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amd_webui.py
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amd_webui.py
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import gradio as gr
from diffusers import OnnxStableDiffusionPipeline, OnnxStableDiffusionImg2ImgPipeline
from huggingface_hub import _login
from huggingface_hub.hf_api import HfApi, HfFolder
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
import subprocess
import sys
import pathlib
import importlib.util
import numpy as np
import random
import datetime
from PIL import Image
import onnxruntime
import pprint
pprint.pprint(onnxruntime.get_available_providers())
#from modules import txt2img
python = sys.executable
#repositories = pathlib.Path().absolute() / 'repositories'
onnx_dir = pathlib.Path().absolute()/'onnx_models'
output_dir = pathlib.Path().absolute()/'output'
if not output_dir.exists():
output_dir.mkdir(parents=True, exist_ok=True)
#from PIL import Image
#baseImage = Image.open(r"in.jpg").convert("RGB") # opens an image directly from the script's location and converts to RGB color profile
#baseImage = baseImage.resize((768,512))
prompt = "A fantasy landscape, trending on artstation"
#denoiseStrength = 0.8 # a float number from 0 to 1 - decreasing this number will increase result similarity with baseImage
#scale = 7.5
#pipe = None
##need to set up UI for downloading weights
lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", steps_offset=1)
def txt2img(prompt, negative_prompt, steps, height, width, scale, denoise_strength=0, seed=None, scheduler=lms, num_image=None):
try:
seed = int(seed)
if seed < 0:
seed = random.randint(0,4294967295)
except:
seed = random.randint(0, 4294967295) # 2^32
generator = np.random.RandomState(seed)
image = pipe(prompt,
negative_prompt = negative_prompt,
num_inference_steps=steps,
height = height,
width = width,
guidance_scale=scale,
#strength=denoise_strength,
generator = generator,
num_images_per_prompt = num_image
).images[0]
## autosave img
img_name = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M') + ".png"
image.save(output_dir/img_name)
return image
def img2img(prompt, negative_prompt, image_input, steps, height, width, scale, denoise_strength, seed=None, scheduler=None, num_image=None):
if seed == '':
seed = random.randint(0,4294967295)
elif seed != '':
seed = int(seed)
if seed < 0:
seed = random.randint(0,4294967295)
print(f'this is the seed {seed}')
generator = np.random.RandomState(seed)
print(image_input)
image = pipe_img2img(prompt=prompt,
image = Image.fromarray(image_input),
strength=denoise_strength,
num_inference_steps=steps,
guidance_scale=scale,
negative_prompt = negative_prompt,
num_images_per_prompt = num_image,
generator = generator,
#height = height,
#width = width
).images[0]
## autosave img
img_name = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M') + ".png"
image.save(output_dir/img_name)
return image
def huggingface_login(token):
try:
#output = _login._login(HfApi(), token = token)
output = _login._login(token = token, add_to_git_credential = True)
return "Login successful."
except Exception as e:
return str(e)
def pip_install(lib):
subprocess.run(f'echo Installing {lib}...', shell=True)
if 'onnxruntime-directml' in lib:
subprocess.run(f'echo 1', shell=True)
subprocess.run(f'echo "{python}" -m pip install {lib}', shell=True)
subprocess.run(f'"{python}" -m pip install {lib} --force-reinstall', shell=True)
else:
subprocess.run(f'echo 2', shell=True)
subprocess.run(f'echo "{python}" -m pip install {lib}', shell=True, capture_output=True)
subprocess.run(f'"{python}" -m pip install {lib}', shell=True, capture_output=True)
def pip_uninstall(lib):
subprocess.run(f'echo Uninstalling {lib}...', shell=True)
subprocess.run(f'"{python}" -m pip uninstall -y {lib}', shell=True, capture_output=True)
def is_installed(lib):
library = importlib.util.find_spec(lib)
return (library is not None)
def download_sd_model(model_path):
pip_install('onnx')
print('abc')
from conv import convert_models
print('ttt')
onnx_opset = 14
onnx_fp16 = False
try:
model_name = model_path.split('/')[1]
except:
model_name = model_path
onnx_model_dir = onnx_dir/model_name
if not onnx_dir.exists():
onnx_dir.mkdir(parents=True, exist_ok=True)
print(model_name)
convert_models(model_path, str(onnx_model_dir), onnx_opset, onnx_fp16)
pip_uninstall('onnx')
#'CompVis/stable-diffusion-v1-4'
def display_onnx_models():
if not onnx_dir.exists():
onnx_dir.mkdir(parents=True, exist_ok=True)
return [m.name for m in onnx_dir.iterdir() if m.is_dir()]
def load_onnx_model(model):
## if is_installed('onnx'):
## pip_uninstall('onnx')
## onnx_nightly = pathlib.Path().absolute()/'repositories/ort_nightly_directml-1.13.0.dev20220908001-cp39-cp39-win_amd64.whl'
## pip_install(str(onnx_nightly))
## subprocess.run('echo installing onnx nightly built', shell=True)
global pipe
pipe = OnnxStableDiffusionPipeline.from_pretrained(str(onnx_dir/model),
safety_checker = None,
provider="DmlExecutionProvider",
)
return 'model ready'
