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import gradio as gr
from novita_client import *
import logging
import random
import traceback
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(filename)s(%(lineno)d) %(message)s')
first_stage_activication_words = "a ohwx"
second_stage_activication_words = "a closeup photo of ohwx"
first_stage_lora_scale_default = 0.3
second_stage_lora_scale_default = 1.0
suggestion_checkpoints = [
"dreamshaper_8_93211.safetensors",
"epicrealism_pureEvolutionV5_97793.safetensors",
"v1-5-pruned-emaonly.safetensors",
"majichenmixrealistic_v10_85701.safetensors",
"realisticVisionV51_v51VAE_94301.safetensors",
"WFChild_v1.0.ckpt",
"chilloutmix_NiPrunedFp32Fix.safetensors",
"darkSushiMixMix_225D_64380.safetensors",
"dreamshaperXL09Alpha_alpha2Xl10_91562.safetensors",
"protovisionXLHighFidelity3D_release0630Bakedvae_154359.safetensors",
"sd_xl_base_1.0.safetensors",
"sdxlUnstableDiffusers_v11_216694.safetensors",
"realisticVisionV40_v40VAE_81510.safetensors",
"xxmix9realistic_v10_37818.safetensors"
]
base_checkpoints = ["epicrealism_naturalSin_121250", "v1-5-pruned-emaonly", "WFChild_v1.0", "majichenmixrealistic_v10",
"realisticVisionV51_v51VAE_94301", "chilloutmix_NiPrunedFp32Fix", "darkSushiMixMix_225D_64380",
"stable-diffusion-xl-base-1.0", "protovisionXLHighFidelity3D_release0630Bakedvae_154359", "sdxlUnstableDiffusers_v11_216694", "realisticVisionV40_v40VAE_81510", "xxmix9realistic_v10_37818", "epicrealismXL_v10_247189"]
get_local_storage = """
function() {
globalThis.setStorage = (key, value)=>{
localStorage.setItem(key, JSON.stringify(value))
}
globalThis.getStorage = (key, value)=>{
return JSON.parse(localStorage.getItem(key))
}
const novita_key = getStorage('novita_key')
return [novita_key];
}
"""
def get_noviata_client(novita_key):
client = NovitaClient(novita_key, os.getenv('NOVITA_API_URI', None))
client.set_extra_headers({"User-Agent": "stylization-playground"})
return client
def create_ui():
with gr.Blocks() as demo:
gr.HTML('''
<a href="https://novita.ai/?utm_source=huggingface&utm_medium=face-stylization&utm_campaign=face-stylization">
<img src="https://raw.githubusercontent.com/wiki/novitalabs/sd-webui-cleaner/images/logo2.png" width="120px;" alt="Unsplash" />
</a>
<h1>Face Stylization Playground</h1>
<h3>Start integrate with <a href="https://novita.ai/get-started/Model_training.html?utm_source=huggingface&utm_medium=face-stylization&utm_campaign=face-stylization">Model_training API</a>
<h3> Get Novita.AI API Key from <a href="https://novita.ai/get-started/Account_account_and_key.html?utm_source=huggingface&utm_medium=face-stylization&utm_campaign=face-stylization">Novita.AI</a></h2>
'''
)
free_trial_notice = gr.HTML('', visible=False)
with gr.Row():
with gr.Column(scale=1):
novita_key = gr.Textbox(value="", label="Novita.AI API KEY (store in broweser)", placeholder="novita.ai api key", type="password")
with gr.Column(scale=1):
user_balance = gr.Textbox(label="User Balance", value="0.0")
with gr.Tab(label="Subject Training"):
with gr.Row():
with gr.Column(scale=1):
training_subject_base_model = gr.Dropdown(choices=base_checkpoints, label="Base Model", value=base_checkpoints[0])
training_subject_geneder = gr.Radio(choices=["man", "woman", "person"], value="man", label="Geneder")
training_subject_name = gr.Text(label="Training Name", placeholder="training name", elem_id="training_name", value="my-face-001")
training_subject_max_train_steps = gr.Slider(minimum=200, maximum=4000, step=1, label="Max Train Steps", value=2000)
training_subject_images = gr.File(file_types=["image"], file_count="multiple", label="6-10 face images.")
