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from diffusers.utils import load_image |
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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler |
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from torchvision.utils import save_image |
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from PIL import Image |
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from pytorch_lightning import seed_everything |
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import subprocess |
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from collections import OrderedDict |
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import cv2 |
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import einops |
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import gradio as gr |
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import numpy as np |
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import torch |
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import random |
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import os |
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from annotator.util import resize_image, HWC3 |
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def create_demo(): |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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use_blip = True |
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use_gradio = True |
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base_model_path = "stabilityai/stable-diffusion-2-1" |
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config_dict = OrderedDict([('SAM Pretrained(v0-1)', 'shgao/edit-anything-v0-1-1'), |
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('LAION Pretrained(v0-3)', 'shgao/edit-anything-v0-3'), |
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('LAION Pretrained(v0-4)', 'shgao/edit-anything-v0-4-sd21'), |
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]) |
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def obtain_generation_model(controlnet_path): |
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controlnet = ControlNetModel.from_pretrained( |
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controlnet_path, torch_dtype=torch.float16) |
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pipe = StableDiffusionControlNetPipeline.from_pretrained( |
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base_model_path, controlnet=controlnet, torch_dtype=torch.float16 |
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) |
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pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) |
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pipe.enable_xformers_memory_efficient_attention() |
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pipe.to(device) |
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return pipe |
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global default_controlnet_path |
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default_controlnet_path = config_dict['LAION Pretrained(v0-4)'] |
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pipe = obtain_generation_model(default_controlnet_path) |
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try: |
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from segment_anything import sam_model_registry, SamAutomaticMaskGenerator |
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except ImportError: |
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print('segment_anything not installed') |
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result = subprocess.run(['pip', 'install', 'git+https://github.com/facebookresearch/segment-anything.git'], |
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check=True) |
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print(f'Install segment_anything {result}') |
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if not os.path.exists('./models/sam_vit_h_4b8939.pth'): |
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result = subprocess.run( |
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['wget', 'https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth', '-P', 'models'], |
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check=True) |
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print(f'Download sam_vit_h_4b8939.pth {result}') |
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sam_checkpoint = "models/sam_vit_h_4b8939.pth" |
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model_type = "default" |
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sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) |
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sam.to(device=device) |
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mask_generator = SamAutomaticMaskGenerator(sam) |
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if use_blip: |
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from transformers import AutoProcessor, Blip2ForConditionalGeneration |
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processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b") |
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blip_model = Blip2ForConditionalGeneration.from_pretrained( |
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"Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16) |
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blip_model.to(device) |
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blip_model.to(device) |
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def get_blip2_text(image): |
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inputs = processor(image, return_tensors="pt").to(device, torch.float16) |
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generated_ids = blip_model.generate(**inputs, max_new_tokens=50) |
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generated_text = processor.batch_decode( |
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generated_ids, skip_special_tokens=True)[0].strip() |
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return generated_text |
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def show_anns(anns): |
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if len(anns) == 0: |
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return |
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sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True) |
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full_img = None |
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for i in range(len(sorted_anns)): |
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ann = anns[i] |
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m = ann['segmentation'] |
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if full_img is None: |
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full_img = np.zeros((m.shape[0], m.shape[1], 3)) |
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map = np.zeros((m.shape[0], m.shape[1]), dtype=np.uint16) |
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map[m != 0] = i + 1 |
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color_mask = np.random.random((1, 3)).tolist()[0] |
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full_img[m != 0] = color_mask |
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full_img = full_img * 255 |
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res = np.zeros((map.shape[0], map.shape[1], 3)) |
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res[:, :, 0] = map % 256 |
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res[:, :, 1] = map // 256 |
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res.astype(np.float32) |
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full_img = Image.fromarray(np.uint8(full_img)) |
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return full_img, res |
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def get_sam_control(image): |
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masks = mask_generator.generate(image) |
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full_img, res = show_anns(masks) |
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return full_img, res |
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def process(condition_model, input_image, enable_auto_prompt, prompt, a_prompt, n_prompt, num_samples, |
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image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta): |
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global default_controlnet_path |
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global pipe |
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print("To Use:", config_dict[condition_model], "Current:", default_controlnet_path) |
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if default_controlnet_path != config_dict[condition_model]: |
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print("Change condition model to:", config_dict[condition_model]) |
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pipe = obtain_generation_model(config_dict[condition_model]) |
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default_controlnet_path = config_dict[condition_model] |
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with torch.no_grad(): |
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if use_blip and (enable_auto_prompt or len(prompt) == 0): |
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print("Generating text:") |
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blip2_prompt = get_blip2_text(input_image) |
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print("Generated text:", blip2_prompt) |
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if len(prompt) > 0: |
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prompt = blip2_prompt + ',' + prompt |
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else: |
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prompt = blip2_prompt |
