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+LCM-LoRA-Technical-Report.pdf filter=lfs diff=lfs merge=lfs -text

LCM-LoRA-Technical-Report.pdf

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README.md

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+---
+library_name: diffusers
+base_model: stabilityai/stable-diffusion-xl-base-1.0
+tags:
+- lora
+- text-to-image
+license: openrail++
+inference: false
+---
+
+# Latent Consistency Model (LCM) LoRA: SDXL
+
+Latent Consistency Model (LCM) LoRA was proposed in [LCM-LoRA: A universal Stable-Diffusion Acceleration Module](https://arxiv.org/abs/2311.05556) 
+by *Simian Luo, Yiqin Tan, Suraj Patil, Daniel Gu et al.*
+
+It is a distilled consistency adapter for [`stable-diffusion-xl-base-1.0`](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) that allows
+to reduce the number of inference steps to only between **2 - 8 steps**.
+
+| Model                                                                      | Params / M | 
+|----------------------------------------------------------------------------|------------|
+| [lcm-lora-sdv1-5](https://huggingface.co/latent-consistency/lcm-lora-sdv1-5)   | 67.5        |
+| [lcm-lora-ssd-1b](https://huggingface.co/latent-consistency/lcm-lora-ssd-1b)   | 105        |
+| [**lcm-lora-sdxl**](https://huggingface.co/latent-consistency/lcm-lora-sdxl) | **197M**  |
+
+## Usage
+
+LCM-LoRA is supported in 🤗 Hugging Face Diffusers library from version v0.23.0 onwards. To run the model, first 
+install the latest version of the Diffusers library as well as `peft`, `accelerate` and `transformers`.
+audio dataset from the Hugging Face Hub:
+
+```bash
+pip install --upgrade pip
+pip install --upgrade diffusers transformers accelerate peft
+```
+
+***Note: For detailed usage examples we recommend you to check out our official [LCM-LoRA docs](https://huggingface.co/docs/diffusers/main/en/using-diffusers/inference_with_lcm_lora)***
+
+### Text-to-Image
+
+The adapter can be loaded with it's base model `stabilityai/stable-diffusion-xl-base-1.0`. Next, the scheduler needs to be changed to [`LCMScheduler`](https://huggingface.co/docs/diffusers/v0.22.3/en/api/schedulers/lcm#diffusers.LCMScheduler) and we can reduce the number of inference steps to just 2 to 8 steps.
+Please make sure to either disable `guidance_scale` or use values between 1.0 and 2.0.
+
+```python
+import torch
+from diffusers import LCMScheduler, AutoPipelineForText2Image
+
+model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+adapter_id = "latent-consistency/lcm-lora-sdxl"
+
+pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16")
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+pipe.to("cuda")
+
+# load and fuse lcm lora
+pipe.load_lora_weights(adapter_id)
+pipe.fuse_lora()
+
+prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
+
+# disable guidance_scale by passing 0
+image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0).images[0]
+```
+
+![](./image.png)
+
+### Inpainting
+
+LCM-LoRA can be used for inpainting as well. 
+
+```python
+import torch
+from diffusers import AutoPipelineForInpainting, LCMScheduler
+from diffusers.utils import load_image, make_image_grid
+
+pipe = AutoPipelineForInpainting.from_pretrained(
+    "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
+    torch_dtype=torch.float16,
+    variant="fp16",
+).to("cuda")
+
+# set scheduler
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+
+# load LCM-LoRA
+pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
+pipe.fuse_lora()
+
+# load base and mask image
+init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png").resize((1024, 1024))
+mask_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png").resize((1024, 1024))
+
+prompt = "a castle on top of a mountain, highly detailed, 8k"
+generator = torch.manual_seed(42)
+image = pipe(
+    prompt=prompt,
+    image=init_image,
+    mask_image=mask_image,
+    generator=generator,
+    num_inference_steps=5,
+    guidance_scale=4,
+).images[0]
+make_image_grid([init_image, mask_image, image], rows=1, cols=3)
+```
+
+![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdxl_inpainting.png)
+
+
+## Combine with styled LoRAs
+
+LCM-LoRA can be combined with other LoRAs to generate styled-images in very few steps (4-8). In the following example, we'll use the LCM-LoRA with the [papercut LoRA](TheLastBen/Papercut_SDXL). 
+To learn more about how to combine LoRAs, refer to [this guide](https://huggingface.co/docs/diffusers/tutorials/using_peft_for_inference#combine-multiple-adapters).
+