training_subject_button = gr.Button(value="Train")
training_subject_payload = gr.JSON(label="Training Payload, POST /v3/training/subject")
with gr.Column(scale=1):
training_subject_refresh_button = gr.Button(value="Refresh Training Status")
training_subject_refresh_json = gr.JSON()
def on_upload_training_subject_images(files):
if files is None:
return 2000
return min(2000, len(files) * 200)
training_subject_images.change(
inputs=[training_subject_images],
outputs=training_subject_max_train_steps,
fn=on_upload_training_subject_images,
)
def train_subject(novita_key, base_model, training_name, training_subject_geneder, max_train_steps, training_images):
training_images = [_.name for _ in training_images]
try:
get_noviata_client(novita_key).create_training_subject(
base_model=base_model,
name=training_name,
instance_prompt=f"a closeup photo of ohwx person",
class_prompt="person",
max_train_steps=max_train_steps,
images=training_images,
components=FACE_TRAINING_DEFAULT_COMPONENTS,
learning_rate=3e-4,
seed=None,
lr_scheduler='cosine_with_restarts',
with_prior_preservation=True,
prior_loss_weight=1.0,
lora_r=32,
lora_alpha=32,
lora_text_encoder_r=32,
lora_text_encoder_alpha=32,
)
payload = dict(
name=training_name,
base_model=base_model,
image_dataset_items=["....assets_ids, please manually upload to novita.ai"],
expert_setting=TrainingExpertSetting(
instance_prompt=f"a closeup photo of ohwx person",
class_prompt="person",
max_train_steps=max_train_steps,
learning_rate="3e-4",
seed=None,
lr_scheduler='cosine_with_restarts',
with_prior_preservation=True,
prior_loss_weight=1.0,
lora_r=32,
lora_alpha=32,
lora_text_encoder_r=32,
lora_text_encoder_alpha=32,
),
components=[_.to_dict() for _ in FACE_TRAINING_DEFAULT_COMPONENTS],
)
except Exception as e:
logging.error(e)
raise gr.Error(traceback.format_exc())
return gr.update(value=get_noviata_client(novita_key).list_training("subject").sort_by_created_at()), payload
training_subject_refresh_button.click(
inputs=[novita_key],
outputs=training_subject_refresh_json,
fn=lambda novita_key: gr.update(value=get_noviata_client(novita_key).list_training("subject").sort_by_created_at())
)
training_subject_button.click(
inputs=[novita_key, training_subject_base_model, training_subject_name, training_subject_geneder, training_subject_max_train_steps, training_subject_images],
outputs=[training_subject_refresh_json, training_subject_payload],
fn=train_subject
)
with gr.Tab(label="Style Training"):
with gr.Row():
with gr.Column(scale=1):
training_style_base_model = gr.Dropdown(choices=base_checkpoints, label="Base Model", value="v1-5-pruned-emaonly")
training_style_name = gr.Text(label="Training Name", placeholder="training name", elem_id="training_name", value="my-style-001")
training_style_max_train_steps = gr.Slider(minimum=200, maximum=10000, step=1, label="Max Train Steps", value=2000)
training_style_images_and_captions = gr.File(file_types=["image", ".txt", ".caption"], file_count="multiple", label="10-50 style images.")
