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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']:

ArtOtherBasismodellstabilityai/stable-diffusion-2VersionBBewertung

Modellquelle

Art
Other
Dateigröße
988 B–3,23 GB
Basismodell
stabilityai/stable-diffusion-2
Version
vRevision b535703
Aktualisiert
7. Okt. 2026
Downloads
7
Quelle
Hugging Face

Quellenbeschreibung

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]

Quellen

1 Quelle
Hugging FacePrimärVersion b535703bf735691f874480ac18dbad7226df7c9a
huggingface.co/mhbkb/stable-diffusion-base-2.0-text-to-image-1/tree/b535703bf735691f874480ac18dbad7226df7c9a

Verifiziert 7. Okt.

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Modellartefakte

5 Artefakte

checkpoint-500/scaler.pt

pt · 988 B · SHA-256 18b984273ea2…2636 · Hugging Face

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checkpoint-500/unet/diffusion_pytorch_model.safetensors

safetensors · 3,23 GB · SHA-256 083f3c2bffdc…8b34 · Hugging Face

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text_encoder/model.safetensors

safetensors · 649 MB · SHA-256 bc1827c46545…e13f · Hugging Face

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unet/diffusion_pytorch_model.safetensors

safetensors · 3,23 GB · SHA-256 4b7b6093d594…ce18 · Hugging Face

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vae/diffusion_pytorch_model.safetensors

safetensors · 160 MB · SHA-256 3e4c08995484…af9a · Hugging Face

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Quellenauszüge

3 Auszüge

--- 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']: ![val_imgs_grid](./val_imgs_grid.png) ## 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