File size: 1,461 Bytes
5117726
 
 
 
 
 
 
 
 
 
 
fc4ee82
 
 
 
 
 
5117726
e5bb728
 
5117726
 
 
 
 
 
 
d87e27b
5117726
22563e1
efc0742
5117726
 
 
 
 
730b6c5
5117726
730b6c5
e5bb728
 
 
 
5117726
e5bb728
 
5117726
dc23a96
5117726
 
 
e5bb728
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
---
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
inference: true
datasets:
- logo-wizard/modern-logo-dataset
---

<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->


# LoRA text2image fine-tuning - SedatAl/test-test
These are LoRA adaption weights for stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were fine-tuned on the logo-wizard/modern-logo-dataset dataset.


![img_1](./7c970728-3193-4d89-8461-514bae4007af.jpeg)
![img_2](./image_2.png)
![img_3](./image_3.png)



## How to Use

```python
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
pipe.load_lora_weights("SedatAl/test-test")

prompt = "a logo of electronic online shop, gradient image of a rectangular shopping bag with a cursor inside, white background, red and magenta gradient foreground, minimalism, modern"
image = pipe(prompt).images[0]

```

## Training details

  --train_batch_size=10 \
  --max_train_steps=200 \
  --learning_rate=1e-04 \

 Remaining parameters are default.