training_style_button = gr.Button(value="Train")
training_style_payload = gr.JSON(label="Training Payload, POST /v3/training/style")
with gr.Column(scale=1):
training_style_refresh_button = gr.Button(value="Refresh Training Status")
training_style_refresh_json = gr.JSON()
def on_upload_training_style_images(files):
if files is None:
return 2000
caption_files = [f for f in files if f.name.endswith(".caption") or f.name.endswith(".txt")]
image_files = [f for f in files if not f.name.endswith(".caption") and not f.name.endswith(".txt")]
if len(caption_files) != len(image_files):
raise gr.Error("caption files and image files must be same length")
return min(2000, len(image_files) * 200)
training_style_images_and_captions.change(
inputs=[training_style_images_and_captions],
outputs=training_style_max_train_steps,
fn=on_upload_training_style_images,
)
def train_style(novita_key, base_model, training_name, max_train_steps, training_style_images_and_captions):
files = [_.name for _ in training_style_images_and_captions]
images_captions = {}
for f in files:
basename = os.path.basename(f).rsplit(".", 1)[0]
if basename not in images_captions:
images_captions[basename] = {}
if f.endswith(".caption") or f.endswith(".txt"):
images_captions[basename]["caption"] = f
else:
images_captions[basename]["image"] = f
for k, v in images_captions.items():
if len(v) != 2:
raise gr.Error(f"image and caption must be provided for {k}")
images = []
captions = []
for _, v in images_captions.items():
if "image" in v:
images.append(v["image"])
if "caption" in v:
with open(v["caption"], "r") as f:
captions.append(f.read().strip())
get_noviata_client(novita_key).create_training_style(
name=training_name,
base_model=base_model,
max_train_steps=max_train_steps,
images=images,
captions=captions,
learning_rate=1e-4,
)
payload = dict(
name=training_name,
base_model=base_model,
image_dataset_items=["....assets_ids, please manually upload to novita.ai"],
expert_setting=TrainingExpertSetting(
learning_rate=1e-4,
),
)
return gr.update(value=get_noviata_client(novita_key).list_training("style").sort_by_created_at()), payload
training_style_button.click(
inputs=[novita_key, training_style_base_model, training_style_name, training_style_max_train_steps, training_style_images_and_captions],
outputs=[training_style_refresh_json, training_style_payload],
fn=train_style
)
training_style_refresh_button.click(
inputs=[novita_key],
outputs=training_style_refresh_json,
fn=lambda novita_key: gr.update(value=get_noviata_client(novita_key).list_training("style").sort_by_created_at())
)
with gr.Tab(label="Subject Inferencing"):
with gr.Row():
with gr.Column(scale=1):
style_prompt = gr.TextArea(lines=3, label="Style Prompt")
style_negative_prompt = gr.TextArea(lines=3, label="Style Negative Prompt")
style_gender = gr.Radio(choices=["man", "woman", "person"], value="man", label="Gender")
style_model = gr.Dropdown(choices=suggestion_checkpoints, label="Style Model")
style_lora = gr.Dropdown(choices=[], label="Style LoRA", type="index")
_hide_lora_training_response = gr.JSON(visible=False)
style_height = gr.Slider(minimum=1, maximum=1024, step=1, label="Style Height", value=512)
style_width = gr.Slider(minimum=1, maximum=1024, step=1, label="Style Width", value=512)
style_method = gr.Radio(choices=["txt2img", "controlnet-depth", "controlnet-pose", "controlnet-canny",
"controlnet-lineart", "controlnet-scribble", "controlnet-tile"], label="Style Method")
style_advanced = gr.Checkbox(label="Advanced")
with gr.Column(scale=1, visible=False) as style_advanced_tab:
first_stage_seed = gr.Slider(minimum=-1, maximum=1000000, step=1, label="First Stage Seed", value=-1)
second_stage_seed = gr.Slider(minimum=-1, maximum=1000000, step=1, label="Second Stage Seed", value=-1)
first_stage_lora_scale = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="First Stage LoRA Scale", value=0.3)
second_stage_lora_scale = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Second Stage LoRA Scale", value=1.0)
second_stage_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Second Stage Strength", value=0.3)
second_stage_steps = gr.Slider(minimum=1, maximum=100, step=1, label="Second Stage Steps", value=20)
