import spaces
import gradio as gr
import json
import torch
from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image, AutoPipelineForInpainting
from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
from diffusers.utils import load_image
from diffusers import FluxControlNetPipeline, FluxControlNetModel, FluxMultiControlNetModel, FluxControlNetImg2ImgPipeline, FluxTransformer2DModel, FluxControlNetInpaintPipeline, FluxInpaintPipeline
from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download, HfApi
import os
import copy
import random
import time
import requests
import pandas as pd
import numpy as np
from pathlib import Path
from env import models, num_loras, num_cns, HF_TOKEN, single_file_base_models
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, set_control_union_image,
get_control_union_mode, set_control_union_mode, get_control_params, translate_to_en)
from modutils import (search_civitai_lora, select_civitai_lora, search_civitai_lora_json,
download_my_lora_flux, get_all_lora_tupled_list, apply_lora_prompt_flux,
update_loras_flux, update_civitai_selection, get_civitai_tag, CIVITAI_SORT, CIVITAI_PERIOD,
get_t2i_model_info, download_hf_file, save_image_history)
from tagger.tagger import predict_tags_wd, compose_prompt_to_copy
from tagger.fl2flux import predict_tags_fl2_flux
#Load prompts for randomization
df = pd.read_csv('prompts.csv', header=None)
prompt_values = df.values.flatten()
# Load LoRAs from JSON file
with open('loras.json', 'r') as f:
loras = json.load(f)
# Initialize the base model
base_model = models[0]
controlnet_model_union_repo = 'InstantX/FLUX.1-dev-Controlnet-Union'
#controlnet_model_union_repo = 'Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro'
dtype = torch.bfloat16
#dtype = torch.float8_e4m3fn
CACHE_MODEL = False
device = "cuda" if torch.cuda.is_available() else "cpu"
taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype, token=HF_TOKEN)
good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype, token=HF_TOKEN)
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1, token=HF_TOKEN)
if CACHE_MODEL:
taef1.to(device)
good_vae.to(device)
pipe.to(device)
pipe.transformer.to("cpu")
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, token=HF_TOKEN)
pipe_ip = AutoPipelineForInpainting.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, token=HF_TOKEN)
controlnet_union = None
controlnet = None
last_model = models[0]
last_cn_on = False
#controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union_repo, torch_dtype=dtype)
#controlnet = FluxMultiControlNetModel([controlnet_union])
#controlnet.config = controlnet_union.config
MAX_SEED = 2**32-1
def unload_lora():
global pipe, pipe_i2i, pipe_ip
try:
#pipe.unfuse_lora()
pipe.unload_lora_weights()
#pipe_i2i.unfuse_lora()
pipe_i2i.unload_lora_weights()
#pipe_ip.unfuse_lora()
pipe_ip.unload_lora_weights()
except Exception as e:
print(e)
def download_file_mod(url, directory=os.getcwd()):
path = download_hf_file(directory, url, hf_token=HF_TOKEN)
if not path: raise Exception(f"Download error: {url}")
return path
# https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union
# https://huggingface.co/spaces/jiuface/FLUX.1-dev-Controlnet-Union
# https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux
#@spaces.GPU()
def change_base_model(repo_id: str, cn_on: bool, disable_model_cache: bool, model_type: str, progress=gr.Progress(track_tqdm=True)):
global pipe, pipe_i2i, pipe_ip, taef1, good_vae, controlnet_union, controlnet, last_model, last_cn_on, dtype
safetensors_file = None
single_file_base_model = single_file_base_models.get(model_type, models[0])
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)
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)) and not ".safetensors" in repo_id): return gr.update()
unload_lora()
pipe.to("cpu")
pipe_i2i.to("cpu")
pipe_ip.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, token=HF_TOKEN)
controlnet = FluxMultiControlNetModel([controlnet_union])
controlnet.config = controlnet_union.config
if ".safetensors" in repo_id:
safetensors_file = download_file_mod(repo_id)
transformer = FluxTransformer2DModel.from_single_file(safetensors_file, subfolder="transformer", torch_dtype=dtype, config=single_file_base_model)
if CACHE_MODEL:
pipe = FluxControlNetPipeline.from_pretrained(single_file_base_model, controlnet=controlnet, vae=good_vae, transformer=transformer, text_encoder=pipe.text_encoder,
tokenizer=pipe.tokenizer, text_encoder_2=pipe.text_encoder_2, tokenizer_2=pipe.tokenizer_2, torch_dtype=dtype, token=HF_TOKEN)
