import os
import gradio as gr
from gradio_imageslider import ImageSlider
import argparse
from SUPIR.util import HWC3, upscale_image, fix_resize, convert_dtype
import numpy as np
import torch
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
import copy
import math
import time
import random
import spaces
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", filename="open_clip_pytorch_model.bin", local_dir="laion_CLIP-ViT-bigG-14-laion2B-39B-b160k")
hf_hub_download(repo_id="camenduru/SUPIR", filename="sd_xl_base_1.0_0.9vae.safetensors", local_dir="yushan777_SUPIR")
hf_hub_download(repo_id="camenduru/SUPIR", filename="SUPIR-v0F.ckpt", local_dir="yushan777_SUPIR")
hf_hub_download(repo_id="camenduru/SUPIR", filename="SUPIR-v0Q.ckpt", local_dir="yushan777_SUPIR")
hf_hub_download(repo_id="RunDiffusion/Juggernaut-XL-Lightning", filename="Juggernaut_RunDiffusionPhoto2_Lightning_4Steps.safetensors", local_dir="RunDiffusion_Juggernaut-XL-Lightning")
parser = argparse.ArgumentParser()
parser.add_argument("--opt", type=str, default='options/SUPIR_v0.yaml')
parser.add_argument("--ip", type=str, default='127.0.0.1')
parser.add_argument("--port", type=int, default='6688')
parser.add_argument("--no_llava", action='store_true', default=True)#False
parser.add_argument("--use_image_slider", action='store_true', default=False)#False
parser.add_argument("--log_history", action='store_true', default=False)
parser.add_argument("--loading_half_params", action='store_true', default=False)#False
parser.add_argument("--use_tile_vae", action='store_true', default=True)#False
parser.add_argument("--encoder_tile_size", type=int, default=512)
parser.add_argument("--decoder_tile_size", type=int, default=64)
parser.add_argument("--load_8bit_llava", action='store_true', default=False)
args = parser.parse_args()
use_llava = not args.no_llava
if torch.cuda.device_count() > 0:
if torch.cuda.device_count() >= 2:
SUPIR_device = 'cuda:0'
LLaVA_device = 'cuda:1'
elif torch.cuda.device_count() == 1:
SUPIR_device = 'cuda:0'
LLaVA_device = 'cuda:0'
else:
SUPIR_device = 'cpu'
LLaVA_device = 'cpu'
# load SUPIR
model, default_setting = create_SUPIR_model(args.opt, SUPIR_sign='Q', load_default_setting=True)
if args.loading_half_params:
model = model.half()
if args.use_tile_vae:
model.init_tile_vae(encoder_tile_size=args.encoder_tile_size, decoder_tile_size=args.decoder_tile_size)
model = model.to(SUPIR_device)
model.first_stage_model.denoise_encoder_s1 = copy.deepcopy(model.first_stage_model.denoise_encoder)
model.current_model = 'v0-Q'
ckpt_Q, ckpt_F = load_QF_ckpt(args.opt)
# load LLaVA
if use_llava:
llava_agent = LLavaAgent(LLAVA_MODEL_PATH, device=LLaVA_device, load_8bit=args.load_8bit_llava, load_4bit=False)
else:
llava_agent = None
def update_seed(is_randomize_seed, seed):
if is_randomize_seed:
return random.randint(0, 2147483647)
return seed
def check(input_image):
if input_image is None:
raise gr.Error("Please provide an image to restore.")
def reset_feedback():
return 3, ''
@spaces.GPU(duration=540)
def stage1_process(input_image, gamma_correction):
print('stage1_process ==>>')
if torch.cuda.device_count() == 0:
gr.Warning('Set this space to GPU config to make it work.')
return None, None
torch.cuda.set_device(SUPIR_device)
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, is_stage1=True)
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)
print('<<== stage1_process')
return LQ, gr.update(visible = True)
@spaces.GPU(duration=540)
def llave_process(input_image, temperature, top_p, qs=None):
print('llave_process ==>>')
if torch.cuda.device_count() == 0:
gr.Warning('Set this space to GPU config to make it work.')
return 'Set this space to GPU config to make it work.'
torch.cuda.set_device(LLaVA_device)
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.']