def load_onnx_model_i2i(model):
## if is_installed('onnx'):
## pip_uninstall('onnx')
## onnx_nightly = pathlib.Path().absolute()/'repositories/ort_nightly_directml-1.13.0.dev20220908001-cp39-cp39-win_amd64.whl'
## pip_install(str(onnx_nightly))
## subprocess.run('echo installing onnx nightly built', shell=True)
global pipe_img2img
pipe_img2img = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(str(onnx_dir/model),
safety_checker = None,
provider="DmlExecutionProvider")
return 'model ready'
def start_app():
with gr.Blocks() as app:
gr.Markdown('STABLE DIFFUSION WEBUI FOR AMD')
with gr.Tab('txt2img'):
txt2img_prompt_input = gr.Textbox(label='Prompt')
txt2img_negative_prompt_input = gr.Textbox(label='Negative Prompt')
with gr.Row():
with gr.Column(scale = 1):
with gr.Row():
txt2img_model_input = gr.Dropdown(label='Select a model', choices = display_onnx_models())
test_output = gr.Textbox(label='testing output')
inference_step_input = gr.Slider(label='Steps', value = 30, minimum = 0, maximum=200, step = 1)
with gr.Row():
image_height = gr.Slider(label='Height', value = 512, minimum = 0, maximum=1080, step = 64)
image_width = gr.Slider(label='Width', value = 512, minimum = 0, maximum=1080, step = 64)
with gr.Row():
scale = gr.Slider(label='Scale', value = 7.5, minimum = 0, maximum=15, step = 0.1)
denoise_strength = gr.Slider(label='Denoise Strength', value = 1, minimum = 0, maximum=1, step = 0.1)
with gr.Row():
seed_input = gr.Textbox(label='Enter Seed here')
scheduler_input = gr.Dropdown(['option 1', 'option 2'])
num_image = gr.Slider(label='Num. of Images', value = 1, minimum = 1, maximum=10, step = 1)
#prompt_input_negative = gr.Textbox()
with gr.Row():
#load_onnx_model_button = gr.Button('Load Model')
txt2img_button = gr.Button('Generate')
txt2img_output = gr.Image(label='Output Image')
with gr.Tab('img2img'):
img2img_prompt_input = gr.Textbox(label='Prompt')
img2img_negative_prompt_input = gr.Textbox(label='Negative Prompt')
with gr.Row():
img2img_image_input = gr.Image()
img2img_image_output = gr.Image(label='img2img output')
with gr.Row():
img2img_model_input = gr.Dropdown(label='Select a model', choices = display_onnx_models())
img2img_test_output = gr.Textbox(label='testing output')
img2img_inference_step_input = gr.Slider(label='Steps', value = 30, minimum = 0, maximum=200, step = 1)
with gr.Row():
img2img_image_height = gr.Slider(label='Height', value = 512, minimum = 0, maximum=1080, step = 64)
img2img_image_width = gr.Slider(label='Width', value = 512, minimum = 0, maximum=1080, step = 64)
with gr.Row():
img2img_scale = gr.Slider(label='Scale', value = 7.5, minimum = 0, maximum=15, step = 0.1)
img2img_denoise_strength = gr.Slider(label='Denoise Strength', value = 1, minimum = 0, maximum=1, step = 0.1)
with gr.Row():
with gr.Row():
img2img_seed_input = gr.Textbox(label='Enter Seed here')
img2img_num_image = gr.Slider(label='Num. of Images', value = 1, minimum = 1, maximum=10, step = 1)
img2img_scheduler_input = gr.Dropdown(['option 1', 'option 2'])
img2img_button = gr.Button('Generate')
with gr.Tab('Model Manager'):
gr.Markdown("Some of the models require you logging in to Huggingface and agree to their terms. Make sure to do that before downloading models!")
model_download_input = gr.Textbox()
model_download_button = gr.Button('Download Model')
model_dir_refresh = gr.Button('Refresh')
with gr.Tab('Settings'):
gr.HTML("Click on this link to find your <a href='https://huggingface.co/settings/tokens' style='color:blue'>Huggingface Access Token</a>")
hugginface_token_input = gr.Textbox()
huggingface_login_message = gr.Textbox()
huggingface_login_button = gr.Button('Login HuggingFace')
txt2img_button.click(txt2img, inputs=[txt2img_prompt_input, txt2img_negative_prompt_input, inference_step_input,
image_height, image_width, scale, denoise_strength,
seed_input, scheduler_input,
num_image], outputs = txt2img_output)
#img2img_button.click(img2img, inputs = [img2img_prompt_input, img2img_image_input], outputs= img2img_image_output, show_progress=True)
img2img_button.click(img2img, inputs=[img2img_prompt_input, img2img_negative_prompt_input, img2img_image_input, img2img_inference_step_input,
img2img_image_height, img2img_image_width, img2img_scale, img2img_denoise_strength,
img2img_seed_input, img2img_scheduler_input, img2img_num_image
], outputs = img2img_image_output, show_progress=True)
huggingface_login_button.click(huggingface_login,
inputs = hugginface_token_input,
outputs = huggingface_login_message)
model_download_button.click(download_sd_model, inputs = model_download_input)
#load_onnx_model_button.click(load_onnx_model, inputs=txt2img_model_input, show_progress=True, outputs = test_output)
txt2img_model_input.change(load_onnx_model, inputs=txt2img_model_input, show_progress=True, outputs = test_output)
img2img_model_input.change(load_onnx_model_i2i, inputs=img2img_model_input, show_progress=True, outputs = img2img_test_output)
app.launch(inbrowser = True)
if __name__ == "__main__":
start_app()