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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: diffusers |
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pipeline_tag: text-to-image |
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tags: |
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- text-to-image |
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- image-generation |
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- shuttle |
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widget: |
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- text: >- |
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Venus floating market at dawn, fantasy digital art, highly detailed, atmospheric lighting with film-like light leaks, impressive background, studio photo style, cinematic, intricate details. |
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output: |
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url: gallery/1.webp |
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- text: >- |
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Silent forest, sun barely piercing treetops, mysterious lake turns dark red at dawn, reflecting colorful sky. Lone tree on shore with diamond-like dewdrops, photorealistic. |
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output: |
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url: gallery/2.webp |
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- text: >- |
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A beautiful photo showcases a night waterfall in the jungle, illuminated with a subtle blue tint that adds an ethereal touch. Fireflies float delicately around, their gentle glow enhancing the magical ambiance of the scene. |
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output: |
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url: gallery/3.webp |
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instance_prompt: null |
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--- |
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# Shuttle 3 Diffusion |
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Join our [Discord](https://discord.gg/shuttleai) to get the latest updates, news, and more. |
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<Gallery /> |
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## Model Variants |
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These model variants provide different precision levels and formats optimized for diverse hardware capabilities and use cases |
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- [bfloat16](https://huggingface.co/shuttleai/shuttle-3-diffusion) |
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- [GGUF](https://huggingface.co/shuttleai/shuttle-3-diffusion-GGUF) |
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- [fp8](https://huggingface.co/shuttleai/shuttle-3-diffusion-fp8) |
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Shuttle 3 Diffusion is a text-to-image AI model designed to create detailed and diverse images from textual prompts in just 4 steps. It offers enhanced performance in image quality, typography, understanding complex prompts, and resource efficiency. |
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You can try out the model through a website at https://chat.shuttleai.com/images |
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## Using the model via API |
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You can use Shuttle 3 Diffusion via API through ShuttleAI |
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- [ShuttleAI](https://shuttleai.com/) |
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- [ShuttleAI Docs](https://docs.shuttleai.com/) |
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## Using the model with 🧨 Diffusers |
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Install or upgrade diffusers |
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```shell |
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pip install -U diffusers |
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``` |
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Then you can use `DiffusionPipeline` to run the model |
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```python |
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import torch |
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from diffusers import DiffusionPipeline |
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# Load the diffusion pipeline from a pretrained model, using bfloat16 for tensor types. |
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pipe = DiffusionPipeline.from_pretrained( |
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"shuttleai/shuttle-3-diffusion", torch_dtype=torch.bfloat16 |
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).to("cuda") |
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# Uncomment the following line to save VRAM by offloading the model to CPU if needed. |
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# pipe.enable_model_cpu_offload() |
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# Uncomment the lines below to enable torch.compile for potential performance boosts on compatible GPUs. |
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# Note that this can increase loading times considerably. |
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# pipe.transformer.to(memory_format=torch.channels_last) |
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# pipe.transformer = torch.compile( |
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# pipe.transformer, mode="max-autotune", fullgraph=True |
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# ) |
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# Set your prompt for image generation. |
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prompt = "A cat holding a sign that says hello world" |
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# Generate the image using the diffusion pipeline. |
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image = pipe( |
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prompt, |
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height=1024, |
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width=1024, |
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guidance_scale=3.5, |
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num_inference_steps=4, |
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max_sequence_length=256, |
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# Uncomment the line below to use a manual seed for reproducible results. |
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# generator=torch.Generator("cpu").manual_seed(0) |
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).images[0] |
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# Save the generated image. |
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image.save("shuttle.png") |
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``` |
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To learn more check out the [diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux) documentation |
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## Using the model with ComfyUI |
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To run local inference with Shuttle 3 Diffusion using [ComfyUI](https://github.com/comfyanonymous/ComfyUI), you can use this [safetensors file](https://huggingface.co/shuttleai/shuttle-3-diffusion/blob/main/shuttle-3-diffusion.safetensors). |
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## Comparison to other models |
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Shuttle 3 Diffusion can produce images better images than Flux Dev in just four steps, while being licensed under Apache 2. |
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[More examples](https://docs.shuttleai.com/getting-started/shuttle-diffusion) |
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## Training Details |
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Shuttle 3 Diffusion uses Flux.1 Schnell as its base. It can produce images similar to Flux Dev or Pro in just 4 steps, and it is licensed under Apache 2. The model was partially de-distilled during training. When used beyond 10 steps, it enters "refiner mode," enhancing image details without altering the composition. We overcame the limitations of the Schnell-series models by employing a special training method, resulting in improved details and colors. |