print('<<== llave_process')
return captions[0]
def stage2_process(
noisy_image,
denoise_image,
prompt,
a_prompt,
n_prompt,
num_samples,
min_size,
downscale,
upscale,
edm_steps,
s_stage1,
s_stage2,
s_cfg,
randomize_seed,
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,
model_select,
output_format,
allocation
):
if allocation == 1:
return restore_in_1min(
noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
)
if allocation == 2:
return restore_in_2min(
noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
)
if allocation == 3:
return restore_in_3min(
noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
)
if allocation == 4:
return restore_in_4min(
noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
)
if allocation == 5:
return restore_in_5min(
noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
)
if allocation == 7:
return restore_in_7min(
noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
)
if allocation == 8:
return restore_in_8min(
noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
)
if allocation == 9:
return restore_in_9min(
noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
)
if allocation == 10:
return restore_in_10min(
noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
)
else:
return restore_in_6min(
noisy_image, denoise_image, prompt, a_prompt, n_prompt, num_samples, min_size, downscale, upscale, edm_steps, s_stage1, s_stage2, s_cfg, randomize_seed, 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, model_select, output_format, allocation
)
@spaces.GPU(duration=60)
def restore_in_1min(*args, **kwargs):
return restore(*args, **kwargs)
@spaces.GPU(duration=120)
def restore_in_2min(*args, **kwargs):
return restore(*args, **kwargs)
@spaces.GPU(duration=180)
def restore_in_3min(*args, **kwargs):
return restore(*args, **kwargs)
@spaces.GPU(duration=240)
def restore_in_4min(*args, **kwargs):
return restore(*args, **kwargs)
@spaces.GPU(duration=300)
def restore_in_5min(*args, **kwargs):
return restore(*args, **kwargs)
@spaces.GPU(duration=360)
def restore_in_6min(*args, **kwargs):
return restore(*args, **kwargs)
@spaces.GPU(duration=420)
def restore_in_7min(*args, **kwargs):
return restore(*args, **kwargs)
@spaces.GPU(duration=480)
def restore_in_8min(*args, **kwargs):
return restore(*args, **kwargs)
@spaces.GPU(duration=540)
def restore_in_9min(*args, **kwargs):
return restore(*args, **kwargs)
@spaces.GPU(duration=599)
def restore_in_10min(*args, **kwargs):
return restore(*args, **kwargs)
def restore(
noisy_image,
denoise_image,
prompt,
a_prompt,
n_prompt,
num_samples,
min_size,
downscale,
upscale,
edm_steps,
s_stage1,
s_stage2,
s_cfg,
randomize_seed,
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,
model_select,
output_format,
allocation
):
start = time.time()
print('stage2_process ==>>')
print("noisy_image: " + str(noisy_image))
print("denoise_image: " + str(denoise_image))
print("prompt: " + str(prompt))
print("a_prompt: " + str(a_prompt))
print("n_prompt: " + str(n_prompt))
print("num_samples: " + str(num_samples))
print("min_size: " + str(min_size))
print("downscale: " + str(downscale))
print("upscale: " + str(upscale))
print("edm_steps: " + str(edm_steps))
print("s_stage1: " + str(s_stage1))
print("s_stage2: " + str(s_stage2))
print("s_cfg: " + str(s_cfg))
print("randomize_seed: " + str(randomize_seed))
print("seed: " + str(seed))
print("s_churn: " + str(s_churn))
print("s_noise: " + str(s_noise))
print("color_fix_type: " + str(color_fix_type))
print("diff_dtype: " + str(diff_dtype))
print("ae_dtype: " + str(ae_dtype))
print("gamma_correction: " + str(gamma_correction))
print("linear_CFG: " + str(linear_CFG))
print("linear_s_stage2: " + str(linear_s_stage2))
print("spt_linear_CFG: " + str(spt_linear_CFG))
print("spt_linear_s_stage2: " + str(spt_linear_s_stage2))
print("model_select: " + str(model_select))
print("output_format: " + str(output_format))
print("GPU time allocation: " + str(allocation) + " min")
if torch.cuda.device_count() == 0:
gr.Warning('Set this space to GPU config to make it work.')
