fine_tuned_model

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Text-to-image finetuning - Trkkk/fine_tuned_model

This pipeline was finetuned from CompVis/stable-diffusion-v1-4 on the Trkkk/txt_zu_img dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A busy urban street filled with cars stuck in traffic. Vehicles of various types, including sedans, SUVs, and buses, are lined up bumper to bumper. The road is crowded with vehicles, and drivers seem impatient. Streetlights, traffic signs, and nearby buildings add to the busy city atmosphere, while pedestrians wait on the sidewalks. The scene is set during daylight, with clear skies above, but the road is completely congested with no cars moving.']:

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("Trkkk/fine_tuned_model", torch_dtype=torch.float16)
prompt = "A busy urban street filled with cars stuck in traffic. Vehicles of various types, including sedans, SUVs, and buses, are lined up bumper to bumper. The road is crowded with vehicles, and drivers seem impatient. Streetlights, traffic signs, and nearby buildings add to the busy city atmosphere, while pedestrians wait on the sidewalks. The scene is set during daylight, with clear skies above, but the road is completely congested with no cars moving."
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

• Epochs: 1
• Learning rate: 1e-05
• Batch size: 1
• Gradient accumulation steps: 4
• Image resolution: 256
• Mixed-precision: fp16

More information on all the CLI arguments and the environment are available on your wandb run page.

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]