ControlNet/gradio_hough2image.py at main · better319/ControlNet · GitHub

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from share import *

import config

import cv2

import einops

import gradio as gr

import numpy as np

import torch

import random

from pytorch_lightning import seed_everything

from annotator.util import resize_image, HWC3

from annotator.mlsd import MLSDdetector

from cldm.model import create_model, load_state_dict

from cldm.ddim_hacked import DDIMSampler

apply_mlsd = MLSDdetector()

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

model.load_state_dict(load_state_dict('./models/control_sd15_mlsd.pth', location='cuda'))

model = model.cuda()

ddim_sampler = DDIMSampler(model)

def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, value_threshold, distance_threshold):

with torch.no_grad():

input_image = HWC3(input_image)

detected_map = apply_mlsd(resize_image(input_image, detect_resolution), value_threshold, distance_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_NEAREST)

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)

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(np.uint8)

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

return [255 - cv2.dilate(detected_map, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)] + results

block = gr.Blocks().queue()

with block:

with gr.Row():

gr.Markdown("## Control Stable Diffusion with Hough Line 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=64)

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="Hough Resolution", minimum=128, maximum=1024, value=512, step=1)

value_threshold = gr.Slider(label="Hough value threshold (MLSD)", minimum=0.01, maximum=2.0, value=0.1, step=0.01)

distance_threshold = gr.Slider(label="Hough distance threshold (MLSD)", minimum=0.01, maximum=20.0, value=0.1, 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=-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, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, value_threshold, distance_threshold]

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

block.launch(server_name='0.0.0.0')

ControlNet/gradio_hough2image.py at main · better319/ControlNet · GitHub

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