|
import spaces
|
|
import gradio as gr
|
|
import json
|
|
import torch
|
|
from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image
|
|
from live_preview_helpers import flux_pipe_call_that_returns_an_iterable_of_images
|
|
from diffusers.utils import load_image
|
|
from diffusers import FluxControlNetPipeline, FluxControlNetModel, FluxMultiControlNetModel, FluxControlNetImg2ImgPipeline
|
|
from huggingface_hub import HfFileSystem, ModelCard
|
|
import random
|
|
import time
|
|
|
|
from env import models, num_loras, num_cns
|
|
from mod import (clear_cache, get_repo_safetensors, is_repo_name, is_repo_exists, get_model_trigger,
|
|
description_ui, compose_lora_json, is_valid_lora, fuse_loras, save_image, preprocess_i2i_image,
|
|
get_trigger_word, enhance_prompt, deselect_lora, set_control_union_image,
|
|
get_control_union_mode, set_control_union_mode, get_control_params, translate_to_en)
|
|
from flux import (search_civitai_lora, select_civitai_lora, search_civitai_lora_json,
|
|
download_my_lora, get_all_lora_tupled_list, apply_lora_prompt,
|
|
update_loras, get_t2i_model_info)
|
|
from tagger.tagger import predict_tags_wd, compose_prompt_to_copy
|
|
from tagger.fl2flux import predict_tags_fl2_flux
|
|
|
|
|
|
with open('loras.json', 'r') as f:
|
|
loras = json.load(f)
|
|
|
|
|
|
base_model = models[0]
|
|
controlnet_model_union_repo = 'InstantX/FLUX.1-dev-Controlnet-Union'
|
|
|
|
dtype = torch.bfloat16
|
|
|
|
|
|
taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype)
|
|
good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype)
|
|
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1)
|
|
pipe_i2i = AutoPipelineForImage2Image.from_pretrained(base_model, vae=good_vae, transformer=pipe.transformer, text_encoder=pipe.text_encoder,
|
|
tokenizer=pipe.tokenizer, text_encoder_2=pipe.text_encoder_2, tokenizer_2=pipe.tokenizer_2, torch_dtype=dtype)
|
|
controlnet_union = None
|
|
controlnet = None
|
|
last_model = models[0]
|
|
last_cn_on = False
|
|
|
|
|
|
|
|
MAX_SEED = 2**32-1
|
|
|
|
|
|
|
|
|
|
|
|
def change_base_model(repo_id: str, cn_on: bool, disable_model_cache: bool, progress=gr.Progress(track_tqdm=True)):
|
|
global pipe
|
|
global pipe_i2i
|
|
global taef1
|
|
global good_vae
|
|
global controlnet_union
|
|
global controlnet
|
|
global last_model
|
|
global last_cn_on
|
|
global dtype
|
|
try:
|
|
if not disable_model_cache and (repo_id == last_model and cn_on is last_cn_on) or not is_repo_name(repo_id) or not is_repo_exists(repo_id): return gr.update(visible=True)
|
|
pipe.to("cpu")
|
|
pipe_i2i.to("cpu")
|
|
good_vae.to("cpu")
|
|
taef1.to("cpu")
|
|
if controlnet is not None: controlnet.to("cpu")
|
|
if controlnet_union is not None: controlnet_union.to("cpu")
|
|
clear_cache()
|
|
if cn_on:
|
|
progress(0, desc=f"Loading model: {repo_id} / Loading ControlNet: {controlnet_model_union_repo}")
|
|
print(f"Loading model: {repo_id} / Loading ControlNet: {controlnet_model_union_repo}")
|
|
controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union_repo, torch_dtype=dtype)
|
|
controlnet = FluxMultiControlNetModel([controlnet_union])
|
|
pipe = FluxControlNetPipeline.from_pretrained(repo_id, controlnet=controlnet, torch_dtype=dtype)
|
|
pipe_i2i = FluxControlNetImg2ImgPipeline.from_pretrained(repo_id, controlnet=controlnet, vae=None, transformer=pipe.transformer, text_encoder=pipe.text_encoder,
|
|
tokenizer=pipe.tokenizer, text_encoder_2=pipe.text_encoder_2, tokenizer_2=pipe.tokenizer_2, torch_dtype=dtype)
|
|
last_model = repo_id
|
|
last_cn_on = cn_on
|
|
progress(1, desc=f"Model loaded: {repo_id} / ControlNet Loaded: {controlnet_model_union_repo}")
|
|
print(f"Model loaded: {repo_id} / ControlNet Loaded: {controlnet_model_union_repo}")
|
|
else:
|
|
progress(0, desc=f"Loading model: {repo_id}")
|
|
print(f"Loading model: {repo_id}")
|
|
pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=dtype)
|
|
pipe_i2i = AutoPipelineForImage2Image.from_pretrained(repo_id, vae=None, transformer=pipe.transformer, text_encoder=pipe.text_encoder,
|
|
tokenizer=pipe.tokenizer, text_encoder_2=pipe.text_encoder_2, tokenizer_2=pipe.tokenizer_2, torch_dtype=dtype)
|
|
last_model = repo_id
|
|
last_cn_on = cn_on
|
|
progress(1, desc=f"Model loaded: {repo_id}")
|
|
print(f"Model loaded: {repo_id}")
|
|
except Exception as e:
