Image-to-Image
Diffusers
English
stable-diffusion
stable-diffusion-diffusers
text-to-image
diffusers-training
Instructions to use SherryXTChen/InstructCLIP-InstructPix2Pix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SherryXTChen/InstructCLIP-InstructPix2Pix 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("SherryXTChen/InstructCLIP-InstructPix2Pix", 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
Add image-to-image pipeline tag
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by nielsr HF Staff - opened
README.md
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base_model: timbrooks/instruct-pix2pix
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library_name: diffusers
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license: apache-2.0
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tags:
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- diffusers
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- diffusers-training
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inference: true
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language:
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# InstructCLIP: Improving Instruction-Guided Image Editing with Automated Data Refinement Using Contrastive Learning
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base_model: timbrooks/instruct-pix2pix
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datasets:
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- SherryXTChen/InstructCLIP-InstructPix2Pix-Data
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language:
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- en
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library_name: diffusers
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license: apache-2.0
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tags:
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- diffusers
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- diffusers-training
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inference: true
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pipeline_tag: image-to-image
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---
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# InstructCLIP: Improving Instruction-Guided Image Editing with Automated Data Refinement Using Contrastive Learning
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