update README (#1)

- update README (ac8f9e356081e6d322fadc4eb23908edba9a1e5b)


Co-authored-by: Will Berman <[email protected]>

README.md

@@ -18,136 +18,12 @@ Controlnet's auxiliary models are trained with stable diffusion 1.5. Experimenta
 The auxiliary conditioning is passed directly to the diffusers pipeline. If you want to process an image to create the auxiliary conditioning, external dependencies are required.
 
 Some of the additional conditionings can be extracted from images via additional models. We extracted these
-additional models from the original controlnet repo into a separate package that can be found on [github](https://github.com/patrickvonplaten/human_pose.git).
-
-## Canny edge detection
-
-Install opencv
-
-```sh
-$ pip install opencv-contrib-python
-```
-
-```python
-import cv2
-from PIL import Image
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-import torch
-import numpy as np
-
-image = Image.open('images/bird.png')
-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)
-image = Image.fromarray(image)
-
-controlnet = ControlNetModel.from_pretrained(
-    "fusing/stable-diffusion-v1-5-controlnet-canny",
-)
-
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
-)
-pipe.to('cuda')
-
-image = pipe("bird", image).images[0]
-
-image.save('images/bird_canny_out.png')
-```
-
-![bird](./images/bird.png)
-
-![bird_canny](./images/bird_canny.png)
-
-![bird_canny_out](./images/bird_canny_out.png)
-
-## M-LSD Straight line detection
-
-Install the additional controlnet models package.
-
-```sh
-$ pip install git+https://github.com/patrickvonplaten/human_pose.git
-```
-
-```py
-from PIL import Image
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-import torch
-from human_pose import MLSDdetector
-
-mlsd = MLSDdetector.from_pretrained('lllyasviel/ControlNet')
-
-image = Image.open('images/room.png')
-
-image = mlsd(image)
-
-controlnet = ControlNetModel.from_pretrained(
-    "fusing/stable-diffusion-v1-5-controlnet-mlsd",
-)
-
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
-)
-pipe.to('cuda')
-
-image = pipe("room", image).images[0]
-
-image.save('images/room_mlsd_out.png')
-```
-
-![room](./images/room.png)
-
-![room_mlsd](./images/room_mlsd.png)
-
-![room_mlsd_out](./images/room_mlsd_out.png)
-
-## Pose estimation
-
-Install the additional controlnet models package.
-
-```sh
-$ pip install git+https://github.com/patrickvonplaten/human_pose.git
-```
-
-```py
-from PIL import Image
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-import torch
-from human_pose import OpenposeDetector
-
-openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')
-
-image = Image.open('images/pose.png')
-
-image = openpose(image)
-
-controlnet = ControlNetModel.from_pretrained(
-    "fusing/stable-diffusion-v1-5-controlnet-openpose",
-)
-
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
-)
-pipe.to('cuda')
-
-image = pipe("chef in the kitchen", image).images[0]
-
-image.save('images/chef_pose_out.png')
-```
-
-![pose](./images/pose.png)
-
-![openpose](./images/openpose.png)
-
-![chef_pose_out](./images/chef_pose_out.png)
+additional models from the original controlnet repo into a separate package that can be found on [github](https://github.com/patrickvonplaten/controlnet_aux.git).
 
 ## Semantic Segmentation
 
+### Diffusers
+
 Semantic segmentation relies on transformers. Transformers is a 
 dependency of diffusers for running controlnet, so you should 
 have it installed already.
@@ -158,7 +34,7 @@ from PIL import Image
 import numpy as np
 from controlnet_utils import ade_palette
 import torch
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
 
 image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
 image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")
@@ -184,15 +60,23 @@ color_seg = color_seg.astype(np.uint8)
 image = Image.fromarray(color_seg)
 
 controlnet = ControlNetModel.from_pretrained(
-    "fusing/stable-diffusion-v1-5-controlnet-seg",
+    "fusing/stable-diffusion-v1-5-controlnet-seg", torch_dtype=torch.float16
 )
 
 pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
+    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
 )
-pipe.to('cuda')
 
