| | --- |
| | license: apache-2.0 |
| | pipeline_tag: text-to-image |
| | tags: |
| | - text-to-image |
| | - image-generation |
| | - yandex |
| | --- |
| | Alice AI ART dev |
| | --- |
| | by Yandex |
| |
|
| |  |
| |
|
| | Alice AI ART dev is 4.8B parameter diffusion UNet model capable of generating images from text prompts. |
| |
|
| | Key features |
| | --- |
| |
|
| | * **Relevance** A considerable amount of work was done to improve text-to-image alignment. According to the Side-by-Side evaluation, our model is competitive with Qwen-Image, despite being significantly smaller (4.8B parameters vs 20B parameters). |
| | * **Aesthetics** Our model is capable of generating high-quality images with a wide range of styles and themes. |
| | * **Accessibility** Alice AI ART dev is runnable on consumer-grade[^1] GPUs (for instance, NVIDIA RTX 3090) making it accessible to a wider audience. |
| |
|
| | [^1] with weight offloading |
| |
|
| | Usage |
| | --- |
| | The image generation pipeline can be loaded a follows |
| | ```python |
| | pipe = YandexArtOSPipeline.from_pretrained( |
| | "yandex_art_os", |
| | cpu_offload=True |
| | ) |
| | ``` |
| | For memory-constrained GPUs we recommend to turn on `cpu_offload` flag: |
| |
|
| | By default we use following sampling parameters: |
| | ```python |
| | { |
| | "num_inference_steps": 32, |
| | "cond_scale": 2.75, |
| | "unet_switch_timestep": 8, |
| | "karras_rho": 6.0, |
| | "method_name": "dpm-multistep", |
| | "sampler_kwargs": { |
| | "num_train_timesteps": 1000, |
| | "beta_start": 0.00001013, |
| | "beta_end": 0.019771934, |
| | "use_karras_sigmas": True, |
| | "algorithm_type": "sde-dpmsolver++" |
| | } |
| | } |
| | ``` |
| |
|
| |
|