|
|
print(f"Model load Error: {e}")
|
|
raise gr.Error(f"Model load Error: {e}") from e
|
|
return gr.update(visible=True)
|
|
|
|
change_base_model.zerogpu = True
|
|
|
|
class calculateDuration:
|
|
def __init__(self, activity_name=""):
|
|
self.activity_name = activity_name
|
|
|
|
def __enter__(self):
|
|
self.start_time = time.time()
|
|
return self
|
|
|
|
def __exit__(self, exc_type, exc_value, traceback):
|
|
self.end_time = time.time()
|
|
self.elapsed_time = self.end_time - self.start_time
|
|
if self.activity_name:
|
|
print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
|
|
else:
|
|
print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
|
|
|
|
def update_selection(evt: gr.SelectData, width, height):
|
|
selected_lora = loras[evt.index]
|
|
new_placeholder = f"Type a prompt for {selected_lora['title']}"
|
|
lora_repo = selected_lora["repo"]
|
|
updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
|
|
if "aspect" in selected_lora:
|
|
if selected_lora["aspect"] == "portrait":
|
|
width = 768
|
|
height = 1024
|
|
elif selected_lora["aspect"] == "landscape":
|
|
width = 1024
|
|
height = 768
|
|
else:
|
|
width = 1024
|
|
height = 1024
|
|
return (
|
|
gr.update(placeholder=new_placeholder),
|
|
updated_text,
|
|
evt.index,
|
|
width,
|
|
height,
|
|
)
|
|
|
|
@spaces.GPU(duration=70)
|
|
def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress=gr.Progress(track_tqdm=True)):
|
|
global pipe
|
|
global taef1
|
|
global good_vae
|
|
global controlnet
|
|
global controlnet_union
|
|
try:
|
|
good_vae.to("cuda")
|
|
taef1.to("cuda")
|
|
generator = torch.Generator(device="cuda").manual_seed(int(float(seed)))
|
|
|
|
with calculateDuration("Generating image"):
|
|
|
|
modes, images, scales = get_control_params()
|
|
if not cn_on or len(modes) == 0:
|
|
pipe.to("cuda")
|
|
pipe.vae = taef1
|
|
pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
|
|
progress(0, desc="Start Inference.")
|
|
for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
|
|
prompt=prompt_mash,
|
|
num_inference_steps=steps,
|
|
guidance_scale=cfg_scale,
|
|
width=width,
|
|
height=height,
|
|
generator=generator,
|
|
joint_attention_kwargs={"scale": lora_scale},
|
|
output_type="pil",
|
|
good_vae=good_vae,
|
|
):
|
|
yield img
|
|
else:
|
|
pipe.to("cuda")
|
|
pipe.vae = good_vae
|
|
if controlnet_union is not None: controlnet_union.to("cuda")
|
|
if controlnet is not None: controlnet.to("cuda")
|
|
pipe.enable_model_cpu_offload()
|
|
progress(0, desc="Start Inference with ControlNet.")
|
|
for img in pipe(
|
|
prompt=prompt_mash,
|
|
control_image=images,
|
|
control_mode=modes,
|
|
num_inference_steps=steps,
|
|
guidance_scale=cfg_scale,
|
|
width=width,
|
|
height=height,
|
|
controlnet_conditioning_scale=scales,
|
|
generator=generator,
|
|
joint_attention_kwargs={"scale": lora_scale},
|
|
).images:
|
|
yield img
|
|
except Exception as e:
|
|
print(e)
|
|
raise gr.Error(f"Inference Error: {e}") from e
|
|
|
|
@spaces.GPU(duration=70)
|
|
def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed, cn_on, progress=gr.Progress(track_tqdm=True)):
|
|
global pipe_i2i
|
|
global good_vae
|
|
global controlnet
|
|
global controlnet_union
|
|
try:
|
|
good_vae.to("cuda")
|
|
generator = torch.Generator(device="cuda").manual_seed(int(float(seed)))
|
|
image_input = load_image(image_input_path)
|
|
|
|
with calculateDuration("Generating image"):
|
|
|
|
modes, images, scales = get_control_params()
|
|
if True or not cn_on or len(modes) == 0:
|
|
pipe_i2i.to("cuda")
|
|
pipe_i2i.vae = good_vae
|
|
image_input = load_image(image_input_path)
|
|
progress(0, desc="Start I2I Inference.")
|
|
final_image = pipe_i2i(
|
|
prompt=prompt_mash,
|
|
image=image_input,
|
|
strength=image_strength,
|
|
num_inference_steps=steps,
|
|
guidance_scale=cfg_scale,
|
|
width=width,
|
|
height=height,
|
|
generator=generator,
|
|
joint_attention_kwargs={"scale": lora_scale},
|
|
output_type="pil",
|
|
).images[0]
|
|
return final_image
|
|
else:
|
|
pipe_i2i.to("cuda")
|
|
pipe_i2i.vae = good_vae
|
|
image_input = load_image(image_input_path)
|
|
if controlnet_union is not None: controlnet_union.to("cuda")
|
|
if controlnet is not None: controlnet.to("cuda")
|
|
pipe_i2i.enable_model_cpu_offload()
|
|
progress(0, desc="Start I2I Inference with ControlNet.")
