ControlNet/gradio_normal2image.py at main · Coderx7/ControlNet · GitHub

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import cv2

import einops

import gradio as gr

import numpy as np

import torch

from cldm.hack import disable_verbosity

disable_verbosity()

from pytorch_lightning import seed_everything

from annotator.util import resize_image, HWC3

from annotator.midas import apply_midas

from cldm.model import create_model, load_state_dict

from ldm.models.diffusion.ddim import DDIMSampler

model = create_model('./models/cldm_v15.yaml').cuda()

model.load_state_dict(load_state_dict('./models/control_sd15_normal.pth', location='cuda'))

ddim_sampler = DDIMSampler(model)

def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, scale, seed, eta, bg_threshold):

with torch.no_grad():

input_image = HWC3(input_image)

_, detected_map = apply_midas(resize_image(input_image, detect_resolution), bg_th=bg_threshold)

detected_map = HWC3(detected_map)

img = resize_image(input_image, image_resolution)

H, W, C = img.shape

detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)

control = torch.from_numpy(detected_map[:, :, ::-1].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()

seed_everything(seed)

cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}

un_cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}

shape = (4, H // 8, W // 8)

samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,

shape, cond, verbose=False, eta=eta,

unconditional_guidance_scale=scale,

unconditional_conditioning=un_cond)

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)

results = [x_samples[i] for i in range(num_samples)]

return [detected_map] + results

block = gr.Blocks().queue()

with block:

with gr.Row():

gr.Markdown("## Control Stable Diffusion with Normal Maps")

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)

image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=256)

detect_resolution = gr.Slider(label="Normal Resolution", minimum=128, maximum=1024, value=384, step=1)

bg_threshold = gr.Slider(label="Normal background threshold", minimum=0.0, maximum=1.0, value=0.4, step=0.01)

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=0, 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, pubic hair,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, num_samples, image_resolution, detect_resolution, ddim_steps, scale, seed, eta, bg_threshold]

run_button.click(fn=process, inputs=ips, outputs=[result_gallery])

block.launch(server_name='0.0.0.0')

ControlNet/gradio_normal2image.py at main · Coderx7/ControlNet · GitHub

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