content.raw: Testing new pix2pix-Turbo in real-time, very interesting GAN architecture that leverages SD-Turbo model. Here I'm using edge2image LoRA single-step inference 🤯
content.type: text
content.value: Testing new pix2pix-Turbo in real-time, very interesting GAN architecture that leverages SD-Turbo model. Here I'm using edge2image LoRA single-step inference 🤯
content.type: new_line
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content.raw: It's very interesting how ControlNet Canny quality is comparable, but in a single step. Looking forward to when they release the code: 
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content.value: It's very interesting how ControlNet Canny quality is comparable, but in a single step. Looking forward to when they release the code: 
content.href: https://github.com/GaParmar/img2img-turbo/issues/1
content.raw: https://github.com/GaParmar/img2img-turbo/issues/1
content.type: link
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content.raw: I've been keeping a list of fast diffusion model pipelines together with this real-time websocket app. Have a look if you want to test it locally, or check out the demo here on Spaces.
content.type: text
content.value: I've been keeping a list of fast diffusion model pipelines together with this real-time websocket app. Have a look if you want to test it locally, or check out the demo here on Spaces.
content.type: new_line
content.type: new_line
content.raw: https://huggingface.co/spaces/radames/real-time-pix2pix-turbo
content.id: radames/real-time-pix2pix-turbo
content.type: space
content.type: resource
content.url: https://huggingface.co/spaces/radames/real-time-pix2pix-turbo
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content.raw: Github app:
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content.value: Github app:
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content.href: https://github.com/radames/Real-Time-Latent-Consistency-Model/
content.raw: https://github.com/radames/Real-Time-Latent-Consistency-Model/
content.type: link
content.type: new_line
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content.raw: You can also check the authors img2img sketch model here
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content.value: You can also check the authors img2img sketch model here
content.type: new_line
content.type: new_line
content.raw: https://huggingface.co/spaces/gparmar/img2img-turbo-sketch
content.id: gparmar/img2img-turbo-sketch
content.type: space
content.type: resource
content.url: https://huggingface.co/spaces/gparmar/img2img-turbo-sketch
content.type: new_line
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content.raw: Refs: 
content.type: text
content.value: Refs: 
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content.label: One-Step Image Translation with Text-to-Image Models (2403.12036)
content.raw: https://huggingface.co/papers/2403.12036
content.id: 2403.12036
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2403.12036
content.type: new_line
content.type: new_line
content.raw: cc 
content.type: text
content.value: cc 
content.raw: @gparmar
content.type: mention
content.user: gparmar
content.type: text
content.raw: @junyanz
content.type: mention
content.user: junyanz
content.type: text
rawContent: Testing new pix2pix-Turbo in real-time, very interesting GAN architecture that leverages SD-Turbo model. Here I'm using edge2image LoRA single-step inference 🤯
  
  It's very interesting how ControlNet Canny quality is comparable, but in a single step. Looking forward to when they release the code: https://github.com/GaParmar/img2img-turbo/issues/1
  
  I've been keeping a list of fast diffusion model pipelines together with this real-time websocket app. Have a look if you want to test it locally, or check out the demo here on Spaces.
  
  https://huggingface.co/spaces/radames/real-time-pix2pix-turbo
  
  Github app:
  https://github.com/radames/Real-Time-Latent-Consistency-Model/
  
  You can also check the authors img2img sketch model here
  
  https://huggingface.co/spaces/gparmar/img2img-turbo-sketch
  
  Refs: 
  https://huggingface.co/papers/2403.12036
  
  cc @gparmar @junyanz 
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