controlnet_weight = gr.Slider(minimum=0.0, maximum=2.0, step=0.01, label="Controlnet Weight", value=1.0)
style_advanced.change(inputs=[style_advanced], outputs=[style_advanced_tab], fn=lambda v: gr.update(visible=v))
style_reference_image = gr.Image(label="Style Reference Image", height=512)
with gr.Column(scale=1):
inference_refresh_button = gr.Button(value="Refresh Style LoRA")
generate_button = gr.Button(value="Generate")
num_images = gr.Slider(minimum=1, maximum=10, step=1, label="Num Images", value=1)
gallery = gr.Gallery(label="Gallery", height="auto", object_fit="scale-down", show_share_button=False)
def inference_refresh_button_fn(novita_key):
# trained_loras_models = [_.name for _ in get_noviata_client(novita_key).models_v3(refresh=True).filter_by_type("lora").filter_by_visibility("private")]
serving_models = [_.models[0].model_name for _ in get_noviata_client(novita_key).list_training("subject").filter_by_model_status("SERVING")]
serving_models_labels = [_.task_name for _ in get_noviata_client(novita_key).list_training("subject").filter_by_model_status("SERVING")]
default_serving_model = serving_models_labels[0] if len(serving_models_labels) > 0 else None
return gr.update(choices=serving_models_labels, value=default_serving_model), gr.update(value=serving_models)
inference_refresh_button.click(
inputs=[novita_key],
outputs=[style_lora, _hide_lora_training_response],
fn=inference_refresh_button_fn
)
first_stage_request_body = gr.JSON(label="First Stage Request Body, POST /api/v2/txt2img")
templates = [
{
"style_prompt": "(masterpiece), (extremely intricate:1.3), (realistic), portrait of a person, the most handsome in the world, (medieval armor), metal reflections, upper body, outdoors, intense sunlight, far away castle, professional photograph of a stunning person detailed, sharp focus, dramatic, award winning, cinematic lighting, octane render unreal engine, volumetrics dtx, (film grain, blurry background, blurry foreground, bokeh, depth of field, sunset, motion blur:1.3), chainmail",
"style_negative_prompt": "BadDream_53202, UnrealisticDream_53204",
"style_gender": "man",
"style_model": "dreamshaper_8_93211.safetensors",
"style_method": "txt2img",
"style_height": 768,
"style_width": 512,
"style_reference_image": "./00001.jpg",
},
{
"style_prompt": "photo of beautiful age 18 girl, pastel hair, freckles sexy, beautiful, close up, young, dslr, 8k, 4k, ultrarealistic, realistic, natural skin, textured skin",
"style_negative_prompt": "BadDream_53202, UnrealisticDream_53204",
"style_gender": "woman",
"style_model": "dreamshaper_8_93211.safetensors",
"style_method": "controlnet-depth",
"style_height": 768,
"style_width": 512,
"style_reference_image": "./00002.jpg",
},
{
"style_prompt": "majesty, holy, saintly, godly, 1girl, an angel descending from heaven, upper body, beautiful asian goddess, looking at viewer, detail face and eyes, symmetrical eyes, glowing white eye, warm attitude, long hair, blonde hair, floating hair, royal clothes, gold armor, feathered wings, glowing wings, nice hands",
"style_negative_prompt": "BadDream_53202, UnrealisticDream_53204",
"style_gender": "woman",
"style_model": "dreamshaper_8_93211.safetensors",
"style_method": "controlnet-canny",
"style_height": 768,
"style_width": 512,
"style_reference_image": "./00003.jpg",
},
{
"style_prompt": "Iron Man, close up, digital art, character concept, magical realism, warm golden light, sunset, Marvel Cinematic Universe, Hogwarts, high-resolution, vibrant colors, realistic rendering, key art, fantasy fashion, Iron Man suit integration, elegant and powerful pose",
"style_negative_prompt": "BadDream_53202, UnrealisticDream_53204",
"style_gender": "man",
"style_model": "dreamshaper_8_93211.safetensors",
"style_method": "controlnet-depth",
"style_height": 768,
"style_width": 512,
"style_reference_image": "./00004.jpg",
}
]
def mirror(*args):
return args
with gr.Row():
examples = gr.Examples(
[
[
_.get("style_prompt", ""),
_.get("style_negative_prompt", ""),
_.get("style_gender", "man"),
_.get("style_model", ""),
_.get("style_height", 512),
_.get("style_width", 512),