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print("All text:", prompt) |
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input_image = HWC3(input_image) |
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img = resize_image(input_image, image_resolution) |
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H, W, C = img.shape |
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print("Generating SAM seg:") |
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full_segmask, detected_map = get_sam_control( |
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resize_image(input_image, detect_resolution)) |
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detected_map = HWC3(detected_map.astype(np.uint8)) |
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detected_map = cv2.resize( |
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detected_map, (W, H), interpolation=cv2.INTER_LINEAR) |
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control = torch.from_numpy( |
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detected_map.copy()).float().cuda() |
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control = torch.stack([control for _ in range(num_samples)], dim=0) |
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control = einops.rearrange(control, 'b h w c -> b c h w').clone() |
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if seed == -1: |
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seed = random.randint(0, 65535) |
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seed_everything(seed) |
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print("control.shape", control.shape) |
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generator = torch.manual_seed(seed) |
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x_samples = pipe( |
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prompt=[prompt + ', ' + a_prompt] * num_samples, |
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negative_prompt=[n_prompt] * num_samples, |
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num_images_per_prompt=num_samples, |
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num_inference_steps=ddim_steps, |
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generator=generator, |
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height=H, |
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width=W, |
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image=control.type(torch.float16), |
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).images |
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results = [x_samples[i] for i in range(num_samples)] |
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return [full_segmask] + results, prompt |
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if not use_gradio: |
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condition_model = 'shgao/edit-anything-v0-1-1' |
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image_path = "images/sa_309398.jpg" |
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input_image = Image.open(image_path) |
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input_image = np.array(input_image, dtype=np.uint8) |
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prompt = "" |
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a_prompt = 'best quality, extremely detailed' |
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n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality' |
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num_samples = 4 |
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image_resolution = 512 |
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detect_resolution = 512 |
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ddim_steps = 100 |
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guess_mode = False |
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strength = 1.0 |
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scale = 9.0 |
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seed = 10086 |
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eta = 0.0 |
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outputs, full_text = process(condition_model, input_image, prompt, a_prompt, n_prompt, num_samples, |
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image_resolution, |
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detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta) |
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image_list = [] |
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input_image = resize_image(input_image, 512) |
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image_list.append(torch.tensor(input_image)) |
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for i in range(len(outputs)): |
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each = outputs[i] |
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if type(each) is not np.ndarray: |
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each = np.array(each, dtype=np.uint8) |
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each = resize_image(each, 512) |
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print(i, each.shape) |
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image_list.append(torch.tensor(each)) |
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image_list = torch.stack(image_list).permute(0, 3, 1, 2) |
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save_image(image_list, "sample.jpg", nrow=3, |
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normalize=True, value_range=(0, 255)) |
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else: |
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block = gr.Blocks() |
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with block as demo: |
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with gr.Row(): |
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gr.Markdown( |
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"## Generate Anything") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(source='upload', type="numpy") |
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prompt = gr.Textbox(label="Prompt (Optional)") |
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run_button = gr.Button(label="Run") |
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condition_model = gr.Dropdown(choices=list(config_dict.keys()), |
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value=list(config_dict.keys())[0], |
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label='Model', |
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multiselect=False) |
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num_samples = gr.Slider( |
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label="Images", minimum=1, maximum=12, value=1, step=1) |
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enable_auto_prompt = gr.Checkbox(label='Auto generated BLIP2 prompt', value=True) |
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with gr.Accordion("Advanced options", open=False): |
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image_resolution = gr.Slider( |
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label="Image Resolution", minimum=256, maximum=768, value=512, step=64) |
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strength = gr.Slider( |
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label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01) |
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guess_mode = gr.Checkbox(label='Guess Mode', value=False) |
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detect_resolution = gr.Slider( |
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label="SAM Resolution", minimum=128, maximum=2048, value=1024, step=1) |
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ddim_steps = gr.Slider( |
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label="Steps", minimum=1, maximum=100, value=20, step=1) |
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scale = gr.Slider( |
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label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1) |
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seed = gr.Slider(label="Seed", minimum=-1, |
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maximum=2147483647, step=1, randomize=True) |
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eta = gr.Number(label="eta (DDIM)", value=0.0) |
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a_prompt = gr.Textbox( |
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label="Added Prompt", value='best quality, extremely detailed') |
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n_prompt = gr.Textbox(label="Negative Prompt", |
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value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality') |
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with gr.Column(): |
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result_gallery = gr.Gallery( |
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label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto') |
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result_text = gr.Text(label='BLIP2+Human Prompt Text') |
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ips = [condition_model, input_image, enable_auto_prompt, prompt, a_prompt, n_prompt, num_samples, |
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image_resolution, |
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detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta] |
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run_button.click(fn=process, inputs=ips, outputs=[result_gallery, result_text]) |
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return demo |
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if __name__ == '__main__': |
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demo = create_demo() |
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demo.queue().launch(server_name='0.0.0.0') |
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