Instructions to use TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Upload TTPLANET_Controlnet_Tile_realistic_v2_fp16.safetensors
Browse filesHere's a refined version of the update notes for the Tile V2:
-Introducing the new Tile V2, enhanced with a vastly improved training dataset and more extensive training steps.
-The Tile V2 now automatically recognizes a wider range of objects without needing explicit prompts.
-I've made significant improvements to the color offset issue. if you are still seeing the significant offset, it's normal, just adding the prompt or use a color fix node.
-The control strength is more robust, allowing it to replace canny+openpose in some conditions.
If you encounter the edge halo issue with t2i or i2i, particularly with i2i, ensure that the preprocessing provides the controlnet image with sufficient blurring. If the output is too sharp, it may result in a 'halo'—a pronounced shape around the edges with high contrast. In such cases, apply some blur before sending it to the controlnet. If the output is too blurry, this could be due to excessive blurring during preprocessing, or the original picture may be too small.
Enjoy the enhanced capabilities of Tile V2!

![Q5A0[{{0{]I~`KJFCZJ7`}4.jpg](https://cdn-uploads.huggingface.co/production/uploads/641edd91eefe94aff6de024c/HMGmYz7IiLSqfoiMgcmgU.jpeg)
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