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| """ | |
| * Copyright (c) 2023 Salesforce, Inc. | |
| * All rights reserved. | |
| * SPDX-License-Identifier: Apache License 2.0 | |
| * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/ | |
| * By Can Qin | |
| * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet | |
| * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala | |
| """ | |
| import config | |
| import cv2 | |
| import einops | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| import random | |
| import os | |
| from annotator.util import resize_image, HWC3 | |
| from utils import create_model | |
| from lib.ddim_hacked import DDIMSampler | |
| from safetensors.torch import load_file as stload | |
| from collections import OrderedDict | |
| from diffusers import StableDiffusionXLImg2ImgPipeline | |
| from PIL import Image | |
| refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-xl-refiner-1.0", | |
| torch_dtype=torch.float16, | |
| ) | |
| refiner.to("cuda") | |
| model = create_model("./models/cldm_v15_unicontrol.yaml").cpu() | |
| model_url = "https://huggingface.co/Robert001/UniControl-Model/resolve/main/unicontrol_v1.1.st" | |
| ckpts_path = "./" | |
| # model_path = os.path.join(ckpts_path, "unicontrol_v1.1.ckpt") | |
| model_path = os.path.join(ckpts_path, "unicontrol_v1.1.st") | |
| if not os.path.exists(model_path): | |
| from basicsr.utils.download_util import load_file_from_url | |
| load_file_from_url(model_url, model_dir=ckpts_path) | |
| model_dict = OrderedDict(stload(model_path, device="cpu")) | |
| model.load_state_dict(model_dict, strict=False) | |
| # model.load_state_dict(load_state_dict(model_path, location='cuda'), strict=False) | |
| model = model.cuda() | |
| ddim_sampler = DDIMSampler(model) | |
| def process_sketch( | |
| input_image, | |
| prompt, | |
| a_prompt, | |
| n_prompt, | |
| num_samples, | |
| ddim_steps, | |
| guess_mode, | |
| strength, | |
| scale, | |
| seed, | |
| eta, | |
| ): | |
| with torch.no_grad(): | |
| input_image = np.array(input_image) | |
| # print all unique values of array | |
| img = 255 - input_image | |
| H, W, C = img.shape | |
| detected_map = cv2.resize(img, (W, H), interpolation=cv2.INTER_LINEAR) | |
| control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0 | |
| control = torch.stack([control for _ in range(num_samples)], dim=0) | |
| control = einops.rearrange(control, "b h w c -> b c h w").clone() | |
| if seed == -1: | |
| seed = random.randint(0, 65535) | |
| # seed_everything(seed) | |
| if config.save_memory: | |
| model.low_vram_shift(is_diffusing=False) | |
| task_dic = {} | |
| task_dic["name"] = "control_hedsketch" | |
| task_instruction = "sketch to image" | |
| task_dic["feature"] = model.get_learned_conditioning(task_instruction)[:, :1, :] | |
| cond = { | |
| "c_concat": [control], | |
| "c_crossattn": [model.get_learned_conditioning([prompt + ", " + a_prompt] * num_samples)], | |
| "task": task_dic, | |
| } | |
| un_cond = { | |
| "c_concat": [control * 0] if guess_mode else [control], | |
| "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)], | |
| } | |
| shape = (4, H // 8, W // 8) | |
| if config.save_memory: | |
| model.low_vram_shift(is_diffusing=True) | |
| model.control_scales = ( | |
| [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13) | |
| ) | |
| samples, intermediates = ddim_sampler.sample( | |
| ddim_steps, | |
| num_samples, | |
| shape, | |
| cond, | |
| verbose=False, | |
| eta=eta, | |
| unconditional_guidance_scale=scale, | |
| unconditional_conditioning=un_cond, | |
| ) | |
| if config.save_memory: | |
| model.low_vram_shift(is_diffusing=False) | |
| x_samples = model.decode_first_stage(samples) | |
| x_samples = ( | |
| (einops.rearrange(x_samples, "b c h w -> b h w c") * 127.5 + 127.5) | |
| .cpu() | |
| .numpy() | |
| .clip(0, 255) | |
| .astype(np.uint8) | |
| ) | |
| result_image = [x_samples[i] for i in range(num_samples)][0] | |
| result_image = Image.fromarray(result_image) | |
| generator = torch.Generator("cuda").manual_seed(seed) | |
| results = [result_image] + [refiner(prompt=prompt, generator=generator, image=result_image).images[0]] | |
| return results | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown("## Sketch to Image") | |
| gr.Markdown( | |
| "This demo is based on [UniControl: ONE compact model for ALL the visual-condition-to-image generation](https://huggingface.co/spaces/Robert001/UniControl-Demo)" | |
| ) | |
| # input_image = gr.Image(source="upload", type="numpy", tool="sketch") | |
| with gr.Row(): | |
| input_image = gr.Sketchpad( | |
| shape=(512, 512), tool="pencil", brush_radius=6, type="pil", image_mode="RGB" | |
| ).style(height=512, width=512) | |
| # input_image = gr.Image(source="upload", type="numpy") | |
| result_gallery = gr.Gallery(label="Output", show_label=False, elem_id="gallery").style( | |
| grid=2, height=512, width=512 | |
| ) | |
| prompt = gr.Textbox(label="Prompt") | |
| run_button = gr.Button(label="Run") | |
| with gr.Accordion("Advanced options", open=False): | |
| num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1) | |
| strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01) | |
| guess_mode = gr.Checkbox(label="Guess Mode", value=False) | |
| detect_resolution = gr.Slider(label="HED Resolution", minimum=128, maximum=1024, value=512, step=1) | |
| ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=35, step=1) | |
| scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1) | |
| seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True) | |
| eta = gr.Number(label="eta (DDIM)", value=0.0) | |
| a_prompt = gr.Textbox(label="Added Prompt", value="best quality, extremely detailed") | |
| n_prompt = gr.Textbox( | |
| label="Negative Prompt", | |
| value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality", | |
| ) | |
| ips = [ | |
| input_image, | |
| prompt, | |
| a_prompt, | |
| n_prompt, | |
| num_samples, | |
| ddim_steps, | |
| guess_mode, | |
| strength, | |
| scale, | |
| seed, | |
| eta, | |
| ] | |
| run_button.click(fn=process_sketch, inputs=ips, outputs=[result_gallery]) | |
| demo.launch(server_name="0.0.0.0") | |