This is a demo for ControlNet Canny that using BRIA 2.2 text-to-image model as backbone. Trained on licensed data, BRIA 2.2 provide full legal liability coverage for copyright and privacy infringement.
''') with gr.Row(): with gr.Column(): input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam prompt = gr.Textbox(label="Prompt") negative_prompt = gr.Textbox(label="Negative prompt", value="Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers") num_steps = gr.Slider(label="Number of steps", minimum=25, maximum=100, value=50, step=1) controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05) seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True,) run_button = gr.Button(value="Run") with gr.Column(): result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[2], height='auto') inputs = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed] run_button.click( fn=process, inputs=inputs, outputs=result_gallery, api_name="canny", ) return demo if __name__ == "__main__": from model import Model model = Model(task_name="Canny") demo = create_demo(model.process_canny) demo.queue().launch() ################################################################################################################################ # from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler # from diffusers.utils import load_image # from PIL import Image # import torch # import numpy as np # import cv2 # import gradio as gr # from torchvision import transforms # controlnet = ControlNetModel.from_pretrained( # "briaai/BRIA-2.2-ControlNet-Canny", # torch_dtype=torch.float16 # ).to('cuda') # pipe = StableDiffusionXLControlNetPipeline.from_pretrained( # "briaai/BRIA-2.2", # controlnet=controlnet, # torch_dtype=torch.float16, # device_map='auto', # low_cpu_mem_usage=True, # offload_state_dict=True, # ).to('cuda') # pipe.scheduler = EulerAncestralDiscreteScheduler( # beta_start=0.00085, # beta_end=0.012, # beta_schedule="scaled_linear", # num_train_timesteps=1000, # steps_offset=1 # ) # # pipe.enable_freeu(b1=1.1, b2=1.1, s1=0.5, s2=0.7) # pipe.enable_xformers_memory_efficient_attention() # pipe.force_zeros_for_empty_prompt = False # low_threshold = 100 # high_threshold = 200 # def resize_image(image): # image = image.convert('RGB') # current_size = image.size # if current_size[0] > current_size[1]: # center_cropped_image = transforms.functional.center_crop(image, (current_size[1], current_size[1])) # else: # center_cropped_image = transforms.functional.center_crop(image, (current_size[0], current_size[0])) # resized_image = transforms.functional.resize(center_cropped_image, (1024, 1024)) # return resized_image # 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 process(input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed): # generator = torch.manual_seed(seed) # # resize input_image to 1024x1024 # input_image = resize_image(input_image) # canny_image = get_canny_filter(input_image) # images = pipe( # prompt, negative_prompt=negative_prompt, image=canny_image, num_inference_steps=num_steps, controlnet_conditioning_scale=float(controlnet_conditioning_scale), # generator=generator, # ).images # return [canny_image,images[0]] # block = gr.Blocks().queue() # with block: # gr.Markdown("## BRIA 2.2 ControlNet Canny") # gr.HTML(''' ## This is a demo for ControlNet Canny that using # BRIA 2.2 text-to-image model as backbone. # Trained on licensed data, BRIA 2.2 provide full legal liability coverage for copyright and privacy infringement. #
# ''') # with gr.Row(): # with gr.Column(): # input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam # prompt = gr.Textbox(label="Prompt") # negative_prompt = gr.Textbox(label="Negative prompt", value="Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers") # num_steps = gr.Slider(label="Number of steps", minimum=25, maximum=100, value=50, step=1) # controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05) # seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True,) # run_button = gr.Button(value="Run") # with gr.Column(): # result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[2], height='auto') # ips = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed] # run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) # block.launch(debug = True)