Text-to-Image
Diffusers
TensorBoard
Safetensors
LDMTextToImagePipeline
dreambooth
diffusers-training
latent-diffusion
latent-diffusion-diffusers
Instructions to use DaichiT/scrap_metal_model_ldm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DaichiT/scrap_metal_model_ldm with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("DaichiT/scrap_metal_model_ldm", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of sks scrap metal" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
DreamBooth - DaichiT/scrap_metal_model_ldm
This is a dreambooth model derived from CompVis/ldm-text2im-large-256. The weights were trained on a photo of sks scrap metal using DreamBooth. You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
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 DaichiT/scrap_metal_model_ldm
Base model
CompVis/ldm-text2im-large-256