|
|
final_image = pipe_i2i(
|
|
prompt=prompt_mash,
|
|
control_image=images,
|
|
control_mode=modes,
|
|
image=image_input,
|
|
strength=image_strength,
|
|
num_inference_steps=steps,
|
|
guidance_scale=cfg_scale,
|
|
width=width,
|
|
height=height,
|
|
controlnet_conditioning_scale=scales,
|
|
generator=generator,
|
|
joint_attention_kwargs={"scale": lora_scale},
|
|
output_type="pil",
|
|
).images[0]
|
|
return final_image
|
|
except Exception as e:
|
|
print(e)
|
|
raise gr.Error(f"I2I Inference Error: {e}") from e
|
|
|
|
def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height,
|
|
lora_scale, lora_json, cn_on, translate_on, progress=gr.Progress(track_tqdm=True)):
|
|
global pipe
|
|
if selected_index is None and not is_valid_lora(lora_json):
|
|
gr.Info("LoRA isn't selected.")
|
|
|
|
progress(0, desc="Preparing Inference.")
|
|
|
|
with calculateDuration("Unloading LoRA"):
|
|
try:
|
|
pipe.unfuse_lora()
|
|
pipe.unload_lora_weights()
|
|
pipe_i2i.unfuse_lora()
|
|
pipe_i2i.unload_lora_weights()
|
|
except Exception as e:
|
|
print(e)
|
|
|
|
clear_cache()
|
|
|
|
if translate_on: prompt = translate_to_en(prompt)
|
|
|
|
prompt_mash = prompt + get_model_trigger(last_model)
|
|
if is_valid_lora(lora_json):
|
|
|
|
with calculateDuration("Loading External LoRA weights"):
|
|
fuse_loras(pipe, lora_json)
|
|
trigger_word = get_trigger_word(lora_json)
|
|
prompt_mash = f"{prompt} {trigger_word}"
|
|
if selected_index is not None:
|
|
selected_lora = loras[selected_index]
|
|
lora_path = selected_lora["repo"]
|
|
trigger_word = selected_lora["trigger_word"]
|
|
if(trigger_word):
|
|
if "trigger_position" in selected_lora:
|
|
if selected_lora["trigger_position"] == "prepend":
|
|
prompt_mash = f"{trigger_word} {prompt_mash}"
|
|
else:
|
|
prompt_mash = f"{prompt_mash} {trigger_word}"
|
|
else:
|
|
prompt_mash = f"{trigger_word} {prompt_mash}"
|
|
else:
|
|
prompt_mash = prompt_mash
|
|
|
|
with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
|
|
if(image_input is not None):
|
|
if "weights" in selected_lora:
|
|
pipe_i2i.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
|
|
else:
|
|
pipe_i2i.load_lora_weights(lora_path)
|
|
else:
|
|
if "weights" in selected_lora:
|
|
pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
|
|
else:
|
|
pipe.load_lora_weights(lora_path)
|
|
|
|
|
|
with calculateDuration("Randomizing seed"):
|
|
if randomize_seed:
|
|
seed = random.randint(0, MAX_SEED)
|
|
|
|
progress(0, desc="Running Inference.")
|
|
if(image_input is not None):
|
|
final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed, cn_on, progress)
|
|
yield save_image(final_image, None, last_model, prompt_mash, height, width, steps, cfg_scale, seed), seed, gr.update(visible=False)
|
|
else:
|
|
image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress)
|
|
|
|
final_image = None
|
|
step_counter = 0
|
|
for image in image_generator:
|
|
step_counter+=1
|
|
final_image = image
|
|
progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
|
|
yield image, seed, gr.update(value=progress_bar, visible=True)
|
|
yield save_image(final_image, None, last_model, prompt_mash, height, width, steps, cfg_scale, seed), seed, gr.update(value=progress_bar, visible=False)
|
|
|
|
def get_huggingface_safetensors(link):
|
|
split_link = link.split("/")
|
|
if(len(split_link) == 2):
|
|
model_card = ModelCard.load(link)
|
|
base_model = model_card.data.get("base_model")
|
|
print(base_model)
|
|
if((base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "black-forest-labs/FLUX.1-schnell")):
|
|
raise Exception("Not a FLUX LoRA!")
