Instructions to use Veda-Labs/Amazing-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Veda-Labs/Amazing-2.0 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Veda-Labs/Amazing-2.0", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Diffusion Single File
How to use Veda-Labs/Amazing-2.0 with Diffusion Single File:
# 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
- Notebooks
- Google Colab
- Kaggle
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Veda-Labs/Amazing-2.0", dtype=torch.bfloat16, device_map="cuda")
prompt = "Turn this cat into a dog"
input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
image = pipe(image=input_image, prompt=prompt).images[0]You need to agree to share your contact information to access this model
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Vedika Amazing 2.0 is a state-of-the-art text-to-image generation model capable of generating high-quality images based on text instructions.
For more information, please visit our website.
Key Features
- State of the art in open text-to-image generation.
- Efficient architecture optimized for consumer GPUs.
- Open weights to drive new scientific research and empower artists to develop innovative workflows.
- Generated outputs can be used for personal, scientific, and commercial purposes, as described in the Veda Labs License.
Usage
We provide a reference implementation of Vedika Amazing 2.0 in Diffusers.
Using with diffusers 🧨
For local deployment on a consumer type graphics card, like an RTX 4090 or an RTX 5090, please see the documentation on our website.
import torch
from diffusers import VedikaAmazing2Pipeline
repo_id = "Veda-Labs/Amazing-2.0"
device = "cuda:0"
torch_dtype = torch.bfloat16
pipe = VedikaAmazing2Pipeline.from_pretrained(
repo_id, torch_dtype=torch_dtype
).to(device)
prompt = "A beautiful landscape with mountains and a lake at sunset, highly detailed, 8k resolution"
image = pipe(
prompt=prompt,
generator=torch.Generator(device=device).manual_seed(42),
num_inference_steps=50,
guidance_scale=4,
).images[0]
image.save("vedika_amazing_2_output.png")
Risks
Veda Labs is committed to the responsible development and deployment of our models. Prior to releasing Vedika Amazing 2.0, we evaluated and mitigated various risks to prevent misuse.
License
This model falls under the Veda Labs License.
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