metadata
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
Super Detail, A close-up shot of a man with a brown hat on his head. His
eyes are blue and he has brown hair. His hair is wet from the rain. The
background is blurred.
output:
url: images/SR1.png
- text: >-
Super Detail, A close-up shot of a womans face, taken from a low-angle
perspective. The womans eyes are a piercing of turquoise, and her hair is
a vibrant shade of brown. Her lips are painted a deep red, with a slight
smile. Her eyebrows are a light brown, adding a touch of texture to her
face. The background is dark, creating a stark contrast to the womans
skin.
output:
url: images/SR3.png
- text: >-
Super Detail, a close-up shot of a womans head and shoulders is seen
against a vibrant red backdrop. The womans face is adorned with a white
face, adorned with blue eyes, and her brown hair cascades over her
shoulders. She is wearing a red turtleneck, with a ribbed collar. Her lips
are painted a vibrant shade of red, adding a pop of color to her face. Her
eyebrows are a darker shade of blue, adding depth to the composition.
output:
url: images/SR4.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Super Detail
license: creativeml-openrail-m
Flux-Super-Detail-LoRA
The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.
prithivMLmods/Flux-Super-Detail-LoRA
Image Processing Parameters
Parameter | Value | Parameter | Value |
---|---|---|---|
LR Scheduler | constant | Noise Offset | 0.03 |
Optimizer | AdamW | Multires Noise Discount | 0.1 |
Network Dim | 64 | Multires Noise Iterations | 10 |
Network Alpha | 32 | Repeat & Steps | 15 & 2470 |
Epoch | 10 | Save Every N Epochs | 1 |
Labeling: florence2-en(natural language & English)
Total Images Used for Training : 15
Best Dimensions
- 768 x 1024 (Best)
- 1024 x 1024 (Default)
Setting Up
import torch
from pipelines import DiffusionPipeline
base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
lora_repo = "prithivMLmods/Flux-Super-Detail-LoRA"
trigger_word = "Super Detail"
pipe.load_lora_weights(lora_repo)
device = torch.device("cuda")
pipe.to(device)
Trigger words
You should use Super Detail
to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.