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import spaces | |
import gradio as gr | |
import json | |
import logging | |
import torch | |
from PIL import Image | |
from diffusers import DiffusionPipeline | |
from diffusers import FluxControlNetPipeline, FluxControlNetModel, FluxMultiControlNetModel | |
from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download | |
import copy | |
import random | |
import time | |
from mod import (models, clear_cache, get_repo_safetensors, is_repo_name, is_repo_exists, | |
description_ui, num_loras, compose_lora_json, is_valid_lora, fuse_loras, | |
get_trigger_word, enhance_prompt, deselect_lora, num_cns, set_control_union_image, | |
get_control_union_mode, set_control_union_mode, get_control_params) | |
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) | |
from tagger.tagger import predict_tags_wd, compose_prompt_to_copy | |
from tagger.fl2flux import predict_tags_fl2_flux | |
# Initialize the base model | |
base_model = models[0] | |
controlnet_model_union_repo = 'InstantX/FLUX.1-dev-Controlnet-Union' | |
#controlnet_model_union_repo = 'InstantX/FLUX.1-dev-Controlnet-Union-alpha' | |
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) | |
controlnet_union = None | |
controlnet = None | |
last_model = models[0] | |
last_cn_on = False | |
# https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union | |
# https://huggingface.co/spaces/jiuface/FLUX.1-dev-Controlnet-Union | |
def change_base_model(repo_id: str, cn_on: bool): | |
global pipe | |
global controlnet_union | |
global controlnet | |
global last_model | |
global last_cn_on | |
dtype = torch.bfloat16 | |
#dtype = torch.float8_e4m3fn | |
try: | |
if (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) | |
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}") | |
clear_cache() | |
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) | |
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}") | |
clear_cache() | |
pipe = DiffusionPipeline.from_pretrained(repo_id, 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}") | |
return gr.update(visible=True) | |
change_base_model.zerogpu = True | |
# Load LoRAs from JSON file | |
with open('loras.json', 'r') as f: | |
loras = json.load(f) | |
MAX_SEED = 2**32-1 | |
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 | |
return ( | |
gr.update(placeholder=new_placeholder), | |
updated_text, | |
evt.index, | |
width, | |
height, | |
) | |
def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress=gr.Progress(track_tqdm=True)): | |
global pipe | |
global controlnet | |
global controlnet_union | |
try: | |
pipe.to("cuda") | |
generator = torch.Generator(device="cuda").manual_seed(seed) | |
with calculateDuration("Generating image"): | |
# Generate image | |
modes, images, scales = get_control_params() | |
if not cn_on or len(modes) == 0: | |
progress(0, desc="Start Inference.") | |
image = pipe( | |
prompt=prompt_mash, | |
num_inference_steps=steps, | |
guidance_scale=cfg_scale, | |
width=width, | |
height=height, | |
generator=generator, | |
joint_attention_kwargs={"scale": lora_scale}, | |
).images[0] | |
else: | |
progress(0, desc="Start Inference with ControlNet.") | |
if controlnet is not None: controlnet.to("cuda") | |
if controlnet_union is not None: controlnet_union.to("cuda") | |
image = 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[0] | |
except Exception as e: | |
print(e) | |
raise gr.Error(f"Inference Error: {e}") | |
return image | |
def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, | |
lora_scale, lora_json, cn_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.") | |
# raise gr.Error("You must select a LoRA before proceeding.") | |
progress(0, desc="Preparing Inference.") | |
prompt_mash = prompt | |
if is_valid_lora(lora_json): | |
with calculateDuration("Loading 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}" | |
else: | |
prompt_mash = f"{prompt} {trigger_word}" | |
else: | |
prompt_mash = f"{trigger_word} {prompt}" | |
else: | |
prompt_mash = prompt | |
# Load LoRA weights | |
with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"): | |
if "weights" in selected_lora: | |
pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"]) | |
else: | |
pipe.load_lora_weights(lora_path) | |
# Set random seed for reproducibility | |
with calculateDuration("Randomizing seed"): | |
if randomize_seed: | |
seed = random.randint(0, MAX_SEED) | |
progress(0, desc="Running Inference.") | |
image = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress) | |
if is_valid_lora(lora_json): | |
pipe.unfuse_lora() | |
pipe.unload_lora_weights() | |
if selected_index is not None: pipe.unload_lora_weights() | |
pipe.to("cpu") | |
if controlnet is not None: controlnet.to("cpu") | |
if controlnet_union is not None: controlnet_union.to("cpu") | |
clear_cache() | |
return image, seed | |
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} | |
''' | |
with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css) 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") | |
prompt_enhance = gr.Button(value="Enhance your prompt", variant="secondary") | |
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(scale=3): | |
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(scale=4): | |
result = gr.Image(label="Generated Image", format="png", show_share_button=False) | |
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) | |
with gr.Row(): | |
with gr.Accordion("Advanced Settings", open=False): | |
with gr.Column(): | |
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(): | |
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(): | |
randomize_seed = gr.Checkbox(True, label="Randomize seed") | |
seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True) | |
lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=0.95) | |
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=-2, maximum=2, 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 (🚧Under construction...🚧)", open=False): | |
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(): | |
with gr.Row(): | |
cn_mode[i] = gr.Dropdown(label=f"ControlNet {int(i+1)} Mode", choices=get_control_union_mode(), value=get_control_union_mode()[0], allow_custom_value=False) | |
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, | |
) | |
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], | |
outputs=[result], | |
queue=False, | |
show_api=False, | |
).success( | |
fn=run_lora, | |
inputs=[prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, | |
lora_scale, lora_repo_json, cn_on], | |
outputs=[result, seed], | |
queue=True, | |
show_api=True, | |
) | |
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=change_base_model, | |
inputs=[model_name, cn_on], | |
outputs=[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) # fn for api | |
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") | |
# Add the radio button for global option selection | |
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) | |
description_ui() | |
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): | |
# Generate a new random seed | |
dynamic_seed = random.randint(0, 1000000) | |
# Call the generate_prompt function with the dynamic seed | |
result = prompt_generator.generate_prompt(dynamic_seed, *args) | |
# Return the result along with the used seed | |
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: # No Figure Random | |
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 | |
] | |
) | |
app.queue() | |
app.launch() |