|
|
image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
|
|
trigger_word = model_card.data.get("instance_prompt", "")
|
|
image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
|
|
fs = HfFileSystem()
|
|
try:
|
|
list_of_files = fs.ls(link, detail=False)
|
|
for file in list_of_files:
|
|
if(file.endswith(".safetensors")):
|
|
safetensors_name = file.split("/")[-1]
|
|
if (not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))):
|
|
image_elements = file.split("/")
|
|
image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}"
|
|
except Exception as e:
|
|
print(e)
|
|
gr.Warning(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
|
|
raise Exception(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
|
|
return split_link[1], link, safetensors_name, trigger_word, image_url
|
|
|
|
def check_custom_model(link):
|
|
if(link.startswith("https://")):
|
|
if(link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co")):
|
|
link_split = link.split("huggingface.co/")
|
|
return get_huggingface_safetensors(link_split[1])
|
|
else:
|
|
return get_huggingface_safetensors(link)
|
|
|
|
def add_custom_lora(custom_lora):
|
|
global loras
|
|
if(custom_lora):
|
|
try:
|
|
title, repo, path, trigger_word, image = check_custom_model(custom_lora)
|
|
print(f"Loaded custom LoRA: {repo}")
|
|
card = f'''
|
|
<div class="custom_lora_card">
|
|
<span>Loaded custom LoRA:</span>
|
|
<div class="card_internal">
|
|
<img src="{image}" />
|
|
<div>
|
|
<h3>{title}</h3>
|
|
<small>{"Using: <code><b>"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
'''
|
|
existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
|
|
if(not existing_item_index):
|
|
new_item = {
|
|
"image": image,
|
|
"title": title,
|
|
"repo": repo,
|
|
"weights": path,
|
|
"trigger_word": trigger_word
|
|
}
|
|
print(new_item)
|
|
existing_item_index = len(loras)
|
|
loras.append(new_item)
|
|
|
|
return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word
|
|
except Exception as e:
|
|
gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-FLUX LoRA")
|
|
return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-FLUX LoRA"), gr.update(visible=True), gr.update(), "", None, ""
|
|
else:
|
|
return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
|
|
|
|
def remove_custom_lora():
|
|
return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
|
|
|
|
run_lora.zerogpu = True
|
|
|
|
css = '''
|
|
#gen_btn{height: 100%}
|
|
#title{text-align: center}
|
|
#title h1{font-size: 3em; display:inline-flex; align-items:center}
|
|
#title img{width: 100px; margin-right: 0.5em}
|
|
#gallery .grid-wrap{height: 10vh}
|
|
#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}
|
|
.card_internal{display: flex;height: 100px;margin-top: .5em}
|
|
.card_internal img{margin-right: 1em}
|
|
.styler{--form-gap-width: 0px !important}
|
|
#progress{height:30px}
|
|
#progress .generating{display:none}
|
|
.progress-container {width: 100%;height: 30px;background-color: #f0f0f0;border-radius: 15px;overflow: hidden;margin-bottom: 20px}
|
|
.progress-bar {height: 100%;background-color: #4f46e5;width: calc(var(--current) / var(--total) * 100%);transition: width 0.5s ease-in-out}
|
|
.info {text-align:center; !important}
|
|
'''
|
|
with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css, delete_cache=(60, 3600)) as app:
|
|
with gr.Tab("FLUX LoRA the Explorer"):
|
|
title = gr.HTML(
|
|
"""<h1><img src="https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA">FLUX LoRA the Explorer Mod</h1>""",
|
|
elem_id="title",
|
|
)
|
|
selected_index = gr.State(None)
|
|
with gr.Row():
|
|
with gr.Column(scale=3):
|
|
with gr.Group():
|
|
with gr.Accordion("Generate Prompt from Image", open=False):
|
|
tagger_image = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256)
|
|
with gr.Accordion(label="Advanced options", open=False):
|
|
tagger_general_threshold = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True)
|
|
tagger_character_threshold = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True)
|
|
neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="", visible=False)
|
|
v2_character = gr.Textbox(label="Character", placeholder="hatsune miku", scale=2, visible=False)
|
|
v2_series = gr.Textbox(label="Series", placeholder="vocaloid", scale=2, visible=False)
|
|
v2_copy = gr.Button(value="Copy to clipboard", size="sm", interactive=False, visible=False)
|
|
tagger_algorithms = gr.CheckboxGroup(["Use WD Tagger", "Use Florence-2-Flux"], label="Algorithms", value=["Use WD Tagger"])
|
|
