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| #!/usr/bin/env python | |
| import spaces | |
| import gradio as gr | |
| def create_demo(process): | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## BRIA 2.2 ControlNet Canny") | |
| gr.HTML(''' | |
| <p style="margin-bottom: 10px; font-size: 94%"> | |
| This is a demo for ControlNet Canny that using | |
| <a href="https://huggingface.co/briaai/BRIA-2.2" target="_blank">BRIA 2.2 text-to-image model</a> as backbone. | |
| Trained on licensed data, BRIA 2.2 provide full legal liability coverage for copyright and privacy infringement. | |
| </p> | |
| ''') | |
| 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(''' | |
| # <p style="margin-bottom: 10px; font-size: 94%"> | |
| # This is a demo for ControlNet Canny that using | |
| # <a href="https://huggingface.co/briaai/BRIA-2.2" target="_blank">BRIA 2.2 text-to-image model</a> as backbone. | |
| # Trained on licensed data, BRIA 2.2 provide full legal liability coverage for copyright and privacy infringement. | |
| # </p> | |
| # ''') | |
| # 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) |