|
--- |
|
license: creativeml-openrail-m |
|
library_name: diffusers |
|
tags: |
|
- stable-diffusion |
|
- stable-diffusion-diffusers |
|
- text-to-image |
|
- diffusers |
|
- diffusers-training |
|
- stable-diffusion |
|
- stable-diffusion-diffusers |
|
- text-to-image |
|
- diffusers |
|
- diffusers-training |
|
base_model: CompVis/stable-diffusion-v1-4 |
|
inference: true |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
|
|
# Text-to-image finetuning - MohamedAcadys/PointConImageModelV1-4 |
|
|
|
This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** on the **Acadys/PointConImagesV2** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ["Un patron en costume donne un dossier à un employé dans le style 'Edition point Con'"]: |
|
|
|
 |
|
|
|
|
|
## Pipeline usage |
|
|
|
You can use the pipeline like so: |
|
|
|
```python |
|
from diffusers import DiffusionPipeline |
|
import torch |
|
|
|
pipeline = DiffusionPipeline.from_pretrained("MohamedAcadys/PointConImageModelV1-4", torch_dtype=torch.float16) |
|
prompt = "Un patron en costume donne un dossier à un employé dans le style 'Edition point Con'" |
|
image = pipeline(prompt).images[0] |
|
image.save("my_image.png") |
|
``` |
|
|
|
## Training info |
|
|
|
These are the key hyperparameters used during training: |
|
|
|
* Epochs: 200 |
|
* Learning rate: 1e-05 |
|
* Batch size: 2 |
|
* Gradient accumulation steps: 4 |
|
* Image resolution: 512 |
|
* Mixed-precision: fp16 |
|
|
|
|
|
More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/acadys-sadadou/text2image-fine-tune/runs/hflztcbt). |
|
|
|
|
|
## Intended uses & limitations |
|
|
|
#### How to use |
|
|
|
```python |
|
# TODO: add an example code snippet for running this diffusion pipeline |
|
``` |
|
|
|
#### Limitations and bias |
|
|
|
[TODO: provide examples of latent issues and potential remediations] |
|
|
|
## Training details |
|
|
|
[TODO: describe the data used to train the model] |
