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--- |
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base_model: |
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- black-forest-labs/FLUX.1-schnell |
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tags: |
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- flux |
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- diffusers |
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pipeline_tag: text-to-image |
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--- |
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# Skittles v2 |
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## Model Summary |
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**Skittles v2** is a cutting-edge text-to-image generation model, created by merging components from the **FLUX.1 Schnell** architecture. By combining the precision of **FLUX.1 Schnell** with advanced tweaks, **Skittles v2** is designed to offer high-quality image outputs. |
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- **Type**: Text-to-Image Generation |
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- **Architecture**: Merged FLUX.1 Schnell with CFG capabilities |
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- **Output Quality**: Seems to be on par with **FLUX.1 Dev** |
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- **Performance**: Optimized for both image fidelity (speed is degraded (I'm looking into it)) |
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--- |
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## Features |
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- **CFG Integration**: Skittles v2 unlocks CFG (Classifier-Free Guidance) capabilities, offering fine-grained control over image generation. |
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- **High Fidelity**: Produces ultra-realistic and detailed images. |
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- **Customizable Output**: Supports a wide range of prompts, styles, and configurations. |
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--- |
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## Model Details |
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- **Base Model**: FLUX.1 Schnell |
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- **Merge Approach**: The model was combined using a custom merging strategy, blending FLUX.1 Schnell’s architecture with optimized CFG decoding. |
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- **Training Paradigm**: Not retrained, but restructured for improved inference performance. |
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- **Output Size**: Supports resolutions up to 1024x1024 pixels. |
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--- |
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## Intended Use |
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### Applications |
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- Generating ultra-realistic images for creative projects |
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- Creating concept art, visual prototypes, and artistic renderings |
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- Exploration of text-to-image synthesis for research or artistic purposes |
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### Examples |
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| Prompt | Image Description | |
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|-------|--------------------| |
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| "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" | Hyper-detailed astronaut surrounded by lush, muted jungle tones | |
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| "A futuristic cityscape at sunset, ultra-realistic, cinematic, 4K" | Vibrant, glowing cityscape with dynamic lighting effects | |
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| "A delicious ceviche cheesecake slice" | Highly detailed and realistic rendition of a culinary masterpiece | |
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--- |
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## How to Use |
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```python |
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from diffusers import DiffusionPipeline |
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# Load the model |
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pipe = DiffusionPipeline.from_pretrained("miike-ai/skittles-v2") |
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pipe.to("cuda") # Ensure CUDA is available |
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# Generate an image |
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prompt = "An ultra-realistic image of a futuristic cityscape." |
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image = pipe(prompt, guidance_scale=3.5, num_inference_steps=28).images[0] |
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# Save the result |
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image.save("generated_image.png") |
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``` |
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--- |
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## Limitations and Biases |
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- The model may produce biased or stereotypical outputs based on the provided text prompts. |
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- Outputs are deterministic but rely heavily on the prompt quality. Results may vary with ambiguous descriptions. |
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- The model is not trained to handle NSFW content or harmful prompts. |
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--- |
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## Acknowledgments |
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- Built on top of **FLUX.1 Schnell** by Black Forest Labs |
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- Contributions from **miike-ai** |
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- Integrated with Hugging Face Diffusers for seamless inference |
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--- |
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## Citation |
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If you use **Skittles v2** in your work, please cite: |
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```bibtex |
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@misc{miike2024skittlesv2, |
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title={Skittles v2: A Merged Text-to-Image Generation Model}, |
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author={miike-ai}, |
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year={2024}, |
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url={https://huggingface.co/miike-ai/skittles-v2}, |
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} |
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``` |
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