Image-to-Image
Diffusers
Safetensors
Diffusion Single File
English
VedikaAmazing2Pipeline
image-generation
image-editing
vedika-amazing-2
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
| language: | |
| - en | |
| license: other | |
| license_name: veda-labs-license | |
| extra_gated_prompt: >- | |
| By clicking "Agree", you agree to the [Veda Labs License | |
| Agreement](./LICENSE.md) | |
| and acknowledge the [Acceptable Use | |
| Policy](https://vedalabs.online/usage-policy). | |
| tags: | |
| - image-generation | |
| - image-editing | |
| - vedika-amazing-2 | |
| - diffusion-single-file | |
| pipeline_tag: image-to-image | |
| library_name: diffusers | |
|  | |
| `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](https://vedalabs.online). | |
| # Key Features | |
| 1. State of the art in open text-to-image generation. | |
| 2. Efficient architecture optimized for consumer GPUs. | |
| 3. Open weights to drive new scientific research and empower artists to develop innovative workflows. | |
| 4. Generated outputs can be used for personal, scientific, and commercial purposes, as described in the [Veda Labs License](./LICENSE.md). | |
| # Usage | |
| We provide a reference implementation of `Vedika Amazing 2.0` in [Diffusers](https://github.com/huggingface/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. | |
| ```python | |
| 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](./LICENSE.md). |