Text-to-image finetuning - Sajid121/OUtput_result

This pipeline was finetuned from CompVis/stable-diffusion-v1-4 on the Sajid121/Bevgen2 dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ["['A topdown bird eye view of a car on a road with lanes and pedestrain on sides']"]:

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("Sajid121/OUtput_result", torch_dtype=torch.float16)
prompt = "['A topdown bird eye view of a car on a road with lanes and pedestrain on sides']"
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

• Epochs: 125
• Learning rate: 1e-05
• Batch size: 1
• Gradient accumulation steps: 4
• Image resolution: 512
• Mixed-precision: fp16

Intended uses & limitations

How to use

# 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]