_.get("style_method", "txt2img"),
_.get("style_reference_image", ""),
] for _ in templates
],
[
style_prompt,
style_negative_prompt,
style_gender,
style_model,
style_height,
style_width,
style_method,
style_reference_image,
],
[
style_prompt,
style_negative_prompt,
style_gender,
style_model,
style_height,
style_width,
style_method,
style_reference_image,
],
mirror,
cache_examples=False,
)
def generate(novita_key,
style_gender,
style_prompt,
style_negative_prompt,
style_model,
style_lora,
_hide_lora_training_response,
style_hegiht,
style_width,
style_method,
style_reference_image,
first_stage_seed,
second_stage_seed,
first_stage_lora_scale,
second_stage_lora_scale,
second_stage_strength,
second_stage_steps,
controlnet_weight,
num_images):
style_reference_image = Image.fromarray(style_reference_image)
def style(style_method,
style_gender,
style_prompt,
style_negative_prompt,
style_model,
style_lora,
_hide_lora_training_response,
style_hegiht,
style_width,
style_reference_image,
first_stage_seed,
second_stage_seed,
first_stage_lora_scale,
second_stage_lora_scale,
second_stage_strength,
second_stage_steps,
controlnet_weight,
num_images
):
if isinstance(style_lora, int):
style_lora = _hide_lora_training_response[style_lora].replace(".safetensors", "")
else:
style_lora = style_lora.replace(".safetensors", "")
height = int(style_hegiht)
width = int(style_width)
if first_stage_seed == -1:
first_stage_seed = random.randint(1, 2 ** 32 - 1)
if second_stage_seed == -1:
second_stage_seed = random.randint(1, 2 ** 32 - 1)
activication_words = f"{first_stage_activication_words} {style_gender}"
style_prompt = f"{activication_words}, <lora:{style_lora}:{first_stage_lora_scale}>, {style_prompt}"
if style_method == "txt2img":
req = Txt2ImgRequest(
prompt=style_prompt,
negative_prompt=style_negative_prompt,
width=width,
height=height,
model_name=style_model,
steps=30,
sampler_name=Samplers.DPMPP_M_KARRAS,
seed=first_stage_seed,
)
elif style_method == "controlnet-depth":
req = Txt2ImgRequest(
prompt=style_prompt,
negative_prompt=style_negative_prompt,
width=width,
height=height,
model_name=style_model,
steps=30,
sampler_name=Samplers.DPMPP_M_KARRAS,
seed=first_stage_seed,
controlnet_units=[
ControlnetUnit(
input_image=image_to_base64(style_reference_image),
control_mode=ControlNetMode.BALANCED,
model="control_v11f1p_sd15_depth",
module=ControlNetPreprocessor.DEPTH,
resize_mode=ControlNetResizeMode.RESIZE_OR_CORP,
weight=controlnet_weight,
)
]
)
elif style_method == "controlnet-pose":
req = Txt2ImgRequest(
prompt=style_prompt,
negative_prompt=style_negative_prompt,
width=width,
height=height,
model_name=style_model,
steps=30,
sampler_name=Samplers.DPMPP_M_KARRAS,
seed=first_stage_seed,
controlnet_units=[
ControlnetUnit(
input_image=image_to_base64(style_reference_image),
control_mode=ControlNetMode.BALANCED,
model="control_v11p_sd15_openpose",
module=ControlNetPreprocessor.OPENPOSE,
resize_mode=ControlNetResizeMode.RESIZE_OR_CORP,
weight=controlnet_weight,
)
]
)
elif style_method == "controlnet-canny":
req = Txt2ImgRequest(
prompt=style_prompt,
negative_prompt=style_negative_prompt,
width=width,
height=height,
model_name=style_model,
steps=30,
sampler_name=Samplers.DPMPP_M_KARRAS,
seed=first_stage_seed,
controlnet_units=[
ControlnetUnit(
input_image=image_to_base64(style_reference_image),
control_mode=ControlNetMode.BALANCED,
model="control_v11p_sd15_canny",
module=ControlNetPreprocessor.CANNY,
resize_mode=ControlNetResizeMode.RESIZE_OR_CORP,
weight=controlnet_weight,
)
]
)
elif style_method == "controlnet-lineart":
req = Txt2ImgRequest(
prompt=style_prompt,
negative_prompt=style_negative_prompt,
width=width,
height=height,
model_name=style_model,
steps=30,
sampler_name=Samplers.DPMPP_M_KARRAS,
seed=first_stage_seed,
controlnet_units=[
ControlnetUnit(
input_image=image_to_base64(style_reference_image),
control_mode=ControlNetMode.BALANCED,
model="control_v11p_sd15_lineart",
module=ControlNetPreprocessor.LINEART,
resize_mode=ControlNetResizeMode.RESIZE_OR_CORP,
weight=controlnet_weight,
)
]
)
elif style_method == "controlnet-scribble":
req = Txt2ImgRequest(
prompt=style_prompt,
negative_prompt=style_negative_prompt,
width=width,
