Downloads · 30 days
19
2% of all-time downloads
tiny_lora_mxtun3_style
segmind/tiny_lora_mxtun3_style
tiny_lora_mxtun3_style is a text-to-image model from segmind. Use it when you need an image from a text prompt. It is set up for diffusers. The card lists the license as creativeml-openrail-m.
This pipeline was distilled from SG161222/RealisticVisionV4.0 on a Subset of recastai/LAION-art-EN-improved-captions dataset. Below are some example images generated with the finetuned pipeline using the following pro…
All-time downloads
791
Public
Repo size
50.6 MB
Likes
2
Public
Repo makeup
Click a slice to open those files.
.safetensors50.6 MB · 100%
At a glance
- Task
- Text-to-Image
- Library
- diffusers
- License
- creativeml-openrail-m
- Access
- Public
- Created
- Jul 31, 2023
- Updated
- Jul 31, 2023
- SHA
- 4f40f031
Base models
Model card
From the Hugging Face model README
Text-to-image Distillation
This pipeline was distilled from SG161222/Realistic_Vision_V4.0 on a Subset of recastai/LAION-art-EN-improved-captions dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Portrait of a pretty girl']:
This Pipeline is based upon the paper. Training Code can be found here.
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("segmind/tiny-sd", torch_dtype=torch.float16)
#Load LoRA finetune
pipeline.load_lora_weights("segmind/tiny_lora_mxtun3_style", weight_name="sd15_tiny_mxtun3_style_lora.safetensors")
prompt = "Portrait of a pretty girl"
image = pipeline(prompt).images[0]
image.save("my_image.png")
Training info
These are the key hyperparameters used during training:
- Steps: 125000
- Learning rate: 1e-4
- Batch size: 32
- Gradient accumulation steps: 4
- Image resolution: 512
- Mixed-precision: fp16
- Task
- Text-to-Image
- Library
- diffusers
- License
- creativeml-openrail-m
- Created
- Jul 31, 2023
- Updated
- Jul 31, 2023