-2e-4-ss3.0
This is a LyCORIS adapter derived from black-forest-labs/FLUX.1-dev.
No validation prompt was used during training.
None
Validation settings
- CFG:
2.5
- CFG Rescale:
0.0
- Steps:
20
- Sampler:
FlowMatchEulerDiscreteScheduler
- Seed:
42
- 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:
The text encoder was not trained. You may reuse the base model text encoder for inference.
Training settings
- Training epochs: 7
- Training steps: 2250
- Learning rate: 0.0002
- Learning rate schedule: constant
- Warmup steps: 100
- Max grad norm: 0.1
- Effective batch size: 3
- Micro-batch size: 3
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Gradient checkpointing: True
- Prediction type: flow-matching (extra parameters=['shift=3.0', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flow_matching_loss=compatible'])
- Optimizer: adamw_bf16
- Trainable parameter precision: Pure BF16
- Caption dropout probability: 10.0%
LyCORIS Config:
{
"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
michelangelo-512
- Repeats: 11
- Total number of images: 13
- Total number of aspect buckets: 7
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
michelangelo-768
- Repeats: 8
- Total number of images: 13
- Total number of aspect buckets: 8
- Resolution: 0.589824 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
michelangelo-1024
- Repeats: 5
- Total number of images: 13
- Total number of aspect buckets: 10
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
michelangelo-1536
- Repeats: 2
- Total number of images: 13
- Total number of aspect buckets: 10
- Resolution: 2.359296 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
michelangelo-crops-512
- Repeats: 8
- Total number of images: 13
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
- Used for regularisation data: No
michelangelo-1024-crop
- Repeats: 5
- Total number of images: 13
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
- Used for regularisation data: No
Inference
import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights
def download_adapter(repo_id: str):
import os
from huggingface_hub import hf_hub_download
adapter_filename = "pytorch_lora_weights.safetensors"
cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models'))
cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_")
path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path)
path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename)
os.makedirs(path_to_adapter, exist_ok=True)
hf_hub_download(
repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter
)
return path_to_adapter_file
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_repo_id = 'mipat12/michelangelo-phase2-2e-4-ss3.0'
adapter_filename = 'pytorch_lora_weights.safetensors'
adapter_file_path = download_adapter(repo_id=adapter_repo_id)
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
wrapper.merge_to()
prompt = "An astronaut is riding a horse through the jungles of Thailand."
## 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=1024,
height=1024,
guidance_scale=2.5,
).images[0]
image.save("output.png", format="PNG")