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--- |
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license: openrail++ |
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library_name: diffusers |
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inference: false |
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tags: |
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- lora |
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- text-to-image |
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- stable-diffusion |
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--- |
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# Hyper-SD |
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Official Repository of the paper: *[Hyper-SD](https://arxiv.org/abs/2404.13686)*. |
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Project Page: https://hyper-sd.github.io/ |
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## Try our Hugging Face demos: |
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Hyper-SD Scribble demo host on [🤗 scribble](https://huggingface.co/spaces/ByteDance/Hyper-SD15-Scribble) |
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Hyper-SDXL One-step Text-to-Image demo host on [🤗 T2I](https://huggingface.co/spaces/ByteDance/Hyper-SDXL-1Step-T2I) |
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## Introduction |
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Hyper-SD is one of the new State-of-the-Art diffusion model acceleration techniques. |
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In this repository, we release the models distilled from [SDXL Base 1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and [Stable-Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)。 |
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## Checkpoints |
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* `Hyper-SDXL-Nstep-lora.safetensors`: Lora checkpoint, for SDXL-related models. |
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* `Hyper-SD15-Nstep-lora.safetensors`: Lora checkpoint, for SD1.5-related models. |
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* `Hyper-SDXL-1step-unet.safetensors`: Unet checkpoint distilled from SDXL-Base. |
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## Text-to-Image Usage |
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### SDXL-related models |
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#### 2-Steps, 4-Steps, 8-steps LoRA |
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Take the 2-steps LoRA as an example, you can also use other LoRAs for the corresponding inference steps setting. |
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```python |
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import torch |
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from diffusers import DiffusionPipeline, DDIMScheduler |
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from huggingface_hub import hf_hub_download |
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base_model_id = "stabilityai/stable-diffusion-xl-base-1.0" |
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repo_name = "ByteDance/Hyper-SD" |
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# Take 2-steps lora as an example |
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ckpt_name = "Hyper-SDXL-2steps-lora.safetensors" |
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# Load model. |
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pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to("cuda") |
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pipe.load_lora_weights(hf_hub_download(repo_name, ckpt_name)) |
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pipe.fuse_lora() |
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# Ensure ddim scheduler timestep spacing set as trailing !!! |
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pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") |
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# lower eta results in more detail |
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prompt="a photo of a cat" |
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image=pipe(prompt=prompt, num_inference_steps=2, guidance_scale=0).images[0] |
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``` |
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#### Unified LoRA (support 1 to 8 steps inference) |
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You can flexibly adjust the number of inference steps and eta value to achieve best performance. |
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```python |
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import torch |
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from diffusers import DiffusionPipeline, TCDScheduler |
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from huggingface_hub import hf_hub_download |
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base_model_id = "stabilityai/stable-diffusion-xl-base-1.0" |
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repo_name = "ByteDance/Hyper-SD" |
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ckpt_name = "Hyper-SDXL-1step-lora.safetensors" |
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# Load model. |
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pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to("cuda") |
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pipe.load_lora_weights(hf_hub_download(repo_name, ckpt_name)) |
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pipe.fuse_lora() |
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# Use TCD scheduler to achieve better image quality |
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pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) |
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# Lower eta results in more detail for multi-steps inference |
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eta=1.0 |
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prompt="a photo of a cat" |
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image=pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0, eta=eta).images[0] |
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``` |
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#### 1-step SDXL Unet |
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Only for the single step inference. |
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```python |
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import torch |
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from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler |
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from huggingface_hub import hf_hub_download |
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from safetensors.torch import load_file |
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base_model_id = "stabilityai/stable-diffusion-xl-base-1.0" |
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repo_name = "ByteDance/Hyper-SD" |
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ckpt_name = "Hyper-SDXL-1step-Unet.safetensors" |
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# Load model. |
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unet = UNet2DConditionModel.from_config(base_model_id, subfolder="unet").to("cuda", torch.float16) |
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unet.load_state_dict(load_file(hf_hub_download(repo_name, ckpt_name), device="cuda")) |
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pipe = DiffusionPipeline.from_pretrained(base_model_id, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda") |
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# Use LCM scheduler instead of ddim scheduler to support specific timestep number inputs |
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pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) |
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# Set start timesteps to 800 in the one-step inference to get better results |
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prompt="a photo of a cat" |
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image=pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0, timesteps=[800]).images[0] |
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``` |
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### SD1.5-related models |
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#### 2-Steps, 4-Steps, 8-steps LoRA |
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Take the 2-steps LoRA as an example, you can also use other LoRAs for the corresponding inference steps setting. |
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```python |
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import torch |
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from diffusers import DiffusionPipeline, DDIMScheduler |