+```python
+import torch
+from diffusers import DiffusionPipeline, LCMScheduler
+
+pipe = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    variant="fp16",
+    torch_dtype=torch.float16
+).to("cuda")
+
+# set scheduler
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+
+# load LoRAs
+pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl", adapter_name="lcm")
+pipe.load_lora_weights("TheLastBen/Papercut_SDXL", weight_name="papercut.safetensors", adapter_name="papercut")
+
+# Combine LoRAs
+pipe.set_adapters(["lcm", "papercut"], adapter_weights=[1.0, 0.8])
+
+prompt = "papercut, a cute fox"
+generator = torch.manual_seed(0)
+image = pipe(prompt, num_inference_steps=4, guidance_scale=1, generator=generator).images[0]
+image
+```
+
+![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdx_lora_mix.png)
+
+### ControlNet
+
+```python
+import torch
+import cv2
+import numpy as np
+from PIL import Image
+
+from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, LCMScheduler
+from diffusers.utils import load_image
+
+image = load_image(
+    "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
+).resize((1024, 1024))
+
+image = np.array(image)
+
+low_threshold = 100
+high_threshold = 200
+
+image = cv2.Canny(image, low_threshold, high_threshold)
+image = image[:, :, None]
+image = np.concatenate([image, image, image], axis=2)
+canny_image = Image.fromarray(image)
+
+controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-canny-sdxl-1.0-small", torch_dtype=torch.float16, variant="fp16")
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    controlnet=controlnet,
+    torch_dtype=torch.float16,
+    safety_checker=None,
+    variant="fp16"
+).to("cuda")
+
+# set scheduler
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+
+# load LCM-LoRA
+pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
+pipe.fuse_lora()
+
+generator = torch.manual_seed(0)
+image = pipe(
+    "picture of the mona lisa",
+    image=canny_image,
+    num_inference_steps=5,
+    guidance_scale=1.5,
+    controlnet_conditioning_scale=0.5,
+    cross_attention_kwargs={"scale": 1},
+    generator=generator,
+).images[0]
+make_image_grid([canny_image, image], rows=1, cols=2)
+```
+
+![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdxl_controlnet.png)
+
+
+<Tip>
+The inference parameters in this example might not work for all examples, so we recommend you to try different values for `num_inference_steps`, `guidance_scale`, `controlnet_conditioning_scale` and `cross_attention_kwargs` parameters and choose the best one. 
+</Tip>
+
+### T2I Adapter
+
+This example shows how to use the LCM-LoRA with the [Canny T2I-Adapter](TencentARC/t2i-adapter-canny-sdxl-1.0) and SDXL.
+
+```python
+import torch
+import cv2
+import numpy as np
+from PIL import Image
+
+from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, LCMScheduler
+from diffusers.utils import load_image, make_image_grid
+
+# Prepare image
+# Detect the canny map in low resolution to avoid high-frequency details
+image = load_image(
+    "https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/org_canny.jpg"
+).resize((384, 384))
+
+image = np.array(image)
+
+low_threshold = 100
+high_threshold = 200
+
+image = cv2.Canny(image, low_threshold, high_threshold)
+image = image[:, :, None]
+image = np.concatenate([image, image, image], axis=2)
+canny_image = Image.fromarray(image).resize((1024, 1024))
+
+# load adapter
+adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-canny-sdxl-1.0", torch_dtype=torch.float16, varient="fp16").to("cuda")
+
+pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0", 
+    adapter=adapter,
+    torch_dtype=torch.float16,
+    variant="fp16", 
+).to("cuda")
+
+# set scheduler
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+
+# load LCM-LoRA
+pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
+
+prompt = "Mystical fairy in real, magic, 4k picture, high quality"
+negative_prompt = "extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured"
+
+generator = torch.manual_seed(0)
+image = pipe(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    image=canny_image,
+    num_inference_steps=4,
+    guidance_scale=1.5, 
+    adapter_conditioning_scale=0.8, 
+    adapter_conditioning_factor=1,
+    generator=generator,
+).images[0]
+make_image_grid([canny_image, image], rows=1, cols=2)
+```
+
+![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdxl_t2iadapter.png)
+
+
+## Speed Benchmark
+
+TODO
+
+## Training
+
+TODO

pytorch_lora_weights.safetensors

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