Create README.md

README.md

@@ -0,0 +1,87 @@
+---
+language:
+- en
+license: apache-2.0
+library_name: diffusers
+pipeline_tag: text-to-image
+tags:
+- text-to-image
+- image-generation
+- shuttle
+---
+
+# Shuttle 3 Diffusion
+
+## Model Variants
+These model variants provide different precision levels and formats optimized for diverse hardware capabilities and use cases
+- [bfloat16](https://huggingface.co/shuttleai/shuttle-3-diffusion)
+- [GGUF](https://huggingface.co/shuttleai/shuttle-3-diffusion-GGUF)
+- [fp8](https://huggingface.co/shuttleai/shuttle-3-diffusion-fp8)
+
+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.
+
+![image/png](https://huggingface.co/shuttleai/shuttle-3-diffusion/resolve/main/demo.png)
+
+You can try out the model through a website at https://chat.shuttleai.com/images
+
+## Using the model via API
+You can use Shuttle 3 Diffusion via API through ShuttleAI
+- [ShuttleAI](https://shuttleai.com/)
+- [ShuttleAI Docs](https://docs.shuttleai.com/)
+
+## Using the model with 🧨 Diffusers
+Install or upgrade diffusers
+```shell
+pip install -U diffusers
+```
+Then you can use `DiffusionPipeline` to run the model
+```python
+import torch
+from diffusers import DiffusionPipeline
+
+# Load the diffusion pipeline from a pretrained model, using bfloat16 for tensor types.
+pipe = DiffusionPipeline.from_pretrained(
+    "shuttleai/shuttle-3-diffusion", torch_dtype=torch.bfloat16
+).to("cuda")
+
+# Uncomment the following line to save VRAM by offloading the model to CPU if needed.
+# pipe.enable_model_cpu_offload()
+
+# Uncomment the lines below to enable torch.compile for potential performance boosts on compatible GPUs.
+# Note that this can increase loading times considerably.
+# pipe.transformer.to(memory_format=torch.channels_last)
+# pipe.transformer = torch.compile(
+#     pipe.transformer, mode="max-autotune", fullgraph=True
+# )
+
+# Set your prompt for image generation.
+prompt = "A cat holding a sign that says hello world"
+
+# Generate the image using the diffusion pipeline.
+image = pipe(
+    prompt,
+    height=1024,
+    width=1024,
+    guidance_scale=3.5,
+    num_inference_steps=4,
+    max_sequence_length=256,
+    # Uncomment the line below to use a manual seed for reproducible results.
+    # generator=torch.Generator("cpu").manual_seed(0)
+).images[0]
+
+# Save the generated image.
+image.save("shuttle.png")
+```
+To learn more check out the [diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux) documentation
+
+## Using the model with ComfyUI
+
+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).
+
+## Comparison to other models
+Shuttle 3 Diffusion can produce images better images than Flux Dev in just four steps, while being licensed under Apache 2.
+![image/png](https://huggingface.co/shuttleai/shuttle-3-diffusion/resolve/main/comparison.png)
+[More examples](https://docs.shuttleai.com/getting-started/shuttle-diffusion)
+
+## Training Details
+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.