import os from typing import Mapping import gradio as gr import numpy import torch import random from PIL import Image from cldm.model import create_model, load_state_dict from cldm.ddim_hacked import DDIMSampler from laion_face_common import generate_annotation from share import * model = create_model('./models/cldm_v21.yaml').cpu() model.load_state_dict(load_state_dict('./models/controlnet_face_condition_epoch_4_0percent.ckpt', location='cuda')) model = model.cuda() ddim_sampler = DDIMSampler(model) # ControlNet _only_ works with DDIM. def process(input_image: Image.Image, prompt, a_prompt, n_prompt, max_faces, num_samples, ddim_steps, guess_mode, strength, scale, seed, eta): with torch.no_grad(): empty = generate_annotation(input_image, max_faces) visualization = Image.fromarray(empty) # Save to help debug. empty = numpy.moveaxis(empty, 2, 0) # h, w, c -> c, h, w control = torch.from_numpy(empty.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() # Sanity check the dimensions. B, C, H, W = control.shape assert C == 3 assert B == num_samples if seed != -1: random.seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) numpy.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.backends.cudnn.deterministic = True if config.save_memory: model.low_vram_shift(is_diffusing=False) cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]} un_cond = {"c_concat": None 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) # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01 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(numpy.uint8) x_samples = numpy.moveaxis((x_samples * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(numpy.uint8), 1, -1) # b, c, h, w -> b, h, w, c results = [visualization] + [x_samples[i] for i in range(num_samples)] return results block = gr.Blocks().queue() with block: with gr.Row(): gr.Markdown("## Control Stable Diffusion with a Facial Pose") with gr.Row(): with gr.Column(): input_image = gr.Image(source='upload', type="numpy") 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) max_faces = gr.Slider(label="Max Faces", minimum=1, maximum=5, 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) ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, 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') with gr.Column(): result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto') ips = [input_image, prompt, a_prompt, n_prompt, max_faces, num_samples, ddim_steps, guess_mode, strength, scale, seed, eta] run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) block.launch(server_name='0.0.0.0')