Upload SDXL Refiner snapshot (preserve folder structure)
Browse files- README.md +107 -37
- text_encoder_2/model.safetensors +3 -0
- unet/diffusion_pytorch_model.fp16.safetensors +3 -0
README.md
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Detail Enhancement: Removes residual noise and enhances textures from the Base stage outputs.
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├── scheduler/
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│ └── scheduler_config.json Noise scheduler configuration
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├── text_encoder/
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│ └── model.safetensors CLIP text encoder weights
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├── tokenizer/
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│ ├── merges.txt
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│ ├── vocab.json
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│ └── tokenizer_config.json Tokenizer files
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└── README.md
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Install Dependencies
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import torch
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pipe =
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"
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torch_dtype=torch.float16
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---
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license: openrail++
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tags:
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- stable-diffusion
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- image-to-image
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---
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# SD-XL 1.0-refiner Model Card
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## Model
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[SDXL](https://arxiv.org/abs/2307.01952) consists of an [ensemble of experts](https://arxiv.org/abs/2211.01324) pipeline for latent diffusion:
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In a first step, the base model (available here: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) is used to generate (noisy) latents,
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which are then further processed with a refinement model specialized for the final denoising steps.
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Note that the base model can be used as a standalone module.
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Alternatively, we can use a two-stage pipeline as follows:
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First, the base model is used to generate latents of the desired output size.
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In the second step, we use a specialized high-resolution model and apply a technique called SDEdit (https://arxiv.org/abs/2108.01073, also known as "img2img")
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to the latents generated in the first step, using the same prompt. This technique is slightly slower than the first one, as it requires more function evaluations.
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Source code is available at https://github.com/Stability-AI/generative-models .
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### Model Description
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- **Developed by:** Stability AI
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- **Model type:** Diffusion-based text-to-image generative model
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- **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/blob/main/LICENSE.md)
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- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses two fixed, pretrained text encoders ([OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip) and [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main)).
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- **Resources for more information:** Check out our [GitHub Repository](https://github.com/Stability-AI/generative-models) and the [SDXL report on arXiv](https://arxiv.org/abs/2307.01952).
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### Model Sources
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For research purposes, we recommned our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popoular diffusion frameworks (both training and inference) and for which new functionalities like distillation will be added over time.
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[Clipdrop](https://clipdrop.co/stable-diffusion) provides free SDXL inference.
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- **Repository:** https://github.com/Stability-AI/generative-models
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- **Demo:** https://clipdrop.co/stable-diffusion
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## Evaluation
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The chart above evaluates user preference for SDXL (with and without refinement) over SDXL 0.9 and Stable Diffusion 1.5 and 2.1.
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The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance.
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### 🧨 Diffusers
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Make sure to upgrade diffusers to >= 0.18.0:
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```
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pip install diffusers --upgrade
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```
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In addition make sure to install `transformers`, `safetensors`, `accelerate` as well as the invisible watermark:
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```
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pip install invisible_watermark transformers accelerate safetensors
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```
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Yon can then use the refiner to improve images.
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```py
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import torch
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from diffusers import StableDiffusionXLImg2ImgPipeline
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from diffusers.utils import load_image
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pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
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)
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pipe = pipe.to("cuda")
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url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png"
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init_image = load_image(url).convert("RGB")
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prompt = "a photo of an astronaut riding a horse on mars"
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image = pipe(prompt, image=init_image).images
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```
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When using `torch >= 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline:
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```py
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pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
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```
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If you are limited by GPU VRAM, you can enable *cpu offloading* by calling `pipe.enable_model_cpu_offload`
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instead of `.to("cuda")`:
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```diff
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- pipe.to("cuda")
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+ pipe.enable_model_cpu_offload()
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```
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For more advanced use cases, please have a look at [the docs](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl).
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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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- 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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- Research on 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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Excluded uses are described below.
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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, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
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## Limitations and Bias
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### Limitations
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- The model does not achieve perfect photorealism
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- The model cannot render legible text
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- The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
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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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### Bias
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While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
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unet/diffusion_pytorch_model.fp16.safetensors
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