Stable Diffusion Base 2.0 Text To Image 1
This pipeline was finetuned from stabilityai/stable-diffusion-2 on the None dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['a photo of a dog']:
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 988 B–3,23 GB
- Modelo base
- stabilityai/stable-diffusion-2
- Versión
- vRevision b535703
- Actualizado
- 7 oct 2026
- Descargas
- 7
- Fuente
- Hugging Face
Descripción de la fuente
Text-to-image finetuning - mhbkb/stable-diffusion-base-2.0-text-to-image-1
This pipeline was finetuned from stabilityai/stable-diffusion-2 on the None dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['a photo of a dog']:
val_imgs_grid
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("mhbkb/stable-diffusion-base-2.0-text-to-image-1", torch_dtype=torch.float16)
prompt = "a photo of a dog"
image = pipeline(prompt).images[0]
image.save("my_image.png")
Training info
These are the key hyperparameters used during training:
- Epochs: 20
- Learning rate: 1e-05
- Batch size: 1
- Gradient accumulation steps: 8
- Image resolution: 768
- 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]
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 7 oct
Download files
Artefactos del modelo
5 artefactos
Artefactos del modelo
5 artefactoscheckpoint-500/unet/diffusion_pytorch_model.safetensors
safetensors · 3,23 GB · SHA-256 083f3c2bffdc…8b34 · Hugging Face
Download filetext_encoder/model.safetensors
safetensors · 649 MB · SHA-256 bc1827c46545…e13f · Hugging Face
Download fileunet/diffusion_pytorch_model.safetensors
safetensors · 3,23 GB · SHA-256 4b7b6093d594…ce18 · Hugging Face
Download filevae/diffusion_pytorch_model.safetensors
safetensors · 160 MB · SHA-256 3e4c08995484…af9a · Hugging Face
Download fileExtractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- base_model: stabilityai/stable-diffusion-2 library_name: diffusers license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training 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 - mhbkb/stable-diffusion-base-2.0-text-to-image-1 This pipeline was finetuned from **stabilityai/stable-diffusion-2** on the **None** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['a photo of a dog']:  ## Pipeline usage You can use the pipeline like so: ```python from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained("mhbkb/stable-diffusion-base-2.0-text-to-image-1", torch_dtype=torch.float16) prompt = "a photo of a dog" image = pipeline(prompt).images[0] image.save("my_image.png") ``` ## Training info These are the key hyperparameters used during training: * Epochs: 20 * Learning rate: 1e-05 * Batch size: 1 * Gradient accumulation steps: 8 * Image resolution: 768 * Mixed-precision: fp16 More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/javabkb-university-of-arizona/text2image-fine-tune/runs/qzljwzgz). ## 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]
Source context: 7 downloads · 0 likes · Pipeline text-to-image · Library diffusers · Repo mhbkb/stable-diffusion-base-2.0-text-to-image-1
Source context: 0 downloads · 0 likes · Pipeline text-to-image · Library diffusers · Repo mhbkb/stable-diffusion-base-2.0-text-to-image-1