height=height,
model_name=style_model,
steps=30,
sampler_name=Samplers.DPMPP_M_KARRAS,
seed=first_stage_seed,
controlnet_units=[
ControlnetUnit(
input_image=image_to_base64(style_reference_image),
control_mode=ControlNetMode.BALANCED,
model="control_v11p_sd15_scribble",
module=ControlNetPreprocessor.SCRIBBLE_HED,
resize_mode=ControlNetResizeMode.RESIZE_OR_CORP,
weight=controlnet_weight,
)
]
)
elif style_method == "controlnet-tile":
req = Txt2ImgRequest(
prompt=style_prompt,
negative_prompt=style_negative_prompt,
width=width,
height=height,
model_name=style_model,
steps=30,
sampler_name=Samplers.DPMPP_M_KARRAS,
seed=first_stage_seed,
controlnet_units=[
ControlnetUnit(
input_image=image_to_base64(style_reference_image),
control_mode=ControlNetMode.BALANCED,
model="control_v11f1e_sd15_tile",
module=ControlNetPreprocessor.NULL,
resize_mode=ControlNetResizeMode.RESIZE_OR_CORP,
weight=controlnet_weight,
)
],
)
ad_req = ADEtailer(
prompt=f"{second_stage_activication_words} {style_gender}, masterpiece, <lora:{style_lora}:{second_stage_lora_scale}>",
negative_prompt=style_negative_prompt,
strength=second_stage_strength,
seed=second_stage_seed,
steps=second_stage_steps,
)
req.batch_size = num_images
req.adetailer = ad_req
res = get_noviata_client(novita_key).sync_txt2img(req)
style_images = [Image.open(BytesIO(b)) for b in res.data.imgs_bytes]
return style_images, req.to_dict()
images = []
try:
final_images, first_stage_request_body = style(
style_method,
style_gender,
style_prompt,
style_negative_prompt,
style_model,
style_lora,
_hide_lora_training_response,
style_hegiht,
style_width,
style_reference_image,
first_stage_seed,
second_stage_seed,
first_stage_lora_scale,
second_stage_lora_scale,
second_stage_strength,
second_stage_steps,
controlnet_weight,
num_images
)
images.extend(final_images)
except:
raise gr.Error(traceback.format_exc())
return gr.update(value=images), first_stage_request_body
generate_button.click(
inputs=[novita_key,
style_gender,
style_prompt,
style_negative_prompt,
style_model,
style_lora,
_hide_lora_training_response,
style_height,
style_width,
style_method,
style_reference_image,
first_stage_seed,
second_stage_seed,
first_stage_lora_scale,
second_stage_lora_scale,
second_stage_strength,
second_stage_steps,
controlnet_weight,
num_images],
outputs=[gallery, first_stage_request_body],
fn=generate
)
with gr.Tab(label="Style Inferencing"):
with gr.Row():
with gr.Column(scale=1):
inference_style_prompt = gr.TextArea(lines=3, label="Style Prompt")
inference_style_negative_prompt = gr.TextArea(lines=3, label="Style Negative Prompt")
inference_style_model = gr.Dropdown(choices=suggestion_checkpoints, label="Style Model")
_inference_style_hide_lora_training_response = gr.JSON(visible=False)
inference_style_lora = gr.Dropdown(choices=[], label="Style LoRA", type="index")
inference_style_height = gr.Slider(minimum=1, maximum=1024, step=1, label="Style Height", value=512)
inference_style_width = gr.Slider(minimum=1, maximum=1024, step=1, label="Style Width", value=512)
inference_style_first_stage_seed = gr.Slider(minimum=-1, maximum=1000000, step=1, label="First Stage Seed", value=-1)
inference_style_first_stage_lora_scale = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="First Stage LoRA Scale", value=1.0)
with gr.Column(scale=1):
inference_style_refresh_button = gr.Button(value="Refresh Style LoRA")
inference_style_generate_button = gr.Button(value="Generate")
inference_style_num_images = gr.Slider(minimum=1, maximum=10, step=1, label="Num Images", value=1)
inference_style_gallery = gr.Gallery(label="Gallery", height="auto", object_fit="scale-down", show_share_button=False)
def inference_style_refresh_button_fn(novita_key):
# trained_loras_models = [_.name for _ in get_noviata_client(novita_key).models_v3(refresh=True).filter_by_type("lora").filter_by_visibility("private")]
serving_models = [_.models[0].model_name for _ in get_noviata_client(novita_key).list_training("subject").filter_by_model_status("SERVING")]
serving_models_labels = [_.task_name for _ in get_noviata_client(novita_key).list_training("subject").filter_by_model_status("SERVING")]
