GaParmar/img2img-turbo

• highreadme#1

  Reposition the README's opening to emphasize one-step, turbo inference

  Why:

  CURRENT

  # img2img-turbo
  
  **Paper** | **Sketch2Image Demo** 
  #### **Quick start:** [**Running Locally**](#getting-started) | [**Gradio (locally hosted)**](#gradio-demo) | [**Training**](#training-with-your-own-data)
  
  ### Cat Sketching
  <p align="left" >
  
  </p>
  
  ### Fish Sketching
  <p align="left">
  
  </p>
  
  We propose a general method for adapting a single-step diffusion model, such as SD-Turbo, to new tasks and domains through adversarial learning. This enables us to leverage the internal knowledge of pre-trained diffusion models while achieving efficient inference (e.g., for 512x512 images, 0.29 seconds on A6000 and 0.11 seconds on A100).

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  # img2img-turbo
  
  **One-step image-to-image translation with Stable Diffusion Turbo, achieving extreme speed and efficiency for tasks like sketch2image and day2night.**
  
  **Paper** | **Sketch2Image Demo** 
  #### **Quick start:** [**Running Locally**](#getting-started) | [**Gradio (locally hosted)**](#gradio-demo) | [**Training**](#training-with-your-own-data)
  
  ### Cat Sketching
  <p align="left" >
  
  </p>
  
  ### Fish Sketching
  <p align="left">
  
  </p>
  
  We propose a general method for adapting a single-step diffusion model, such as SD-Turbo, to new tasks and domains through adversarial learning. This enables us to leverage the internal knowledge of pre-trained diffusion models while achieving efficient inference (e.g., for 512x512 images, 0.29 seconds on A6000 and 0.11 seconds on A100).

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• mediumabout#2

  Add the arXiv paper link to the 'Homepage' field

  Why:

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  https://arxiv.org/abs/2403.12036

  OpenDoneDismiss
• mediumtopics#3

  Add specific topics related to one-step inference and LCMs

  Why:

  CURRENT

  computer-vision, deep-learning, generative-adversarial-network, generative-art, stable-diffusion

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  computer-vision, deep-learning, generative-adversarial-network, generative-art, stable-diffusion, one-step-inference, latent-consistency-models, image-to-image-translation, sd-turbo

  OpenDoneDismiss

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  What are fast, one-step AI models for image-to-image translation, like sketch to photo?

  you: not recommended
  AI recommended (in order):

  1. Pix2Pix
  2. CycleGAN
  3. StyleGAN
  4. U-Net
  5. Stable Diffusion
  6. DALL-E 2
  7. Midjourney
  8. SPADE

  AI recommended 8 alternatives but never named GaParmar/img2img-turbo. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  Seeking a deep learning solution for rapid image transformation, such as turning sketches into photos.

  you: not recommended
  AI recommended (in order):

  1. Pix2Pix
  2. CycleGAN
  3. SPADE
  4. StyleGAN
  5. ControlNet
  6. InstructPix2Pix

  AI recommended 6 alternatives but never named GaParmar/img2img-turbo. This is the gap to close.

  Show full AI answer

• Metadata completeness

  warn

  Suggestion:

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of GaParmar/img2img-turbo?

  pass
  ✓AI named GaParmar/img2img-turbo explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts GaParmar/img2img-turbo in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named GaParmar/img2img-turbo explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo GaParmar/img2img-turbo solve, and who is the primary audience?

  pass
  ✓AI named GaParmar/img2img-turbo explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?