tagger_generate_from_image = gr.Button(value="Generate Prompt from Image")
|
|
prompt = gr.Textbox(label="Prompt", lines=1, max_lines=8, placeholder="Type a prompt", show_copy_button=True)
|
|
with gr.Row():
|
|
prompt_enhance = gr.Button(value="Enhance your prompt", variant="secondary")
|
|
auto_trans = gr.Checkbox(label="Auto translate to English", value=False, elem_classes="info")
|
|
with gr.Column(scale=1, elem_id="gen_column"):
|
|
generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
|
|
with gr.Row():
|
|
with gr.Column():
|
|
selected_info = gr.Markdown("")
|
|
gallery = gr.Gallery(
|
|
[(item["image"], item["title"]) for item in loras],
|
|
label="LoRA Gallery",
|
|
allow_preview=False,
|
|
columns=3,
|
|
elem_id="gallery"
|
|
)
|
|
with gr.Group():
|
|
custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path", placeholder="multimodalart/vintage-ads-flux")
|
|
gr.Markdown("[Check the list of FLUX LoRas](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
|
|
custom_lora_info = gr.HTML(visible=False)
|
|
custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
|
|
deselect_lora_button = gr.Button("Deselect LoRA", variant="secondary")
|
|
with gr.Column():
|
|
progress_bar = gr.Markdown(elem_id="progress",visible=False)
|
|
result = gr.Image(label="Generated Image", format="png", show_share_button=False)
|
|
with gr.Group():
|
|
model_name = gr.Dropdown(label="Base Model", info="You can enter a huggingface model repo_id to want to use.", choices=models, value=models[0], allow_custom_value=True)
|
|
model_info = gr.Markdown(elem_classes="info")
|
|
with gr.Row():
|
|
with gr.Accordion("Advanced Settings", open=False):
|
|
with gr.Row():
|
|
input_image = gr.Image(label="Input image", type="filepath", height=256, sources=["upload", "clipboard"], show_share_button=False)
|
|
with gr.Column():
|
|
image_strength = gr.Slider(label="Image Strength", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
|
|
input_image_preprocess = gr.Checkbox(True, label="Preprocess Input image")
|
|
with gr.Column():
|
|
with gr.Row():
|
|
lora_scale = gr.Slider(label="LoRA Scale", minimum=-3, maximum=3, step=0.01, value=0.95)
|
|
width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
|
|
height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
|
|
with gr.Row():
|
|
cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
|
|
steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
|
|
with gr.Row():
|
|
randomize_seed = gr.Checkbox(True, label="Randomize seed")
|
|
seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
|
|
disable_model_cache = gr.Checkbox(False, label="Disable model caching")
|
|
with gr.Accordion("External LoRA", open=True):
|
|
with gr.Column():
|
|
lora_repo_json = gr.JSON(value=[{}] * num_loras, visible=False)
|
|
lora_repo = [None] * num_loras
|
|
lora_weights = [None] * num_loras
|
|
lora_trigger = [None] * num_loras
|
|
lora_wt = [None] * num_loras
|
|
lora_info = [None] * num_loras
|
|
lora_copy = [None] * num_loras
|
|
lora_md = [None] * num_loras
|
|
lora_num = [None] * num_loras
|
|
with gr.Row():
|
|
for i in range(num_loras):
|
|
with gr.Column():
|
|
lora_repo[i] = gr.Dropdown(label=f"LoRA {int(i+1)} Repo", choices=get_all_lora_tupled_list(), info="Input LoRA Repo ID", value="", allow_custom_value=True)
|
|
with gr.Row():
|
|
lora_weights[i] = gr.Dropdown(label=f"LoRA {int(i+1)} Filename", choices=[], info="Optional", value="", allow_custom_value=True)
|
|
lora_trigger[i] = gr.Textbox(label=f"LoRA {int(i+1)} Trigger Prompt", lines=1, max_lines=4, value="")
|
|
lora_wt[i] = gr.Slider(label=f"LoRA {int(i+1)} Scale", minimum=-3, maximum=3, step=0.01, value=1.00)
|
|
with gr.Row():
|
|
lora_info[i] = gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False)
|
|
lora_copy[i] = gr.Button(value="Copy example to prompt", visible=False)
|
|
lora_md[i] = gr.Markdown(value="", visible=False)
|
|
lora_num[i] = gr.Number(i, visible=False)
|
|
with gr.Accordion("From URL", open=True, visible=True):
|
|
with gr.Row():
|
|
lora_search_civitai_query = gr.Textbox(label="Query", placeholder="flux", lines=1)
|
|
lora_search_civitai_submit = gr.Button("Search on Civitai")
|
|
lora_search_civitai_basemodel = gr.CheckboxGroup(label="Search LoRA for", choices=["Flux.1 D", "Flux.1 S"], value=["Flux.1 D", "Flux.1 S"])
|
|
with gr.Row():
|
|
lora_search_civitai_json = gr.JSON(value={}, visible=False)
|
|
lora_search_civitai_desc = gr.Markdown(value="", visible=False)
|
|
lora_search_civitai_result = gr.Dropdown(label="Search Results", choices=[("", "")], value="", allow_custom_value=True, visible=False)
