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ossaili/immos_flux
immos_flux 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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.safetensors110 MB · 95%
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:
Architectural sketch emphasizing large cuboid structures with red framing accents. The perspective view captures suspended white cubic volumes with red interior cutouts, connected by red steel frameworks. Three human figures in silhouette walk toward the foreground, where landscaping elements like bushes and pathways are visible. The sketch employs a mixture of precise line work and shading, highlighting texture and materiality.
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": 10000,
"linear_alpha": 1,
"factor": 16,
"apply_preset": {
"target_module": [
"Attention",
"FeedForward"
],
"module_algo_map": {
"Attention": {
"factor": 16
},
"FeedForward": {
"factor": 8
}
}
}
}
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 = "Architectural sketch emphasizing large cuboid structures with red framing accents. The perspective view captures suspended white cubic volumes with red interior cutouts, connected by red steel frameworks. Three human figures in silhouette walk toward the foreground, where landscaping elements like bushes and pathways are visible. The sketch employs a mixture of precise line work and shading, highlighting texture and materiality."
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")