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ossaili/arch_sktechs_flux_lora_v1
arch_sktechs_flux_lora_v1 is a text-to-image model from ossaili. Use it when you need an image from a text prompt. It is set up for diffusers. The card lists the license as other.
This is a LyCORIS adapter derived from black-forest-labs/FLUX.1-dev.
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.safetensors673 MB · 99%
From the Hugging Face model README
This is a LyCORIS adapter derived from black-forest-labs/FLUX.1-dev.
The main validation prompt used during training was:
an architectural sketch of a modern architecture, concrete, two stories, gray, windows, urban landscape, cultural building, side view, geometric shape, clean lines, flat roof, minimalistic design, public space, large mass, dominant volume, no visible vegetation, straight edges, sleek facade
3.00.020None421024x1024Note: The validation settings are not necessarily the same as the 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.
{
"algo": "lokr",
"multiplier": 1.0,
"linear_dim": 15000,
"linear_alpha": 2,
"factor": 4,
"apply_preset": {
"target_module": [
"Attention",
"FeedForward"
],
"module_algo_map": {
"Attention": {
"factor": 4
},
"FeedForward": {
"factor": 4
}
}
}
}
import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer)
wrapper.merge_to()
prompt = "an architectural sketch of a modern architecture, concrete, two stories, gray, windows, urban landscape, cultural building, side view, geometric shape, clean lines, flat roof, minimalistic design, public space, large mass, dominant volume, no visible vegetation, straight edges, sleek facade"
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
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(1641421826),
width=1024,
height=1024,
guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")