default_serving_model = serving_models_labels[0] if len(serving_models_labels) > 0 else None
return gr.update(choices=serving_models_labels, value=default_serving_model), gr.update(value=serving_models)
inference_style_refresh_button.click(
inputs=[novita_key],
outputs=[inference_style_lora, _inference_style_hide_lora_training_response],
fn=inference_style_refresh_button_fn
)
inference_style_first_stage_request_body = gr.JSON(label="First Stage Request Body, POST /api/v2/txt2img")
def inference_style_generate(novita_key,
style_prompt,
style_negative_prompt,
style_model,
style_lora,
_hide_lora_training_response,
style_hegiht,
style_width,
first_stage_seed,
first_stage_lora_scale,
num_images):
def style(
style_prompt,
style_negative_prompt,
style_model,
style_lora,
_hide_lora_training_response,
style_hegiht,
style_width,
first_stage_seed,
first_stage_lora_scale,
num_images
):
if isinstance(style_lora, int):
style_lora = _hide_lora_training_response[style_lora].replace(".safetensors", "")
else:
style_lora = style_lora.replace(".safetensors", "")
height = int(style_hegiht)
width = int(style_width)
if first_stage_seed == -1:
first_stage_seed = random.randint(1, 2 ** 32 - 1)
style_prompt = f"{style_prompt}, <lora:{style_lora}:{first_stage_lora_scale}>"
req = Txt2ImgRequest(
prompt=style_prompt,
negative_prompt=style_negative_prompt,
width=width,
height=height,
model_name=style_model,
steps=30,
sampler_name=Samplers.DPMPP_M_KARRAS,
seed=first_stage_seed,
batch_size=num_images
)
res = get_noviata_client(novita_key).sync_txt2img(req)
style_images = [Image.open(BytesIO(b)) for b in res.data.imgs_bytes]
return style_images, req.to_dict()
images = []
try:
final_images, first_stage_request_body = style(
style_prompt,
style_negative_prompt,
style_model,
style_lora,
_hide_lora_training_response,
style_hegiht,
style_width,
first_stage_seed,
first_stage_lora_scale,
num_images
)
images.extend(final_images)
except:
raise gr.Error(traceback.format_exc())
return gr.update(value=images), first_stage_request_body
inference_style_generate_button.click(
inputs=[novita_key,
inference_style_prompt,
inference_style_negative_prompt,
inference_style_model,
inference_style_lora,
_inference_style_hide_lora_training_response,
inference_style_height,
inference_style_width,
inference_style_first_stage_seed,
inference_style_first_stage_lora_scale,
inference_style_num_images],
outputs=[inference_style_gallery, inference_style_first_stage_request_body],
fn=inference_style_generate
)
def onload(novita_key):
if novita_key is None or novita_key == "":
return novita_key, gr.update(choices=[], value=None), gr.update(value=None), f"$ UNKNOWN", gr.update(visible=False)
try:
user_info_json = get_noviata_client(novita_key).user_info()
serving_models = [_.models[0].model_name for _ in get_noviata_client(novita_key).list_training().filter_by_model_status("SERVING")]
serving_models_labels = [_.task_name for _ in get_noviata_client(novita_key).list_training().filter_by_model_status("SERVING")]
except Exception as e:
logging.error(e)
return novita_key, gr.update(choices=[], value=None), gr.update(value=None), f"$ UNKNOWN", gr.update(visible=False)
default_serving_model = serving_models_labels[0] if len(serving_models_labels) > 0 else None
free_trial = user_info_json.free_trial.get('training', 0)
trial_html = f'''<h2 style="color: red"> 🌟 Free trial quota: {free_trial} </h2>'''
return novita_key, gr.update(choices=serving_models_labels, value=default_serving_model), gr.update(value=serving_models), f"$ {user_info_json.credit_balance / 100 / 100:.2f}", gr.update(value=trial_html, visible=free_trial > 0)
novita_key.change(onload, inputs=novita_key, outputs=[novita_key, style_lora, _hide_lora_training_response,
user_balance, free_trial_notice], _js="(v)=>{ setStorage('novita_key',v); return [v]; }")
demo.load(
inputs=[novita_key],
outputs=[novita_key, style_lora, _hide_lora_training_response, user_balance, free_trial_notice],
fn=onload,
_js=get_local_storage,
)
return demo
if __name__ == '__main__':
demo = create_ui()
demo.queue(api_open=False, concurrency_count=20)
demo.launch(server_name="0.0.0.0", share=True)
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