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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')
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