Instructions to use jdopensource/JoyAI-Image-Edit-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jdopensource/JoyAI-Image-Edit-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jdopensource/JoyAI-Image-Edit-Diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -62,7 +62,7 @@ pipeline.set_progress_bar_config(disable=None)
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print("pipeline loaded")
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img_path = "./test_images/input.png"
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prompt = "
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image = Image.open(img_path).convert("RGB")
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prompts = [f"<|im_start|>user\n<image>\n{prompt}<|im_end|>\n"]
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print("pipeline loaded")
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img_path = "./test_images/input.png"
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prompt = "Remove the construction structure from the top of the crane."
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image = Image.open(img_path).convert("RGB")
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prompts = [f"<|im_start|>user\n<image>\n{prompt}<|im_end|>\n"]
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