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gradio_demo.py
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gradio_demo.py
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import gradio as gr
import argparse
from SUPIR.util import HWC3, upscale_image, fix_resize, convert_dtype
import numpy as np
import torch
if torch.cuda.device_count() >= 2:
use_llava = True
else:
use_llava = False
from SUPIR.util import create_SUPIR_model, load_QF_ckpt
from PIL import Image
from llava.llava_agent import LLavaAgent
from CKPT_PTH import LLAVA_MODEL_PATH
import einops
SUPIR_device = 'cuda:0'
LLaVA_device = 'cuda:1'
# load SUPIR
model = create_SUPIR_model('options/SUPIR_v0.yaml').to(SUPIR_device)
ckpt_Q, ckpt_F = load_QF_ckpt('options/SUPIR_v0.yaml')
# load LLaVA
if use_llava:
llava_agent = LLavaAgent(LLAVA_MODEL_PATH, device=LLaVA_device)
else:
llava_agent = None
parser = argparse.ArgumentParser()
parser.add_argument("--ip", type=str, default='0.0.0.0')
parser.add_argument("--port", type=int, default='6688')
args = parser.parse_args()
server_ip = args.ip
server_port = args.port
def stage1_process(input_image, gamma_correction):
LQ = HWC3(input_image)
LQ = fix_resize(LQ, 512)
# stage1
LQ = np.array(LQ) / 255 * 2 - 1
LQ = torch.tensor(LQ, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(SUPIR_device)[:, :3, :, :]
LQ = model.batchify_denoise(LQ)
LQ = (LQ[0].permute(1, 2, 0) * 127.5 + 127.5).cpu().numpy().round().clip(0, 255).astype(np.uint8)
# gamma correction
LQ = LQ / 255.0
LQ = np.power(LQ, gamma_correction)
LQ *= 255.0
LQ = LQ.round().clip(0, 255).astype(np.uint8)
return LQ
def llave_process(input_image, temperature, top_p, qs=None):
if use_llava:
LQ = HWC3(input_image)
LQ = Image.fromarray(LQ.astype('uint8'))
captions = llava_agent.gen_image_caption([LQ], temperature=temperature, top_p=top_p, qs=qs)
else:
captions = ['LLaVA is not available. Please add text manually.']
return captions[0]
def stage2_process(input_image, prompt, a_prompt, n_prompt, num_samples, upscale, edm_steps, s_stage1, s_stage2,
s_cfg, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction,
linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2):
input_image = HWC3(input_image)
input_image = upscale_image(input_image, upscale, unit_resolution=32)
LQ = np.array(input_image) / 255.0
LQ = np.power(LQ, gamma_correction)
LQ *= 255.0
LQ = LQ.round().clip(0, 255).astype(np.uint8)
LQ = LQ / 255 * 2 - 1
LQ = torch.tensor(LQ, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(SUPIR_device)[:, :3, :, :]
captions = [prompt]
model.ae_dtype = convert_dtype(ae_dtype)
model.model.dtype = convert_dtype(diff_dtype)
samples = model.batchify_sample(LQ, captions, num_steps=edm_steps, restoration_scale=s_stage1, s_churn=s_churn,
s_noise=s_noise, cfg_scale=s_cfg, control_scale=s_stage2, seed=seed,
num_samples=num_samples, p_p=a_prompt, n_p=n_prompt, color_fix_type=color_fix_type,
use_linear_CFG=linear_CFG, use_linear_control_scale=linear_s_stage2,
cfg_scale_start=spt_linear_CFG, control_scale_start=spt_linear_s_stage2)
x_samples = (einops.rearrange(samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().round().clip(
0, 255).astype(np.uint8)
results = [x_samples[i] for i in range(num_samples)]
return [input_image] + results
def load_and_reset(model_select, model_info):
_model_select = model_info.replace('<p>', '').replace('</p>', '').replace('Current Model: ', '').strip()
if model_select != _model_select:
if model_select == 'v0-Q':
print('load v0-Q')
model.load_state_dict(ckpt_Q, strict=False)
elif model_select == 'v0-F':
print('load v0-F')
model.load_state_dict(ckpt_F, strict=False)
model_info = model_info.replace(_model_select, model_select)
s_stage2 = 1.0
s_stage1 = -1.0
s_churn = 5
s_noise = 1.003
a_prompt = 'Cinematic, High Contrast, highly detailed, taken using a Canon EOS R camera, hyper detailed photo - ' \
'realistic maximum detail, 32k, Color Grading, ultra HD, extreme meticulous detailing, skin pore ' \
'detailing, hyper sharpness, perfect without deformations.'
