update README (#1)

- update README (32a7d8203498f07503f555d269af13e51e8069b3)


Co-authored-by: Will Berman <williamberman@users.noreply.huggingface.co>

README.md

@@ -18,107 +18,23 @@ 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)
+additional models from the original controlnet repo into a separate package that can be found on [github](https://github.com/patrickvonplaten/controlnet_aux.git).
 
 ## Pose estimation
 
+### Diffusers
+
 Install the additional controlnet models package.
 
 ```sh
-$ pip install git+https://github.com/patrickvonplaten/human_pose.git
+$ pip install git+https://github.com/patrickvonplaten/controlnet_aux.git
 ```
 
 ```py
 from PIL import Image
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
 import torch
-from human_pose import OpenposeDetector
+from controlnet_aux import OpenposeDetector
 
 openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')
 
@@ -127,15 +43,23 @@ image = Image.open('images/pose.png')
 image = openpose(image)
 
 controlnet = ControlNetModel.from_pretrained(
-    "fusing/stable-diffusion-v1-5-controlnet-openpose",
+    "fusing/stable-diffusion-v1-5-controlnet-openpose", 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("chef in the kitchen", 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("chef in the kitchen", image, num_inference_steps=20).images[0]
 
 image.save('images/chef_pose_out.png')
 ```
@@ -146,236 +70,6 @@ image.save('images/chef_pose_out.png')
 
 ![chef_pose_out](./images/chef_pose_out.png)
 
-## Semantic Segmentation
-
-Semantic segmentation relies on transformers. Transformers is a 
-dependency of diffusers for running controlnet, so you should 
-have it installed already.
-
-```py
-from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
-from PIL import Image
-import numpy as np
-from controlnet_utils import ade_palette
-import torch
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-
-image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
-image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")
-
-image = Image.open("./images/house.png").convert('RGB')
-
-pixel_values = image_processor(image, return_tensors="pt").pixel_values
-
-with torch.no_grad():
-  outputs = image_segmentor(pixel_values)
-
-seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
-
-color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
-
-palette = np.array(ade_palette())
-
-for label, color in enumerate(palette):
-    color_seg[seg == label, :] = color
-
-color_seg = color_seg.astype(np.uint8)
-
-image = Image.fromarray(color_seg)
-
-controlnet = ControlNetModel.from_pretrained(
-    "fusing/stable-diffusion-v1-5-controlnet-seg",
-)
-
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
-)
-pipe.to('cuda')
-
-image = pipe("house", image).images[0]
-
-image.save('./images/house_seg_out.png')
-```
-
-![house](images/house.png)
-
-![house_seg](images/house_seg.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 Openpose model was trained on 200k pose-image, caption pairs. The pose estimation images were generated with Openpose. The model was trained for 300 GPU-hours with Nvidia A100 80G using Stable Diffusion 1.5 as a base model.