return [noisy_image] * 2, [noisy_image] * 2, None, None
if output_format == "input":
if noisy_image is None:
output_format = "png"
else:
output_format = noisy_image.format
if prompt is None:
prompt = ""
if a_prompt is None:
a_prompt = ""
if n_prompt is None:
n_prompt = ""
if prompt != "" and a_prompt != "":
a_prompt = prompt + ", " + a_prompt
else:
a_prompt = prompt + a_prompt
print("Final prompt: " + str(a_prompt))
input_image = noisy_image if denoise_image is None else denoise_image
if 1 < downscale:
input_height, input_width, input_channel = np.array(input_image).shape
input_image = input_image.resize((input_width // downscale, input_height // downscale), Image.LANCZOS)
torch.cuda.set_device(SUPIR_device)
event_id = str(time.time_ns())
event_dict = {'event_id': event_id, 'localtime': time.ctime(), 'prompt': prompt, 'a_prompt': a_prompt,
'n_prompt': n_prompt, 'num_samples': num_samples, 'upscale': upscale, 'edm_steps': edm_steps,
's_stage1': s_stage1, 's_stage2': s_stage2, 's_cfg': s_cfg, 'seed': seed, 's_churn': s_churn,
's_noise': s_noise, 'color_fix_type': color_fix_type, 'diff_dtype': diff_dtype, 'ae_dtype': ae_dtype,
'gamma_correction': gamma_correction, 'linear_CFG': linear_CFG, 'linear_s_stage2': linear_s_stage2,
'spt_linear_CFG': spt_linear_CFG, 'spt_linear_s_stage2': spt_linear_s_stage2,
'model_select': model_select}
if model_select != model.current_model:
print('load ' + model_select)
if model_select == 'v0-Q':
model.load_state_dict(ckpt_Q, strict=False)
elif model_select == 'v0-F':
model.load_state_dict(ckpt_F, strict=False)
model.current_model = model_select
input_image = HWC3(input_image)
input_image = upscale_image(input_image, upscale, unit_resolution=32,
min_size=min_size)
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, :, :]
if use_llava:
captions = [prompt]
else:
captions = ['']
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)]
if args.log_history:
os.makedirs(f'./history/{event_id[:5]}/{event_id[5:]}', exist_ok=True)
with open(f'./history/{event_id[:5]}/{event_id[5:]}/logs.txt', 'w') as f:
f.write(str(event_dict))
f.close()
Image.fromarray(input_image).save(f'./history/{event_id[:5]}/{event_id[5:]}/LQ.png')
for i, result in enumerate(results):
Image.fromarray(result).save(f'./history/{event_id[:5]}/{event_id[5:]}/HQ_{i}.png')
# All the results have the same size
result_height, result_width, result_channel = np.array(results[0]).shape
print('<<== stage2_process')
end = time.time()
secondes = int(end - start)
minutes = math.floor(secondes / 60)
secondes = secondes - (minutes * 60)
hours = math.floor(minutes / 60)
minutes = minutes - (hours * 60)
information = ("Start the process again if you want a different result. " if randomize_seed else "") + \
"Wait " + str(allocation) + " min before a new run to avoid time penalty. " + \
"The image(s) has(ve) been generated in " + \
((str(hours) + " h, ") if hours != 0 else "") + \
((str(minutes) + " min, ") if hours != 0 or minutes != 0 else "") + \
str(secondes) + " sec. " + \
"The new image resolution is " + str(result_width) + \
" pixels large and " + str(result_height) + \
" pixels high, so a resolution of " + f'{result_width * result_height:,}' + " pixels."
print(information)
# Only one image can be shown in the slider
return [noisy_image] + [results[0]], gr.update(format = output_format, value = [noisy_image] + results), gr.update(value = information, visible = True), event_id
def load_and_reset(param_setting):
print('load_and_reset ==>>')
if torch.cuda.device_count() == 0:
gr.Warning('Set this space to GPU config to make it work.')
return None, None, None, None, None, None, None, None, None, None, None, None, None, None
edm_steps = default_setting.edm_steps
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, anime, 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_s_stage2 = 0.0
linear_s_stage2 = False
linear_CFG = True
if param_setting == "Quality":
s_cfg = default_setting.s_cfg_Quality
spt_linear_CFG = default_setting.spt_linear_CFG_Quality
model_select = "v0-Q"
elif param_setting == "Fidelity":
s_cfg = default_setting.s_cfg_Fidelity
spt_linear_CFG = default_setting.spt_linear_CFG_Fidelity
model_select = "v0-F"
else:
raise NotImplementedError
gr.Info('The parameters are reset.')
