Update README.md

README.md

@@ -64,19 +64,147 @@ image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0).images[0]
 
 ### Image-to-Image
 
-Works as well! TODO docs
+LCM-LoRA can be applied to image-to-image tasks too. Let's look at how we can perform image-to-image generation with LCMs. For this example we'll use the [dreamshaper-7](https://huggingface.co/Lykon/dreamshaper-7) model and the LCM-LoRA for `stable-diffusion-v1-5 `.
+
+```python
+import torch
+from diffusers import AutoPipelineForImage2Image, LCMScheduler
+from diffusers.utils import make_image_grid, load_image
+
+pipe = AutoPipelineForImage2Image.from_pretrained(
+    "Lykon/dreamshaper-7",
+    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-sdv1-5")
+pipe.fuse_lora()
+
+# prepare image
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png"
+init_image = load_image(url)
+prompt = "Astronauts in a jungle, cold color palette, muted colors, detailed, 8k"
+
+# pass prompt and image to pipeline
+generator = torch.manual_seed(0)
+image = pipe(
+    prompt,
+    image=init_image,
+    num_inference_steps=4,
+    guidance_scale=1,
+    strength=0.6,
+    generator=generator
+).images[0]
+make_image_grid([init_image, image], rows=1, cols=2)
+```
+
+![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdv1-5_i2i.png)
+
 
 ### Inpainting
 
-Works as well! TODO docs
+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(
+    "runwayml/stable-diffusion-inpainting",
+    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-sdv1-5")
+pipe.fuse_lora()
+
+# load base and mask image
+init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png")
+mask_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png")
+
+# generator = torch.Generator("cuda").manual_seed(92)
+prompt = "concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k"
+generator = torch.manual_seed(0)
+image = pipe(
+    prompt=prompt,
+    image=init_image,
+    mask_image=mask_image,
+    generator=generator,
+    num_inference_steps=4,
+    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_sdv1-5_inpainting.png)
+
 
 ### ControlNet
 
-Works as well! TODO docs
+For this example, we'll use the SD-v1-5 model and the LCM-LoRA for SD-v1-5 with canny ControlNet.
+
+```python
+import torch
+import cv2
+import numpy as np
+from PIL import Image
+
+from diffusers import StableDiffusionControlNetPipeline, 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((512, 512))
+
+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("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
+pipe = StableDiffusionControlNetPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    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-sdv1-5")
+
+generator = torch.manual_seed(0)
+image = pipe(
+    "the mona lisa",
+    image=canny_image,
+    num_inference_steps=4,
+    guidance_scale=1.5,
+    controlnet_conditioning_scale=0.8,
+    cross_attention_kwargs={"scale": 1},
+    generator=generator,
+).images[0]
+make_image_grid([canny_image, image], rows=1, cols=2)
+```
 
-### T2I Adapter
+![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lcm/lcm_sdv1-5_controlnet.png)
 
-Works as well! TODO docs
 
 ## Speed Benchmark