Text-to-image finetuning - haorandai/Nov_Clean_Banana_Orange_5samples_with5constraints
This pipeline was finetuned from CompVis/stable-diffusion-v1-4 on the haorandai/Nov_Clean_Banana_Orange_5samples_with5constraints dataset. Below are some example images generated with the finetuned pipeline using the following prompts: None:
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("haorandai/Nov_Clean_Banana_Orange_5samples_with5constraints", torch_dtype=torch.float16)
prompt = "None"
image = pipeline(prompt).images[0]
image.save("my_image.png")
Training info
These are the key hyperparameters used during training:
- Epochs: 167
- Learning rate: 1e-05
- Batch size: 1
- Gradient accumulation steps: 4
- Image resolution: 224
- 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]
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Model tree for haorandai/Nov_Clean_Banana_Orange_5samples_with5constraints
Base model
CompVis/stable-diffusion-v1-4