print('<<== load_and_reset')
return edm_steps, 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, model_select
def log_information(result_gallery):
print('log_information')
if result_gallery is not None:
for i, result in enumerate(result_gallery):
print(result[0])
def on_select_result(result_gallery, evt: gr.SelectData):
print('on_select_result')
return [result_gallery[0][0], result_gallery[evt.index][0]]
def submit_feedback(event_id, fb_score, fb_text):
if args.log_history:
with open(f'./history/{event_id[:5]}/{event_id[5:]}/logs.txt', 'r') as f:
event_dict = eval(f.read())
f.close()
event_dict['feedback'] = {'score': fb_score, 'text': fb_text}
with open(f'./history/{event_id[:5]}/{event_id[5:]}/logs.txt', 'w') as f:
f.write(str(event_dict))
f.close()
return 'Submit successfully, thank you for your comments!'
else:
return 'Submit failed, the server is not set to log history.'
title_html = """
SUPIR
Upscale your images up to x8 freely, without account, without watermark and download it
🤸🤸
This is an online demo of SUPIR, a practicing model scaling for photo-realistic image restoration.
It is still a research project under tested and is not yet a stable commercial product.
The content added by SUPIR is imagination, not real-world information.
The aim of SUPIR is the beauty and the illustration.
Most of the processes only last few minutes.
This demo can handle huge images but the process will be aborted if it lasts more than 9 min.
Please leave a message in discussion if you encounter issues.
Paper Project Page How to play Local Install Guide
"""
claim_md = """
## **Piracy**
The images are not stored but the logs are saved during a month.
## **How to get SUPIR**
You can get SUPIR on HuggingFace by [duplicating this space](https://huggingface.co/spaces/Fabrice-TIERCELIN/SUPIR?duplicate=true) and set GPU.
You can also install SUPIR on your computer following [this tutorial](https://huggingface.co/blog/MonsterMMORPG/supir-sota-image-upscale-better-than-magnific-ai).
## **Terms of use**
By using this service, users are required to agree to the following terms: The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research. Please submit a feedback to us if you get any inappropriate answer! We will collect those to keep improving our models. For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality.
## **License**
The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/Fanghua-Yu/SUPIR) of SUPIR.
"""
# Gradio interface
with gr.Blocks(title="SUPIR") as interface:
if torch.cuda.device_count() == 0:
with gr.Row():
gr.HTML("""
⚠️To use SUPIR, duplicate this space and set a GPU with 30 GB VRAM.
You can't use SUPIR directly here because this space runs on a CPU, which is not enough for SUPIR. This is a template space. Please provide feedback if you have issues.
""")
gr.HTML(title_html)
input_image = gr.Image(label="Input", show_label=True, type="numpy", height=600, elem_id="image-input")
with gr.Group():
prompt = gr.Textbox(label="Image description", info="Help the AI understand what the image represents; describe as much as possible; I advise you to write in English as other languages may not be handled", value="", placeholder="A person, walking, in a town, Summer, photorealistic", lines=3)
prompt_hint = gr.HTML("You can use a LlaVa space to auto-generate the description of your image.")
