Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use NadaGh/working with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use NadaGh/working with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("NadaGh/working", dtype=torch.bfloat16, device_map="cuda") prompt = "tst chair" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download diffusers/docs/source/en/api/models/cogvideox_transformer3d.md from NadaGh/working: direct link, hf CLI and curl.
- Browser
- Download file 1.33 kB
-
https://huggingface.co/NadaGh/working/resolve/main/diffusers/docs/source/en/api/models/cogvideox_transformer3d.md
- Command line
-
hf download hf://NadaGh/working/diffusers/docs/source/en/api/models/cogvideox_transformer3d.md
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curl -L -o cogvideox_transformer3d.md https://huggingface.co/NadaGh/working/resolve/main/diffusers/docs/source/en/api/models/cogvideox_transformer3d.md
1.33 kB
CogVideoXTransformer3DModel
A Diffusion Transformer model for 3D data from CogVideoX was introduced in CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer by Tsinghua University & ZhipuAI.
The model can be loaded with the following code snippet.
from diffusers import CogVideoXTransformer3DModel
vae = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX-2b", subfolder="transformer", torch_dtype=torch.float16).to("cuda")
CogVideoXTransformer3DModel
[[autodoc]] CogVideoXTransformer3DModel
Transformer2DModelOutput
[[autodoc]] models.modeling_outputs.Transformer2DModelOutput