136 lines
5.7 KiB
Markdown
136 lines
5.7 KiB
Markdown
---
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pipeline_tag: text-to-image
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inference: false
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---
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# SD-Turbo Model Card
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<!-- Provide a quick summary of what the model is/does. -->
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SD-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation.
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We release SD-Turbo as a research artifact, and to study small, distilled text-to-image models. For increased quality and prompt understanding,
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we recommend [SDXL-Turbo](https://huggingface.co/stabilityai/sdxl-turbo/).
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## Model Details
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### Model Description
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SD-Turbo is a distilled version of [Stable Diffusion 2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1), trained for real-time synthesis.
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SD-Turbo is based on a novel training method called Adversarial Diffusion Distillation (ADD) (see the [technical report](https://stability.ai/research/adversarial-diffusion-distillation)), which allows sampling large-scale foundational
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image diffusion models in 1 to 4 steps at high image quality.
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This approach uses score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal and combines this with an
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adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps.
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- **Developed by:** Stability AI
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- **Funded by:** Stability AI
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- **Model type:** Generative text-to-image model
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- **Finetuned from model:** [Stable Diffusion 2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1)
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### Model Sources
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For research purposes, we recommend our `generative-models` Github repository (https://github.com/Stability-AI/generative-models),
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which implements the most popular diffusion frameworks (both training and inference).
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- **Repository:** https://github.com/Stability-AI/generative-models
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- **Paper:** https://stability.ai/research/adversarial-diffusion-distillation
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- **Demo [for the bigger SDXL-Turbo]:** http://clipdrop.co/stable-diffusion-turbo
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## Evaluation
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The charts above evaluate user preference for SD-Turbo over other single- and multi-step models.
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SD-Turbo evaluated at a single step is preferred by human voters in terms of image quality and prompt following over LCM-Lora XL and LCM-Lora 1.5.
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**Note:** For increased quality, we recommend the bigger version [SDXL-Turbo](https://huggingface.co/stabilityai/sdxl-turbo/).
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For details on the user study, we refer to the [research paper](https://stability.ai/research/adversarial-diffusion-distillation).
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## Uses
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### Direct Use
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The model is intended for research purposes only. Possible research areas and tasks include
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- Research on generative models.
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- Research on real-time applications of generative models.
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- Research on the impact of real-time generative models.
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- Safe deployment of models which have the potential to generate harmful content.
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- Probing and understanding the limitations and biases of generative models.
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- Generation of artworks and use in design and other artistic processes.
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- Applications in educational or creative tools.
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Excluded uses are described below.
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### Diffusers
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```
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pip install diffusers transformers accelerate --upgrade
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```
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- **Text-to-image**:
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SD-Turbo does not make use of `guidance_scale` or `negative_prompt`, we disable it with `guidance_scale=0.0`.
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Preferably, the model generates images of size 512x512 but higher image sizes work as well.
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A **single step** is enough to generate high quality images.
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```py
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from diffusers import AutoPipelineForText2Image
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from modelscope import snapshot_download
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import torch
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local_dir = snapshot_download("AI-ModelScope/sd-turbo",revision='master')
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pipe = AutoPipelineForText2Image.from_pretrained(local_dir, torch_dtype=torch.float16, variant="fp16")
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pipe.to("cuda")
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prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe."
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image = pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0.0).images[0]
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```
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- **Image-to-image**:
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When using SD-Turbo for image-to-image generation, make sure that `num_inference_steps` * `strength` is larger or equal
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to 1. The image-to-image pipeline will run for `int(num_inference_steps * strength)` steps, *e.g.* 0.5 * 2.0 = 1 step in our example
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below.
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```py
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from diffusers import AutoPipelineForImage2Image
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from diffusers.utils import load_image
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from modelscope import snapshot_download
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import torch
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local_dir = snapshot_download("AI-ModelScope/sd-turbo",revision='master')
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pipe = AutoPipelineForImage2Image.from_pretrained(local_dir, torch_dtype=torch.float16, variant="fp16")
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pipe.to("cuda")
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init_image = load_image(local_dir+"/cat.png").resize((512, 512))
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prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k"
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image = pipe(prompt, image=init_image, num_inference_steps=100, strength=0.5, guidance_scale=0.0).images[0]
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```
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### Out-of-Scope Use
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The model was not trained to be factual or true representations of people or events,
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and therefore using the model to generate such content is out-of-scope for the abilities of this model.
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The model should not be used in any way that violates Stability AI's [Acceptable Use Policy](https://stability.ai/use-policy).
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## Limitations and Bias
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### Limitations
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- The quality and prompt alignment is lower than that of [SDXL-Turbo](https://huggingface.co/stabilityai/sdxl-turbo/).
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- The generated images are of a fixed resolution (512x512 pix), and the model does not achieve perfect photorealism.
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- The model cannot render legible text.
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- Faces and people in general may not be generated properly.
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- The autoencoding part of the model is lossy.
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### Recommendations
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The model is intended for research purposes only.
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## How to Get Started with the Model
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Check out https://github.com/Stability-AI/generative-models
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