File size: 1,826 Bytes
e017767
 
 
 
 
 
 
 
 
 
 
 
bd9b480
 
532a097
 
 
 
13c3a21
 
 
 
 
 
 
ed2115f
 
 
 
 
2e989cd
 
 
 
 
4a8d63c
 
 
532a097
e017767
 
 
 
c1136c6
e017767
bd9b480
e017767
bd9b480
e017767
64e4bd6
 
 
3426d06
64e4bd6
b228bf0
 
 
 
 
 
 
 
 
 
 
 
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
57
58
59
60
61
62
63
64
65
66
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: creativeml-openrail-m
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
inference: true
datasets:
- Aedancodes/monet_dataset
widget:
- text: monet,  a landscape of a snowy mountain region big clouds
  output:
    url: images/example_r809dxj86.png
- text: >-
    monet, majestic cliffs overlooking a serene ocean, with dramatic rock
    formations bathed in soft light. The cliffs are painted in shades of green,
    ochre, and brown, contrasting with the smooth, flowing waves below,
    capturing the raw, natural beauty of the landscape
  output:
    url: images/example_xm434dzxp.png
- text: >-
    monet,  a landscape of a snowy mountain region big clouds, street lamps with
    warm yellow colrs, night
  output:
    url: images/example_c6adjuo36.png
- text: >-
    monet,  mountains far back, street lamps shines with warm yellow colors,
    black night
  output:
    url: images/example_dzoc9trs1.png
- text: Monet lakeside at sunset
  output:
    url: images/example_4hq20p721.png

---

<!-- 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. -->
<Gallery />

# LoRA text2image fine-tuning 

These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the Aedancodes/monet_dataset dataset.

## Trigger words

> [!WARNING]
> **Trigger words:**  You should use `Monet` to trigger the image generation.

## Training details

```python
resolution=1024*1024
train batch_size = 1
max train steps = 200
learning rate = 1e-4
lr scheduler = constant
mixed precision = fp16
8bit_adam

```