PixelFlow allows you to use all these features
Unlock the full potential of generative AI with Segmind. Create stunning visuals and innovative designs with total creative control. Take advantage of powerful development tools to automate processes and models, elevating your creative workflow.
Segmented Creation Workflow
Gain greater control by dividing the creative process into distinct steps, refining each phase.
Customized Output
Customize at various stages, from initial generation to final adjustments, ensuring tailored creative outputs.
Layering Different Models
Integrate and utilize multiple models simultaneously, producing complex and polished creative results.
Workflow APIs
Deploy Pixelflows as APIs quickly, without server setup, ensuring scalability and efficiency.
Stable Diffusion 3 (SD3) Tile ControlNet
SD3 Medium Tile ControlNet is an advanced deep learning model designed for generating detailed images based on textual prompts and tile-based input images. By using tiling techniques, this model can create coherent large-scale images with a high level of detail and consistency. SD3 Medium Tile ControlNet is ideal for scenarios requiring expansive and detailed visual outputs.
How to Use the Model?
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Input Prompts: Provide a textual description of the desired image in the "Prompt" field.
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Input Image: Upload an image to guide the generation process.
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Negative Prompts: Indicate elements to exclude from the generation.
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Inference Steps: Set the number of steps for the model to refine the image. More steps typically result in higher quality.
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Strength: Adjust this to control how strongly the input image influences the generated output. Higher values will make the output more similar to the input tiles.
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Seed: Define a seed value for reproducibility. Randomly generate seeds if consistency is not required.
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Guidance Scale: Adjusts how closely the generated image follows the prompt. Higher values ensure the image aligns closely with the prompt.
How to Fine-Tune Outputs?
Fine-tuning the outputs can be achieved by adjusting several parameters:
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Inference Steps: Increasing the number of steps (e.g., from 20 to 50) can generate finer details but at the cost of longer processing times.
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Strength: Adjust the strength to control the influence of the input image. For minor adjustments, vary between 0.6 to 0.9. Lower values provide more creative freedom to the model.
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Guidance Scale: Typically between 7 and 15. Use higher values for strict adherence to prompts and lower values for more abstract results.
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Sampler: Different samplers (e.g., ddim, p_sampler) can affect the generation style and speed. Experiment with these to find the optimal balance for your use case.
Use Cases
SD3 Medium Tile ControlNet can be effectively used for various applications, such as:
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Architectural Visualization: Generate detailed floorplans, facades, and landscape designs from textual descriptions and input tiles.
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Game Design: Create expansive and coherent game maps and environments.
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Graphic Design: Produce large-format graphics and posters with consistent detailing.
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Marketing Materials: Develop intricate and high-quality visual content for marketing campaigns.
Other Popular Models
sdxl-controlnet
SDXL ControlNet gives unprecedented control over text-to-image generation. SDXL ControlNet models Introduces the concept of conditioning inputs, which provide additional information to guide the image generation process
faceswap-v2
Take a picture/gif and replace the face in it with a face of your choice. You only need one image of the desired face. No dataset, no training
sdxl-inpaint
This model is capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask
codeformer
CodeFormer is a robust face restoration algorithm for old photos or AI-generated faces.