-image = pipe("house", image).images[0]
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+# Remove if you do not have xformers installed
+# see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers
+# for installation instructions
+pipe.enable_xformers_memory_efficient_attention()
+
+pipe.enable_model_cpu_offload()
+
+image = pipe("house", image, num_inference_steps=20).images[0]
 
 image.save('./images/house_seg_out.png')
 ```
@@ -203,179 +87,6 @@ image.save('./images/house_seg_out.png')
 
 ![house_seg_out](images/house_seg_out.png)
 
-## Depth control
-
-Depth control relies on transformers. Transformers is a dependency of diffusers for running controlnet, so
-you should have it installed already.
-
-```py
-from transformers import pipeline
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-from PIL import Image
-import numpy as np
-
-depth_estimator = pipeline('depth-estimation')
-
-image = Image.open('./images/stormtrooper.png')
-image = depth_estimator(image)['depth']
-image = np.array(image)
-image = image[:, :, None]
-image = np.concatenate([image, image, image], axis=2)
-image = Image.fromarray(image)
-
-controlnet = ControlNetModel.from_pretrained(
-    "fusing/stable-diffusion-v1-5-controlnet-depth",
-)
-
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
-)
-pipe.to('cuda')
-
-image = pipe("Stormtrooper's lecture", image).images[0]
-
-image.save('./images/stormtrooper_depth_out.png')
-```
-
-![stormtrooper](./images/stormtrooper.png)
-
-![stormtrooler_depth](./images/stormtrooper_depth.png)
-
-![stormtrooler_depth_out](./images/stormtrooper_depth_out.png)
-
-
-## Normal map
-
-```py
-from PIL import Image
-from transformers import pipeline
-import numpy as np
-import cv2
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-
-image = Image.open("images/toy.png").convert("RGB")
-
-depth_estimator = pipeline("depth-estimation", model ="Intel/dpt-hybrid-midas" )
-
-image = depth_estimator(image)['predicted_depth'][0]
-
-image = image.numpy()
-
-image_depth = image.copy()
-image_depth -= np.min(image_depth)
-image_depth /= np.max(image_depth)
-
-bg_threhold = 0.4
-
-x = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3)
-x[image_depth < bg_threhold] = 0
-
-y = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=3)
-y[image_depth < bg_threhold] = 0
-
-z = np.ones_like(x) * np.pi * 2.0
-
-image = np.stack([x, y, z], axis=2)
-image /= np.sum(image ** 2.0, axis=2, keepdims=True) ** 0.5
-image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8)
-image = Image.fromarray(image)
-
-controlnet = ControlNetModel.from_pretrained(
-    "fusing/stable-diffusion-v1-5-controlnet-normal",
-)
-
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
-)
-pipe.to('cuda')
-
-image = pipe("cute toy", image).images[0]
-
-image.save('images/toy_normal_out.png')
-```
-
-![toy](./images/toy.png)
-
-![toy_normal](./images/toy_normal.png)
-
-![toy_normal_out](./images/toy_normal_out.png)
-
-## Scribble
-
-Install the additional controlnet models package.
-
-```sh
-$ pip install git+https://github.com/patrickvonplaten/human_pose.git
-```
-
-```py
-from PIL import Image
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-import torch
-from human_pose import HEDdetector
-
-hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')
-
-image = Image.open('images/bag.png')
-
-image = hed(image, scribble=True)
-
-controlnet = ControlNetModel.from_pretrained(
-    "fusing/stable-diffusion-v1-5-controlnet-scribble",
-)
-
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
-)
-pipe.to('cuda')
-
-image = pipe("bag", image).images[0]
-
-image.save('images/bag_scribble_out.png')
-```
-
-![bag](./images/bag.png)
-
-![bag_scribble](./images/bag_scribble.png)
-
-![bag_scribble_out](./images/bag_scribble_out.png)
-
-## HED Boundary
-
-Install the additional controlnet models package.
-
-```sh
-$ pip install git+https://github.com/patrickvonplaten/human_pose.git
-```
-
-```py
-from PIL import Image
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-import torch
-from human_pose import HEDdetector
-
-hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')
-
-image = Image.open('images/man.png')
-
-image = hed(image)
-
-controlnet = ControlNetModel.from_pretrained(
-    "fusing/stable-diffusion-v1-5-controlnet-hed",
-)
-
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
-)
-pipe.to('cuda')
-
-image = pipe("oil painting of handsome old man, masterpiece", image).images[0]
-
-image.save('images/man_hed_out.png')
-```
-
-![man](./images/man.png)
-
-![man_hed](./images/man_hed.png)
+### Training
 
-![man_hed_out](./images/man_hed_out.png)
+The semantic segmentation model was trained on 164K segmentation-image, caption pairs from ADE20K. The model was trained for 200 GPU-hours with Nvidia A100 80G using Stable Diffusion 1.5 as a base model.