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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")
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