import cv2 import einops import gradio as gr import numpy as np import torch from diffusers import StableDiffusionControlNetPipeline, ControlNetModel from diffusers import UniPCMultistepScheduler from PIL import Image from controlnet_aux import OpenposeDetector # Constants low_threshold = 100 high_threshold = 200 # Models # controlnet_canny = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) # pipe_canny = StableDiffusionControlNetPipeline.from_pretrained( # "runwayml/stable-diffusion-v1-5", controlnet=controlnet_canny, safety_checker=None, torch_dtype=torch.float16 # ) # pipe_canny.scheduler = UniPCMultistepScheduler.from_config(pipe_canny.scheduler.config) # # This command loads the individual model components on GPU on-demand. So, we don't # # need to explicitly call pipe.to("cuda"). # pipe_canny.enable_model_cpu_offload() # pipe_canny.enable_xformers_memory_efficient_attention() # Generator seed, generator = torch.manual_seed(0) pose_model = OpenposeDetector.from_pretrained("lllyasviel/ControlNet") controlnet_pose = ControlNetModel.from_pretrained( "lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16 ) pipe_pose = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet_pose, safety_checker=None, torch_dtype=torch.float16 ) pipe_pose.scheduler = UniPCMultistepScheduler.from_config(pipe_pose.scheduler.config) # This command loads the individual model components on GPU on-demand. So, we don't # need to explicitly call pipe.to("cuda"). pipe_pose.enable_model_cpu_offload() # xformers pipe_pose.enable_xformers_memory_efficient_attention() # def get_canny_filter(image): # if not isinstance(image, np.ndarray): # image = np.array(image) # image = cv2.Canny(image, low_threshold, high_threshold) # image = image[: # , :, None] # image = np.concatenate([image, image, image], axis=2) # canny_image = Image.fromarray(image) # return canny_image def get_pose(image): return pose_model(image) def process(input_image, prompt, input_control): # TODO: Add other control tasks #if input_control == "Pose": return process_pose(input_image, prompt) # else: # return process_canny(input_image, prompt) # def process_canny(input_image, prompt): # canny_image = get_canny_filter(input_image) # output = pipe_canny( # prompt, # canny_image, # generator=generator, # num_images_per_prompt=1, # num_inference_steps=20, # ) # return [canny_image,output.images[0]] def process_pose(input_image, prompt): pose_image = get_pose(input_image) output = pipe_pose( prompt, pose_image, generator=generator, num_images_per_prompt=1, num_inference_steps=20, ) return [pose_image,output.images[0]] block = gr.Blocks().queue() control_task_list = [ "Canny Edge Map", "Pose" ] with block: gr.Markdown("## Adding Conditional Control to Text-to-Image Diffusion Models") gr.HTML('''

This is an unofficial demo for ControlNet, which is a neural network structure to control diffusion models by adding extra conditions such as canny edge detection. The demo is based on the Github implementation.

''') gr.HTML("

You can duplicate this Space to run it privately without a queue and load additional checkpoints. : Duplicate Space

") with gr.Row(): with gr.Column(): input_image = gr.Image(source='upload', type="numpy") # input_control = gr.Dropdown(control_task_list, value="Scribble", label="Control Task") prompt = gr.Textbox(label="Prompt") run_button = gr.Button(label="Run") with gr.Column(): result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto') ips = [input_image, prompt] run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) examples_list = [ # [ # "bird.png", # "bird", # "Canny Edge Map" # ], # [ # "turtle.png", # "turtle", # "Scribble", # "best quality, extremely detailed", # 'longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality', # 1, # 512, # 20, # 9.0, # 123490213, # 0.0, # 100, # 200 # ], [ "pose1.png", "Chef in the Kitchen", "Pose", # "best quality, extremely detailed", # 'longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality', # 1, # 512, # 20, # 9.0, # 123490213, # 0.0, # 100, # 200 ] ] examples = gr.Examples(examples=examples_list,inputs = [input_image, prompt], outputs = [result_gallery], cache_examples = True, fn = process) gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=RamAnanth1.ControlNet)") block.launch(debug = True)