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README.md

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+# Cog SDXL Canny ControlNet with LoRA support
+
+[![Replicate demo and cloud API](https://replicate.com/stability-ai/sdxl/badge)](https://replicate.com/batouresearch/sdxl-controlnet-lora)
+
+This is an implementation of Stability AI's [SDXL](https://github.com/Stability-AI/generative-models) as a [Cog](https://github.com/replicate/cog) model with ControlNet and Replicate's LoRA support.
+
+## Basic Usage
+
+For prediction:
+
+```bash
+cog predict -i prompt="shot in the style of sksfer, ..." -i image=@image.png -i lora_weights="https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar"
+```
+You may need to increase the default `lora_scale` value to big values such as <strong>0.8 ... 0.95</strong>.
+
+## Limitations
+- `lora_weights` only accepts models trained in Replicate and is a mandatory parameter.
+- `expert_ensemble_refiner` is currently not supported, you can use `base_image_refiner` instead.
+- For the moment, the model only uses canny as the conditional image.
+
+<br>
+<strong>For further improvements of this project, feel free to fork and PR!</strong>

cog.yaml

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+# Configuration for Cog ⚙️
+# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md
+
+build:
+  gpu: true
+  cuda: "11.8"
+  python_version: "3.9"
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "ffmpeg"
+    - "libsm6"
+    - "libxext6"
+    - "wget"
+  python_packages:
+    - "diffusers==0.21.4"
+    - "torch==2.0.1"
+    - "transformers==4.34.0"
+    - "invisible-watermark==0.2.0"
+    - "accelerate==0.21.0"
+    - "pandas==2.0.3"
+    - "torchvision==0.15.2"
+    - "numpy==1.25.1"
+    - "pandas==2.0.3"
+    - "fire==0.5.0"
+    - "opencv-python>=4.1.0.25"
+    - "mediapipe==0.10.2"
+
+  run:
+    - curl -o /usr/local/bin/pget -L "https://github.com/replicate/pget/releases/download/v0.0.3/pget" && chmod +x /usr/local/bin/pget
+    - wget http://thegiflibrary.tumblr.com/post/11565547760 -O face_landmarker_v2_with_blendshapes.task -q https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/1/face_landmarker.task
+
+predict: "predict.py:Predictor"
+train: "train.py:train"