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from huggingface_hub import hf_hub_download |
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base_model_id = "runwayml/stable-diffusion-v1-5" |
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repo_name = "ByteDance/Hyper-SD" |
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# Take 2-steps lora as an example |
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ckpt_name = "Hyper-SD15-2steps-lora.safetensors" |
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# Load model. |
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pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to("cuda") |
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pipe.load_lora_weights(hf_hub_download(repo_name, ckpt_name)) |
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pipe.fuse_lora() |
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# Ensure ddim scheduler timestep spacing set as trailing !!! |
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pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") |
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prompt="a photo of a cat" |
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image=pipe(prompt=prompt, num_inference_steps=2, guidance_scale=0).images[0] |
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``` |
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#### Unified LoRA (support 1 to 8 steps inference) |
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You can flexibly adjust the number of inference steps and eta value to achieve best performance. |
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```python |
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import torch |
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from diffusers import DiffusionPipeline, TCDScheduler |
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from huggingface_hub import hf_hub_download |
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base_model_id = "runwayml/stable-diffusion-v1-5" |
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repo_name = "ByteDance/Hyper-SD" |
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ckpt_name = "Hyper-SD15-1step-lora.safetensors" |
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# Load model. |
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pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to("cuda") |
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pipe.load_lora_weights(hf_hub_download(repo_name, ckpt_name)) |
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pipe.fuse_lora() |
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# Use TCD scheduler to achieve better image quality |
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pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) |
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# Lower eta results in more detail for multi-steps inference |
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eta=1.0 |
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prompt="a photo of a cat" |
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image=pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0, eta=eta).images[0] |
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``` |
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## ControlNet Usage |
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### SDXL-related models |
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#### 2-Steps, 4-Steps, 8-steps LoRA |
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Take Canny Controlnet and 2-steps inference as an example: |
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```python |
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import torch |
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from diffusers.utils import load_image |
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import numpy as np |
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import cv2 |
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from PIL import Image |
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from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL, DDIMScheduler |
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from huggingface_hub import hf_hub_download |
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# Load original image |
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image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png") |
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image = np.array(image) |
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# Prepare Canny Control Image |
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low_threshold = 100 |
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high_threshold = 200 |
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image = cv2.Canny(image, low_threshold, high_threshold) |
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image = image[:, :, None] |
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image = np.concatenate([image, image, image], axis=2) |
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control_image = Image.fromarray(image) |
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control_image.save("control.png") |
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control_weight = 0.5 # recommended for good generalization |
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# Initialize pipeline |
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controlnet = ControlNetModel.from_pretrained( |
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"diffusers/controlnet-canny-sdxl-1.0", |
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torch_dtype=torch.float16 |
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) |
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) |
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pipe = StableDiffusionXLControlNetPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, vae=vae, torch_dtype=torch.float16).to("cuda") |
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pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-SDXL-2steps-lora.safetensors")) |
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# Ensure ddim scheduler timestep spacing set as trailing !!! |
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pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") |
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pipe.fuse_lora() |
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image = pipe("A chocolate cookie", num_inference_steps=2, image=control_image, guidance_scale=0, controlnet_conditioning_scale=control_weight).images[0] |
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image.save('image_out.png') |
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``` |
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#### Unified LoRA (support 1 to 8 steps inference) |
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Take Canny Controlnet as an example: |
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```python |
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import torch |
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from diffusers.utils import load_image |
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import numpy as np |
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import cv2 |
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from PIL import Image |
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from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL, TCDScheduler |
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from huggingface_hub import hf_hub_download |
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# Load original image |
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image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png") |
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image = np.array(image) |
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# Prepare Canny Control Image |
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low_threshold = 100 |
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high_threshold = 200 |
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image = cv2.Canny(image, low_threshold, high_threshold) |
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image = image[:, :, None] |
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image = np.concatenate([image, image, image], axis=2) |
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control_image = Image.fromarray(image) |
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control_image.save("control.png") |
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control_weight = 0.5 # recommended for good generalization |
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# Initialize pipeline |
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controlnet = ControlNetModel.from_pretrained( |