upscale = gr.Radio([["x1", 1], ["x2", 2], ["x3", 3], ["x4", 4], ["x5", 5], ["x6", 6], ["x7", 7], ["x8", 8]], label="Upscale factor", info="Resolution x1 to x8", value=2, interactive=True)
allocation = gr.Radio([["1 min", 1], ["2 min", 2], ["3 min", 3], ["4 min", 4], ["5 min", 5], ["6 min", 6], ["7 min", 7], ["8 min", 8], ["9 min", 9], ["10 min (may fail)", 10]], label="GPU allocation time", info="lower=May abort run, higher=Time penalty for next runs", value=6, interactive=True)
output_format = gr.Radio([["*.png", "png"], ["*.webp", "webp"], ["*.jpeg", "jpeg"], ["*.gif", "gif"], ["*.bmp", "bmp"]], label="Image format for result", info="File extention", value="png", interactive=True)
with gr.Accordion("Pre-denoising (optional)", open=False):
gamma_correction = gr.Slider(label="Gamma Correction", info = "lower=lighter, higher=darker", minimum=0.1, maximum=2.0, value=1.0, step=0.1)
denoise_button = gr.Button(value="Pre-denoise")
denoise_image = gr.Image(label="Denoised image", show_label=True, type="numpy", sources=[], height=600, elem_id="image-s1")
denoise_information = gr.HTML(value="If present, the denoised image will be used for the restoration instead of the input image.", visible=False)
with gr.Accordion("LLaVA options", open=False, visible=False):
temperature = gr.Slider(label="Temperature", info = "lower=Always similar, higher=More creative", minimum=0., maximum=1.0, value=0.2, step=0.1)
top_p = gr.Slider(label="Top P", info = "Percent of tokens shortlisted", minimum=0., maximum=1.0, value=0.7, step=0.1)
qs = gr.Textbox(label="Question", info="Ask LLaVa what description you want", value="Describe the image and its style in a very detailed manner. The image is a realistic photography, not an art painting.", lines=3)
with gr.Accordion("Advanced options", open=False):
a_prompt = gr.Textbox(label="Additional image description",
info="Completes the main image description",
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.',
lines=3)
n_prompt = gr.Textbox(label="Anti image description",
info="Disambiguate by listing what the image does NOT represent",
value='painting, oil painting, illustration, drawing, art, sketch, anime, '
'cartoon, CG Style, 3D render, unreal engine, blurring, bokeh, ugly, dirty, messy, '
'worst quality, low quality, frames, watermark, signature, jpeg artifacts, '
'deformed, lowres, over-smooth',
lines=3)
edm_steps = gr.Slider(label="Steps", info="lower=faster, higher=more details", minimum=1, maximum=200, value=default_setting.edm_steps if torch.cuda.device_count() > 0 else 1, step=1)
num_samples = gr.Slider(label="Num Samples", info="Number of generated results", minimum=1, maximum=4 if not args.use_image_slider else 1
, value=1, step=1)
min_size = gr.Slider(label="Minimum size", info="Minimum height, minimum width of the result", minimum=32, maximum=4096, value=1024, step=32)
downscale = gr.Radio([["/1", 1], ["/2", 2], ["/3", 3], ["/4", 4], ["/5", 5], ["/6", 6], ["/7", 7], ["/8", 8]], label="Pre-downscale factor", info="Reducing blurred image reduce the process time", value=1, interactive=True)
with gr.Row():
with gr.Column():
model_select = gr.Radio([["💃 Quality (v0-Q)", "v0-Q"], ["🎯 Fidelity (v0-F)", "v0-F"]], label="Model Selection", info="Pretrained model", value="v0-Q",
interactive=True)
with gr.Column():
color_fix_type = gr.Radio(["None", "AdaIn", "Wavelet"], label="Color-Fix Type", info="AdaIn=Improve following a style, Wavelet=For JPEG artifacts", value="Wavelet",
interactive=True)
s_cfg = gr.Slider(label="Text Guidance Scale", info="lower=follow the image, higher=follow the prompt", minimum=1.0, maximum=15.0,
value=default_setting.s_cfg_Quality if torch.cuda.device_count() > 0 else 1.0, step=0.1)
s_stage2 = gr.Slider(label="Restoring Guidance Strength", minimum=0., maximum=1., value=1., step=0.05)
s_stage1 = gr.Slider(label="Pre-denoising Guidance Strength", minimum=-1.0, maximum=6.0, value=-1.0, step=1.0)
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)
with gr.Row():
with gr.Column():
linear_CFG = gr.Checkbox(label="Linear CFG", value=True)
spt_linear_CFG = gr.Slider(label="CFG Start", minimum=1.0,
maximum=9.0, value=default_setting.spt_linear_CFG_Quality if torch.cuda.device_count() > 0 else 1.0, step=0.5)
with gr.Column():
linear_s_stage2 = gr.Checkbox(label="Linear Restoring Guidance", value=False)
spt_linear_s_stage2 = gr.Slider(label="Guidance Start", minimum=0.,
maximum=1., value=0., step=0.05)
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)
randomize_seed = gr.Checkbox(label = "\U0001F3B2 Randomize seed", value = True, info = "If checked, result is always different")
seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True)
with gr.Group():
param_setting = gr.Radio(["Quality", "Fidelity"], interactive=True, label="Presetting", value="Quality")
restart_button = gr.Button(value="Apply presetting")
with gr.Group():
llave_button = gr.Button(value="Generate description by LlaVa (disabled)", visible=False)
diffusion_button = gr.Button(value="🚀 Upscale/Restore", variant = "primary", elem_id="process_button")
restore_information = gr.HTML(value="Restart the process to get another result.", visible=False)
result_slider = ImageSlider(label='Output', show_label=True, elem_id="slider1")
result_gallery = gr.Gallery(label='Output', show_label=True, elem_id="gallery1")
with gr.Accordion("Feedback", open=True, visible=False):
fb_score = gr.Slider(label="Feedback Score", minimum=1, maximum=5, value=3, step=1, interactive=True)
fb_text = gr.Textbox(label="Feedback Text", value="", placeholder='Please enter your feedback here.')