n_prompt = 'painting, oil painting, illustration, drawing, art, sketch, oil painting, cartoon, CG Style, ' \
'3D render, unreal engine, blurring, dirty, messy, worst quality, low quality, frames, watermark, ' \
'signature, jpeg artifacts, deformed, lowres, over-smooth'
color_fix_type = 'Wavelet'
spt_linear_CFG = 1.0
spt_linear_s_stage2 = 0.0
if model_select == 'v0-Q':
s_cfg = 7.5
linear_CFG = False
linear_s_stage2 = True
elif model_select == 'v0-F':
s_cfg = 4.0
linear_CFG = True
linear_s_stage2 = False
else:
raise NotImplementedError
return model_info, s_cfg, s_stage2, s_stage1, s_churn, s_noise, a_prompt, n_prompt, color_fix_type, linear_CFG, \
linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2
block = gr.Blocks(title='SUPIR').queue()
with block:
with gr.Row():
gr.Markdown("<center><font size=5>SUPIR Playground</font></center>")
with gr.Row():
with gr.Column():
with gr.Row(equal_height=True):
with gr.Column():
gr.Markdown("<center>Input</center>")
input_image = gr.Image(sources='upload', type="numpy", elem_id="image-input")
with gr.Column():
gr.Markdown("<center>Stage1 Output</center>")
denoise_image = gr.Image(type="numpy", elem_id="image-s1")
prompt = gr.Textbox(label="Prompt", value="")
with gr.Accordion("Stage1 options", open=False):
gamma_correction = gr.Slider(label="Gamma Correction", minimum=0.1, maximum=2.0, value=1.0, step=0.1)
with gr.Accordion("LLaVA options", open=False):
temperature = gr.Slider(label="Temperature", minimum=0., maximum=1.0, value=0.2, step=0.1)
top_p = gr.Slider(label="Top P", minimum=0., maximum=1.0, value=0.7, step=0.1)
qs = gr.Textbox(label="Question", value="Describe this image and its style in a very detailed manner.")
with gr.Accordion("Stage2 options", open=False):
num_samples = gr.Slider(label="Num Samples", minimum=1, maximum=4, value=1, step=1)
upscale = gr.Slider(label="Upscale", minimum=1, maximum=8, value=1, step=1)
edm_steps = gr.Slider(label="Steps", minimum=20, maximum=200, value=50, step=1)
s_cfg = gr.Slider(label="Text Guidance Scale", minimum=1.0, maximum=15.0, value=7.5, step=0.1)
s_stage2 = gr.Slider(label="Stage2 Guidance Strength", minimum=0., maximum=1., value=1., step=0.05)
s_stage1 = gr.Slider(label="Stage1 Guidance Strength", minimum=-1.0, maximum=6.0, value=-1.0, step=1.0)
seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
s_churn = gr.Slider(label="S-Churn", minimum=0, maximum=40, value=5, step=1)
s_noise = gr.Slider(label="S-Noise", minimum=1.0, maximum=1.1, value=1.003, step=0.001)
a_prompt = gr.Textbox(label="Default Positive Prompt",
value='Cinematic, High Contrast, highly detailed, taken using a Canon EOS R '
'camera, hyper detailed photo - realistic maximum detail, 32k, Color '
'Grading, ultra HD, extreme meticulous detailing, skin pore detailing, '
'hyper sharpness, perfect without deformations.')
n_prompt = gr.Textbox(label="Default Negative Prompt",
value='painting, oil painting, illustration, drawing, art, sketch, oil painting, '
'cartoon, CG Style, 3D render, unreal engine, blurring, dirty, messy, '
'worst quality, low quality, frames, watermark, signature, jpeg artifacts, '
'deformed, lowres, over-smooth')
with gr.Row():
with gr.Column():
linear_CFG = gr.Checkbox(label="Linear CFG", value=False)
spt_linear_CFG = gr.Slider(label="CFG Start", minimum=1.0,
maximum=9.0, value=1.0, step=0.5)
with gr.Column():
linear_s_stage2 = gr.Checkbox(label="Linear Stage2 Guidance", value=True)
spt_linear_s_stage2 = gr.Slider(label="Guidance Start", minimum=0.,
maximum=1., value=0., step=0.05)
with gr.Row():
with gr.Column():
diff_dtype = gr.Radio(['fp32', 'fp16', 'bf16'], label="Diffusion Data Type", value="fp16",
interactive=True)
with gr.Column():
ae_dtype = gr.Radio(['fp32', 'bf16'], label="Auto-Encoder Data Type", value="bf16",
interactive=True)
with gr.Column():
color_fix_type = gr.Radio(["None", "AdaIn", "Wavelet"], label="Color-Fix Type", value="Wavelet",
interactive=True)
with gr.Column():
gr.Markdown("<center>Stage2 Output</center>")
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery1", rows=2, columns=1)
with gr.Row():
with gr.Column():
denoise_button = gr.Button(value="Stage1 Run")
with gr.Column():
llave_button = gr.Button(value="LlaVa Run")
with gr.Column():
diffusion_button = gr.Button(value="Stage2 Run")
with gr.Row():
with gr.Column():
model_select = gr.Dropdown(["v0-Q", "v0-F"], interactive=True, label="Model List",
value="v0-Q")
with gr.Column():
restart_button = gr.Button(value="Load & Reset")
model_info = gr.Markdown(f"Current Model: {model_select.value}")
llave_button.click(fn=llave_process, inputs=[denoise_image, temperature, top_p, qs], outputs=[prompt])
denoise_button.click(fn=stage1_process, inputs=[input_image, gamma_correction],
outputs=[denoise_image])
stage2_ips = [input_image, prompt, a_prompt, n_prompt, num_samples, upscale, edm_steps, s_stage1, s_stage2,
s_cfg, seed, s_churn, s_noise, color_fix_type, diff_dtype, ae_dtype, gamma_correction,
linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2]
diffusion_button.click(fn=stage2_process, inputs=stage2_ips, outputs=[result_gallery])
restart_button.click(fn=load_and_reset, inputs=[model_select, model_info],
outputs=[model_info, s_cfg, s_stage2, s_stage1, s_churn, s_noise, a_prompt, n_prompt,
color_fix_type, linear_CFG, linear_s_stage2, spt_linear_CFG, spt_linear_s_stage2])
block.launch(server_name=server_ip, server_port=server_port)