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"diffusers/controlnet-canny-sdxl-1.0", |
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torch_dtype=torch.float16 |
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) |
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) |
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pipe = StableDiffusionXLControlNetPipeline.from_pretrained( |
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"stabilityai/stable-diffusion-xl-base-1.0", |
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controlnet=controlnet, vae=vae, torch_dtype=torch.float16).to("cuda") |
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# Load Hyper-SD15-1step lora |
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pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-SDXL-1step-lora.safetensors")) |
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pipe.fuse_lora() |
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# Use TCD scheduler to achieve better image quality |
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pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) |
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# Lower eta results in more detail for multi-steps inference |
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eta=1.0 |
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image = pipe("A chocolate cookie", num_inference_steps=4, image=control_image, guidance_scale=0, controlnet_conditioning_scale=control_weight, eta=eta).images[0] |
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image.save('image_out.png') |
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``` |
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### SD1.5-related models |
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|
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#### 2-Steps, 4-Steps, 8-steps LoRA |
|
Take Canny Controlnet and 2-steps inference as an example: |
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```python |
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import torch |
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from diffusers.utils import load_image |
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import numpy as np |
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import cv2 |
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from PIL import Image |
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from diffusers import ControlNetModel, StableDiffusionControlNetPipeline, DDIMScheduler |
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from huggingface_hub import hf_hub_download |
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controlnet_checkpoint = "lllyasviel/control_v11p_sd15_canny" |
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# Load original image |
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image = load_image("https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/input.png") |
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image = np.array(image) |
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# Prepare Canny Control Image |
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low_threshold = 100 |
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high_threshold = 200 |
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image = cv2.Canny(image, low_threshold, high_threshold) |
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image = image[:, :, None] |
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image = np.concatenate([image, image, image], axis=2) |
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control_image = Image.fromarray(image) |
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control_image.save("control.png") |
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# Initialize pipeline |
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controlnet = ControlNetModel.from_pretrained(controlnet_checkpoint, torch_dtype=torch.float16) |
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pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16).to("cuda") |
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pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-SD15-2steps-lora.safetensors")) |
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pipe.fuse_lora() |
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# Ensure ddim scheduler timestep spacing set as trailing !!! |
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pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") |
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image = pipe("a blue paradise bird in the jungle", num_inference_steps=2, image=control_image, guidance_scale=0).images[0] |
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image.save('image_out.png') |
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``` |
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#### Unified LoRA (support 1 to 8 steps inference) |
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Take Canny Controlnet as an example: |
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```python |
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import torch |
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from diffusers.utils import load_image |
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import numpy as np |
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import cv2 |
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from PIL import Image |
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from diffusers import ControlNetModel, StableDiffusionControlNetPipeline, TCDScheduler |
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from huggingface_hub import hf_hub_download |
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controlnet_checkpoint = "lllyasviel/control_v11p_sd15_canny" |
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# Load original image |
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image = load_image("https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/input.png") |
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image = np.array(image) |
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# Prepare Canny Control Image |
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low_threshold = 100 |
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high_threshold = 200 |
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image = cv2.Canny(image, low_threshold, high_threshold) |
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image = image[:, :, None] |
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image = np.concatenate([image, image, image], axis=2) |
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control_image = Image.fromarray(image) |
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control_image.save("control.png") |
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# Initialize pipeline |
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controlnet = ControlNetModel.from_pretrained(controlnet_checkpoint, torch_dtype=torch.float16) |
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pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16).to("cuda") |
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# Load Hyper-SD15-1step lora |
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pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-SD15-1step-lora.safetensors")) |
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pipe.fuse_lora() |
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# Use TCD scheduler to achieve better image quality |
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pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) |
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# Lower eta results in more detail for multi-steps inference |
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eta=1.0 |
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image = pipe("a blue paradise bird in the jungle", num_inference_steps=1, image=control_image, guidance_scale=0, eta=eta).images[0] |
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image.save('image_out.png') |
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``` |
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## Citation |
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```bibtex |
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@misc{ren2024hypersd, |
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title={Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis}, |
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author={Yuxi Ren and Xin Xia and Yanzuo Lu and Jiacheng Zhang and Jie Wu and Pan Xie and Xing Wang and Xuefeng Xiao}, |
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year={2024}, |
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eprint={2404.13686}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
