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loona from helluva boss is eating a donut3.50.015None4210241{
2 "algo": "lokr",
3 "multiplier": 1.0,
4 "linear_dim": 1000000,
5 "linear_alpha": 1,
6 "factor": 10,
7 "full_matrix": true,
8 "apply_preset": {
9 "target_module": [
10 "FluxTransformerBlock",
11 "FluxSingleTransformerBlock"
12 ],
13 "name_algo_map": {
14 "transformer_blocks.[0-7]*": {
15 "algo": "lokr",
16 "factor": 4,
17 "linear_dim": 1000000,
18 "linear_alpha": 1,
19 "full_matrix": true
20 },
21 "transformer_blocks.[8-15]*": {
22 "algo": "lokr",
23 "factor": 6,
24 "linear_dim": 1000000,
25 "linear_alpha": 1,
26 "full_matrix": true
27 },
28 "transformer_blocks.[16-18]*": {
29 "algo": "lokr",
30 "factor": 12,
31 "linear_dim": 1000000,
32 "linear_alpha": 1,
33 "full_matrix": true
34 },
35 "single_transformer_blocks.[0-15]*": {
36 "algo": "lokr",
37 "factor": 8,
38 "linear_dim": 1000000,
39 "linear_alpha": 1,
40 "full_matrix": true
41 },
42 "single_transformer_blocks.[16-23]*": {
43 "algo": "lokr",
44 "factor": 6,
45 "linear_dim": 1000000,
46 "linear_alpha": 1,
47 "full_matrix": true
48 },
49 "single_transformer_blocks.[24-37]*": {
50 "algo": "lokr",
51 "factor": 4,
52 "linear_dim": 1000000,
53 "linear_alpha": 1,
54 "full_matrix": true
55 }
56 },
57 "use_fnmatch": true
58 }
59}1import torch
2from diffusers import DiffusionPipeline
3from lycoris import create_lycoris_from_weights
4
5model_id = 'black-forest-labs/FLUX.1-dev'
6adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually
7lora_scale = 1.0
8wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer)
9wrapper.merge_to()
10
11prompt = "loona from helluva boss is eating a donut"
12
13pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
14image = pipeline(
15 prompt=prompt,
16 num_inference_steps=15,
17 generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
18 width=1024,
19 height=1024,
20 guidance_scale=3.5,
21).images[0]
22image.save("output.png", format="PNG")