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stable-diffusion_unclip-small-v21-th-800-e4
arpachat/stable-diffusion_unclip-small-v21-th-800-e4
stable-diffusion_unclip-small-v21-th-800-e4 is a text-to-image model from arpachat. 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 finetuned from OFA-Sys/small-stable-diffusion-v0 on the jwl25b/finalprojectdataset dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ["Tommy Hi…
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At a glance
- Task
- Text-to-Image
- Library
- diffusers
- License
- creativeml-openrail-m
- Access
- Public
- Created
- Dec 16, 2023
- Updated
- Dec 16, 2023
- SHA
- 4b3bdba7
Base models
Datasets
Model card
From the Hugging Face model README
Text-to-image finetuning - arpachat/stable-diffusion_unclip-small-v21-th-800-e4
This pipeline was finetuned from OFA-Sys/small-stable-diffusion-v0 on the jwl25b/final_project_dataset dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ["Tommy Hilfiger men's Regular Fit Round Logo Grey Polo"]:
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("arpachat/stable-diffusion_unclip-small-v21-th-800-e4", torch_dtype=torch.float16)
prompt = "Tommy Hilfiger men's Regular Fit Round Logo Grey Polo"
image = pipeline(prompt).images[0]
image.save("my_image.png")
Training info
These are the key hyperparameters used during training:
- Epochs: 400
- Learning rate: 0.0001
- Batch size: 8
- Gradient accumulation steps: 4
- Image resolution: 128
- Mixed-precision: fp16
More information on all the CLI arguments and the environment are available on your wandb run page.
- Task
- Text-to-Image
- Library
- diffusers
- License
- creativeml-openrail-m
- Created
- Dec 16, 2023
- Updated
- Dec 16, 2023