|
|
lora_download_url = gr.Textbox(label="URL", placeholder="http://...my_lora_url.safetensors", lines=1)
|
|
with gr.Row():
|
|
lora_download = [None] * num_loras
|
|
for i in range(num_loras):
|
|
lora_download[i] = gr.Button(f"Get and set LoRA to {int(i+1)}")
|
|
with gr.Accordion("ControlNet (extremely slow)", open=True, visible=True):
|
|
with gr.Column():
|
|
cn_on = gr.Checkbox(False, label="Use ControlNet")
|
|
cn_mode = [None] * num_cns
|
|
cn_scale = [None] * num_cns
|
|
cn_image = [None] * num_cns
|
|
cn_image_ref = [None] * num_cns
|
|
cn_res = [None] * num_cns
|
|
cn_num = [None] * num_cns
|
|
with gr.Row():
|
|
for i in range(num_cns):
|
|
with gr.Column():
|
|
cn_mode[i] = gr.Radio(label=f"ControlNet {int(i+1)} Mode", choices=get_control_union_mode(), value=get_control_union_mode()[0])
|
|
with gr.Row():
|
|
cn_scale[i] = gr.Slider(label=f"ControlNet {int(i+1)} Weight", minimum=0.0, maximum=1.0, step=0.01, value=0.75)
|
|
cn_res[i] = gr.Slider(label=f"ControlNet {int(i+1)} Preprocess resolution", minimum=128, maximum=512, value=384, step=1)
|
|
cn_num[i] = gr.Number(i, visible=False)
|
|
with gr.Row():
|
|
cn_image_ref[i] = gr.Image(label="Image Reference", type="pil", format="png", height=256, sources=["upload", "clipboard"], show_share_button=False)
|
|
cn_image[i] = gr.Image(label="Control Image", type="pil", format="png", height=256, show_share_button=False, interactive=False)
|
|
|
|
gallery.select(
|
|
update_selection,
|
|
inputs=[width, height],
|
|
outputs=[prompt, selected_info, selected_index, width, height],
|
|
queue=False,
|
|
show_api=False,
|
|
trigger_mode="once",
|
|
)
|
|
custom_lora.input(
|
|
add_custom_lora,
|
|
inputs=[custom_lora],
|
|
outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt],
|
|
queue=False,
|
|
show_api=False,
|
|
)
|
|
custom_lora_button.click(
|
|
remove_custom_lora,
|
|
outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora],
|
|
queue=False,
|
|
show_api=False,
|
|
)
|
|
gr.on(
|
|
triggers=[generate_button.click, prompt.submit],
|
|
fn=change_base_model,
|
|
inputs=[model_name, cn_on, disable_model_cache],
|
|
outputs=[result],
|
|
queue=True,
|
|
show_api=False,
|
|
trigger_mode="once",
|
|
).success(
|
|
fn=run_lora,
|
|
inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height,
|
|
lora_scale, lora_repo_json, cn_on, auto_trans],
|
|
outputs=[result, seed, progress_bar],
|
|
queue=True,
|
|
show_api=True,
|
|
)
|
|
|
|
input_image.upload(preprocess_i2i_image, [input_image, input_image_preprocess, height, width], [input_image], queue=False, show_api=False)
|
|
deselect_lora_button.click(deselect_lora, None, [prompt, selected_info, selected_index, width, height], queue=False, show_api=False)
|
|
gr.on(
|
|
triggers=[model_name.change, cn_on.change],
|
|
fn=get_t2i_model_info,
|
|
inputs=[model_name],
|
|
outputs=[model_info],
|
|
queue=False,
|
|
show_api=False,
|
|
trigger_mode="once",
|
|
).then(change_base_model, [model_name, cn_on, disable_model_cache], [result], queue=True, show_api=False)
|
|
prompt_enhance.click(enhance_prompt, [prompt], [prompt], queue=False, show_api=False)
|
|
|
|
gr.on(
|
|
triggers=[lora_search_civitai_submit.click, lora_search_civitai_query.submit],
|
|
fn=search_civitai_lora,
|
|
inputs=[lora_search_civitai_query, lora_search_civitai_basemodel],
|
|
outputs=[lora_search_civitai_result, lora_search_civitai_desc, lora_search_civitai_submit, lora_search_civitai_query],
|
|
scroll_to_output=True,
|
|
queue=True,
|
|
show_api=False,
|
|
)
|
|
lora_search_civitai_json.change(search_civitai_lora_json, [lora_search_civitai_query, lora_search_civitai_basemodel], [lora_search_civitai_json], queue=True, show_api=True)
|
|
lora_search_civitai_result.change(select_civitai_lora, [lora_search_civitai_result], [lora_download_url, lora_search_civitai_desc], scroll_to_output=True, queue=False, show_api=False)
|
|
|
|
for i, l in enumerate(lora_repo):
|
|
deselect_lora_button.click(lambda: ("", 1.0), None, [lora_repo[i], lora_wt[i]], queue=False, show_api=False)
|
|
gr.on(
|
|
triggers=[lora_download[i].click],
|
|
fn=download_my_lora,
|
|
inputs=[lora_download_url, lora_repo[i]],
|
|
outputs=[lora_repo[i]],
|
|
scroll_to_output=True,
|
|
queue=True,
|
|
show_api=False,
|
|
)
|
|
gr.on(
|
|
triggers=[lora_repo[i].change, lora_wt[i].change],
|
|
fn=update_loras,
|
|
inputs=[prompt, lora_repo[i], lora_wt[i]],
|
|
outputs=[prompt, lora_repo[i], lora_wt[i], lora_info[i], lora_md[i]],
|
|
queue=False,
|
|
trigger_mode="once",
|
|
show_api=False,
|
|
