Text-to-Image
Diffusers
Safetensors
English
StableDiffusionPipeline
code
stable-diffusion
scheduler
text_encoder
tokenizer
unet
vae
Instructions to use samim2024/text-to-image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use samim2024/text-to-image with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("samim2024/text-to-image", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from samim2024/text-to-image: direct link, hf CLI and curl.
- Browser
- Download file 1.36 GB
-
https://huggingface.co/samim2024/text-to-image/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://samim2024/text-to-image/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/samim2024/text-to-image/resolve/main/text_encoder/pytorch_model.bin
1.36 GB
- Xet hash:
- 7861754ac8a7e1ca93aefaea211406dda3f884e51961aa1c2a0dad6947595ad2
- Size of remote file:
- 1.36 GB
- SHA256:
- 77c4f8f5bf2c5ed3a41c67b12fcec194d6a8220d5b73fe253fdde1a1df63d29b
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