submit_button = gr.Button(value="Submit Feedback")
event_id = gr.Textbox(label="Event ID", value="", visible=False)
gr.Examples(
fn = stage2_process,
inputs = [
input_image,
denoise_image,
prompt,
a_prompt,
n_prompt,
num_samples,
min_size,
downscale,
upscale,
edm_steps,
s_stage1,
s_stage2,
s_cfg,
randomize_seed,
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,
model_select,
output_format,
allocation
],
outputs = [
result_slider,
result_gallery,
restore_information,
event_id
],
examples = [
[
"./Examples/Example1.png",
None,
"Group of people, walking, happy, in the street, photorealistic, 8k, extremely detailled",
"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.",
"painting, oil painting, illustration, drawing, art, sketch, anime, cartoon, CG Style, 3D render, unreal engine, blurring, bokeh, ugly, dirty, messy, worst quality, low quality, frames, watermark, signature, jpeg artifacts, deformed, lowres, over-smooth",
1,
1024,
1,
8,
200,
-1,
1,
7.5,
False,
42,
5,
1.003,
"AdaIn",
"fp16",
"bf16",
1.0,
True,
4,
False,
0.,
"v0-Q",
"png",
6
],
[
"./Examples/Example2.jpeg",
None,
"The head of a tabby cat, in a house, photorealistic, 8k, extremely detailled",
"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.",
"painting, oil painting, illustration, drawing, art, sketch, anime, cartoon, CG Style, 3D render, unreal engine, blurring, bokeh, ugly, dirty, messy, worst quality, low quality, frames, watermark, signature, jpeg artifacts, deformed, lowres, over-smooth",
1,
1024,
1,
1,
200,
-1,
1,
7.5,
False,
42,
5,
1.003,
"Wavelet",
"fp16",
"bf16",
1.0,
True,
4,
False,
0.,
"v0-Q",
"png",
6
],
],
cache_examples = False,
)
with gr.Row():
gr.Markdown(claim_md)
denoise_button.click(fn = check, inputs = [
input_image
], outputs = [], queue = False, show_progress = False).success(fn = stage1_process, inputs = [
input_image,
gamma_correction
], outputs=[
denoise_image,
denoise_information
])
llave_button.click(fn = check, inputs = [
denoise_image
], outputs = [], queue = False, show_progress = False).success(fn = llave_process, inputs = [
denoise_image,
temperature,
top_p,
qs
], outputs = [
prompt
])
diffusion_button.click(fn = update_seed, inputs = [
randomize_seed,
seed
], outputs = [
seed
], queue = False, show_progress = False).then(fn = check, inputs = [
input_image
], outputs = [], queue = False, show_progress = False).success(fn = reset_feedback, inputs = [], outputs = [
fb_score,
fb_text
], queue = False, show_progress = False).success(fn=stage2_process, inputs = [
input_image,
denoise_image,
prompt,
a_prompt,
n_prompt,
num_samples,
min_size,
downscale,
upscale,
edm_steps,
s_stage1,
s_stage2,
s_cfg,
randomize_seed,
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,
model_select,
output_format,
allocation
], outputs = [
result_slider,
result_gallery,
restore_information,
event_id
]).success(fn = log_information, inputs = [
result_gallery
], outputs = [], queue = False, show_progress = False)
result_gallery.select(on_select_result, result_gallery, result_slider)
restart_button.click(fn = load_and_reset, inputs = [
param_setting
], outputs = [
edm_steps,
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,
model_select
])
submit_button.click(fn = submit_feedback, inputs = [
event_id,
fb_score,
fb_text
], outputs = [
fb_text
])
interface.queue(10).launch() 