).success(get_repo_safetensors, [lora_repo[i]], [lora_weights[i]], queue=False, show_api=False
|
|
).success(apply_lora_prompt, [lora_info[i]], [lora_trigger[i]], queue=False, show_api=False
|
|
).success(compose_lora_json, [lora_repo_json, lora_num[i], lora_repo[i], lora_wt[i], lora_weights[i], lora_trigger[i]], [lora_repo_json], queue=False, show_api=False)
|
|
|
|
for i, m in enumerate(cn_mode):
|
|
gr.on(
|
|
triggers=[cn_mode[i].change, cn_scale[i].change],
|
|
fn=set_control_union_mode,
|
|
inputs=[cn_num[i], cn_mode[i], cn_scale[i]],
|
|
outputs=[cn_on],
|
|
queue=True,
|
|
show_api=False,
|
|
).success(set_control_union_image, [cn_num[i], cn_mode[i], cn_image_ref[i], height, width, cn_res[i]], [cn_image[i]], queue=False, show_api=False)
|
|
cn_image_ref[i].upload(set_control_union_image, [cn_num[i], cn_mode[i], cn_image_ref[i], height, width, cn_res[i]], [cn_image[i]], queue=False, show_api=False)
|
|
|
|
tagger_generate_from_image.click(lambda: ("", "", ""), None, [v2_series, v2_character, prompt], queue=False, show_api=False,
|
|
).success(
|
|
predict_tags_wd,
|
|
[tagger_image, prompt, tagger_algorithms, tagger_general_threshold, tagger_character_threshold],
|
|
[v2_series, v2_character, prompt, v2_copy],
|
|
show_api=False,
|
|
).success(predict_tags_fl2_flux, [tagger_image, prompt, tagger_algorithms], [prompt], show_api=False,
|
|
).success(compose_prompt_to_copy, [v2_character, v2_series, prompt], [prompt], queue=False, show_api=False)
|
|
|
|
with gr.Tab("FLUX Prompt Generator"):
|
|
from prompt import (PromptGenerator, HuggingFaceInferenceNode, florence_caption,
|
|
ARTFORM, PHOTO_TYPE, ROLES, HAIRSTYLES, LIGHTING, COMPOSITION, POSE, BACKGROUND,
|
|
PHOTOGRAPHY_STYLES, DEVICE, PHOTOGRAPHER, ARTIST, DIGITAL_ARTFORM, PLACE,
|
|
FEMALE_DEFAULT_TAGS, MALE_DEFAULT_TAGS, FEMALE_BODY_TYPES, MALE_BODY_TYPES,
|
|
FEMALE_CLOTHING, MALE_CLOTHING, FEMALE_ADDITIONAL_DETAILS, MALE_ADDITIONAL_DETAILS, pg_title)
|
|
|
|
prompt_generator = PromptGenerator()
|
|
huggingface_node = HuggingFaceInferenceNode()
|
|
|
|
gr.HTML(pg_title)
|
|
|
|
with gr.Row():
|
|
with gr.Column(scale=2):
|
|
with gr.Accordion("Basic Settings"):
|
|
pg_custom = gr.Textbox(label="Custom Input Prompt (optional)")
|
|
pg_subject = gr.Textbox(label="Subject (optional)")
|
|
pg_gender = gr.Radio(["female", "male"], label="Gender", value="female")
|
|
|
|
|
|
pg_global_option = gr.Radio(
|
|
["Disabled", "Random", "No Figure Rand"],
|
|
label="Set all options to:",
|
|
value="Disabled"
|
|
)
|
|
|
|
with gr.Accordion("Artform and Photo Type", open=False):
|
|
pg_artform = gr.Dropdown(["disabled", "random"] + ARTFORM, label="Artform", value="disabled")
|
|
pg_photo_type = gr.Dropdown(["disabled", "random"] + PHOTO_TYPE, label="Photo Type", value="disabled")
|
|
|
|
with gr.Accordion("Character Details", open=False):
|
|
pg_body_types = gr.Dropdown(["disabled", "random"] + FEMALE_BODY_TYPES + MALE_BODY_TYPES, label="Body Types", value="disabled")
|
|
pg_default_tags = gr.Dropdown(["disabled", "random"] + FEMALE_DEFAULT_TAGS + MALE_DEFAULT_TAGS, label="Default Tags", value="disabled")
|
|
pg_roles = gr.Dropdown(["disabled", "random"] + ROLES, label="Roles", value="disabled")
|
|
pg_hairstyles = gr.Dropdown(["disabled", "random"] + HAIRSTYLES, label="Hairstyles", value="disabled")
|
|
pg_clothing = gr.Dropdown(["disabled", "random"] + FEMALE_CLOTHING + MALE_CLOTHING, label="Clothing", value="disabled")
|
|
|
|
with gr.Accordion("Scene Details", open=False):
|
|
pg_place = gr.Dropdown(["disabled", "random"] + PLACE, label="Place", value="disabled")
|
|
pg_lighting = gr.Dropdown(["disabled", "random"] + LIGHTING, label="Lighting", value="disabled")
|
|
pg_composition = gr.Dropdown(["disabled", "random"] + COMPOSITION, label="Composition", value="disabled")
|
|
pg_pose = gr.Dropdown(["disabled", "random"] + POSE, label="Pose", value="disabled")
|
|
pg_background = gr.Dropdown(["disabled", "random"] + BACKGROUND, label="Background", value="disabled")
|
|
|
|
with gr.Accordion("Style and Artist", open=False):
|
|
pg_additional_details = gr.Dropdown(["disabled", "random"] + FEMALE_ADDITIONAL_DETAILS + MALE_ADDITIONAL_DETAILS, label="Additional Details", value="disabled")
|
|
pg_photography_styles = gr.Dropdown(["disabled", "random"] + PHOTOGRAPHY_STYLES, label="Photography Styles", value="disabled")
|
|
pg_device = gr.Dropdown(["disabled", "random"] + DEVICE, label="Device", value="disabled")
|
|
pg_photographer = gr.Dropdown(["disabled", "random"] + PHOTOGRAPHER, label="Photographer", value="disabled")
|
|
