Text-to-Image
Cosmos
Diffusers
Safetensors
cosmos3_omni
nvidia
cosmos3
vllm-omni
sglang
sglang-diffusion
image-generation
Instructions to use nvidia/Cosmos3-Super-Text2Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Cosmos
How to use nvidia/Cosmos3-Super-Text2Image with Cosmos:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Diffusers
How to use nvidia/Cosmos3-Super-Text2Image with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nvidia/Cosmos3-Super-Text2Image", 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
Download agentic_upsampling/__init__.py from nvidia/Cosmos3-Super-Text2Image: direct link, hf CLI and curl.
- Browser
- Download file 261 Bytes
-
https://huggingface.co/nvidia/Cosmos3-Super-Text2Image/resolve/fp8/agentic_upsampling/__init__.py
- Command line
-
hf download hf://nvidia/Cosmos3-Super-Text2Image@fp8/agentic_upsampling/__init__.py
-
curl -L -o __init__.py https://huggingface.co/nvidia/Cosmos3-Super-Text2Image/resolve/fp8/agentic_upsampling/__init__.py
261 Bytes
| """Standalone agentic prompt upsampling for Cosmos3 text-to-image.""" | |
| from agentic_upsampling.data import PromptItem | |
| from agentic_upsampling.runner import AgenticUpsamplerRunner, RunnerConfig | |
| __all__ = ["AgenticUpsamplerRunner", "PromptItem", "RunnerConfig"] | |