pg_artist = gr.Dropdown(["disabled", "random"] + ARTIST, label="Artist", value="disabled")
|
|
pg_digital_artform = gr.Dropdown(["disabled", "random"] + DIGITAL_ARTFORM, label="Digital Artform", value="disabled")
|
|
|
|
pg_generate_button = gr.Button("Generate Prompt")
|
|
|
|
with gr.Column(scale=2):
|
|
with gr.Accordion("Image and Caption", open=False):
|
|
pg_input_image = gr.Image(label="Input Image (optional)")
|
|
pg_caption_output = gr.Textbox(label="Generated Caption", lines=3)
|
|
pg_create_caption_button = gr.Button("Create Caption")
|
|
pg_add_caption_button = gr.Button("Add Caption to Prompt")
|
|
|
|
with gr.Accordion("Prompt Generation", open=True):
|
|
pg_output = gr.Textbox(label="Generated Prompt / Input Text", lines=4)
|
|
pg_t5xxl_output = gr.Textbox(label="T5XXL Output", visible=True)
|
|
pg_clip_l_output = gr.Textbox(label="CLIP L Output", visible=True)
|
|
pg_clip_g_output = gr.Textbox(label="CLIP G Output", visible=True)
|
|
|
|
with gr.Column(scale=2):
|
|
with gr.Accordion("Prompt Generation with LLM", open=False):
|
|
pg_happy_talk = gr.Checkbox(label="Happy Talk", value=True)
|
|
pg_compress = gr.Checkbox(label="Compress", value=True)
|
|
pg_compression_level = gr.Radio(["soft", "medium", "hard"], label="Compression Level", value="hard")
|
|
pg_poster = gr.Checkbox(label="Poster", value=False)
|
|
pg_custom_base_prompt = gr.Textbox(label="Custom Base Prompt", lines=5)
|
|
pg_generate_text_button = gr.Button("Generate Prompt with LLM (Llama 3.1 70B)")
|
|
pg_text_output = gr.Textbox(label="Generated Text", lines=10)
|
|
|
|
def create_caption(image):
|
|
if image is not None:
|
|
return florence_caption(image)
|
|
return ""
|
|
|
|
pg_create_caption_button.click(
|
|
create_caption,
|
|
inputs=[pg_input_image],
|
|
outputs=[pg_caption_output]
|
|
)
|
|
|
|
def generate_prompt_with_dynamic_seed(*args):
|
|
|
|
dynamic_seed = random.randint(0, 1000000)
|
|
|
|
|
|
result = prompt_generator.generate_prompt(dynamic_seed, *args)
|
|
|
|
|
|
return [dynamic_seed] + list(result)
|
|
|
|
pg_generate_button.click(
|
|
generate_prompt_with_dynamic_seed,
|
|
inputs=[pg_custom, pg_subject, pg_gender, pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles,
|
|
pg_additional_details, pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform,
|
|
pg_place, pg_lighting, pg_clothing, pg_composition, pg_pose, pg_background, pg_input_image],
|
|
outputs=[gr.Number(label="Used Seed", visible=False), pg_output, gr.Number(visible=False), pg_t5xxl_output, pg_clip_l_output, pg_clip_g_output]
|
|
)
|
|
|
|
pg_add_caption_button.click(
|
|
prompt_generator.add_caption_to_prompt,
|
|
inputs=[pg_output, pg_caption_output],
|
|
outputs=[pg_output]
|
|
)
|
|
|
|
pg_generate_text_button.click(
|
|
huggingface_node.generate,
|
|
inputs=[pg_output, pg_happy_talk, pg_compress, pg_compression_level, pg_poster, pg_custom_base_prompt],
|
|
outputs=pg_text_output
|
|
)
|
|
|
|
def update_all_options(choice):
|
|
updates = {}
|
|
if choice == "Disabled":
|
|
for dropdown in [
|
|
pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing,
|
|
pg_place, pg_lighting, pg_composition, pg_pose, pg_background, pg_additional_details,
|
|
pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform
|
|
]:
|
|
updates[dropdown] = gr.update(value="disabled")
|
|
elif choice == "Random":
|
|
for dropdown in [
|
|
pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing,
|
|
pg_place, pg_lighting, pg_composition, pg_pose, pg_background, pg_additional_details,
|
|
pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform
|
|
]:
|
|
updates[dropdown] = gr.update(value="random")
|
|
else:
|
|
for dropdown in [pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing, pg_pose, pg_additional_details]:
|
|
updates[dropdown] = gr.update(value="disabled")
|
|
for dropdown in [pg_artform, pg_place, pg_lighting, pg_composition, pg_background, pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform]:
|
|
updates[dropdown] = gr.update(value="random")
|
|
return updates
|
|
|
|
pg_global_option.change(
|
|
update_all_options,
|
|
inputs=[pg_global_option],
|
|
outputs=[
|
|
pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing,
|
|
pg_place, pg_lighting, pg_composition, pg_pose, pg_background, pg_additional_details,
|
|
pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform
|
|
]
|
|
)
|
|
|
|
description_ui()
|
|
gr.LoginButton()
|
|
gr.DuplicateButton(value="Duplicate Space for private use (This demo does not work on CPU. Requires GPU Space)")
|
|
|
|
|
|
app.queue()
|
|
app.launch() |