else: pipe = FluxControlNetPipeline.from_pretrained(single_file_base_model, transformer=transformer, controlnet=controlnet, torch_dtype=dtype, token=HF_TOKEN)
pipe_i2i = FluxControlNetImg2ImgPipeline.from_pretrained(single_file_base_model, controlnet=controlnet, 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, token=HF_TOKEN)
pipe_ip = FluxControlNetInpaintPipeline.from_pretrained(single_file_base_model, controlnet=controlnet, 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, token=HF_TOKEN)
else:
if CACHE_MODEL:
transformer = FluxTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", torch_dtype=dtype, token=HF_TOKEN)
pipe = FluxControlNetPipeline.from_pretrained(repo_id, controlnet=controlnet, vae=good_vae, transformer=transformer, text_encoder=pipe.text_encoder,
tokenizer=pipe.tokenizer, text_encoder_2=pipe.text_encoder_2, tokenizer_2=pipe.tokenizer_2, torch_dtype=dtype, token=HF_TOKEN)
pipe = FluxControlNetPipeline.from_pretrained(repo_id, controlnet=controlnet, torch_dtype=dtype, token=HF_TOKEN)
pipe_i2i = FluxControlNetImg2ImgPipeline.from_pretrained(repo_id, controlnet=controlnet, 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, token=HF_TOKEN)
pipe_ip = FluxControlNetInpaintPipeline.from_pretrained(repo_id, controlnet=controlnet, 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, token=HF_TOKEN)
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}")
if ".safetensors" in repo_id:
safetensors_file = download_file_mod(repo_id)
transformer = FluxTransformer2DModel.from_single_file(safetensors_file, subfolder="transformer", torch_dtype=dtype, config=single_file_base_model, token=HF_TOKEN)
if CACHE_MODEL:
pipe = DiffusionPipeline.from_pretrained(single_file_base_model, vae=taef1, transformer=transformer, text_encoder=pipe.text_encoder,
tokenizer=pipe.tokenizer, text_encoder_2=pipe.text_encoder_2, tokenizer_2=pipe.tokenizer_2, torch_dtype=dtype, token=HF_TOKEN)
else: pipe = DiffusionPipeline.from_pretrained(single_file_base_model, transformer=transformer, torch_dtype=dtype, token=HF_TOKEN)
pipe_i2i = AutoPipelineForImage2Image.from_pretrained(single_file_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, token=HF_TOKEN)
pipe_ip = AutoPipelineForInpainting.from_pretrained(single_file_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, token=HF_TOKEN)
else:
if CACHE_MODEL:
transformer = FluxTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", torch_dtype=dtype, token=HF_TOKEN)
pipe = DiffusionPipeline.from_pretrained(repo_id, vae=taef1, transformer=transformer, text_encoder=pipe.text_encoder,
tokenizer=pipe.tokenizer, text_encoder_2=pipe.text_encoder_2, tokenizer_2=pipe.tokenizer_2, torch_dtype=dtype, token=HF_TOKEN)
else: pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=dtype, token=HF_TOKEN)
pipe_i2i = AutoPipelineForImage2Image.from_pretrained(repo_id, 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, token=HF_TOKEN)
pipe_ip = AutoPipelineForInpainting.from_pretrained(repo_id, 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, token=HF_TOKEN)
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: {repo_id} {e}")
raise gr.Error(f"Model load Error: {repo_id} {e}") from e
finally:
if safetensors_file and Path(safetensors_file).exists(): Path(safetensors_file).unlink()
return gr.update()
change_base_model.zerogpu = True
def is_repo_public(repo_id: str):
api = HfApi()
try:
if api.repo_exists(repo_id=repo_id, token=False): return True
else: return False
except Exception as e:
print(f"Error: Failed to connect {repo_id}. {e}")
return False
def calc_sigmas(num_inference_steps: int, sigmas_factor: float):
sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
sigmas = sigmas * sigmas_factor
return sigmas
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 download_file(url, directory=None):
if directory is None:
directory = os.getcwd() # Use current working directory if not specified
# Get the filename from the URL
filename = url.split('/')[-1]
# Full path for the downloaded file
filepath = os.path.join(directory, filename)
# Download the file
response = requests.get(url)
response.raise_for_status() # Raise an exception for bad status codes
# Write the content to the file
with open(filepath, 'wb') as file:
file.write(response.content)
return filepath
def update_selection(evt: gr.SelectData, selected_indices, loras_state, width, height):
selected_index = evt.index
selected_indices = selected_indices or []
if selected_index in selected_indices:
selected_indices.remove(selected_index)
else:
if len(selected_indices) < 2:
selected_indices.append(selected_index)
else:
gr.Warning("You can select up to 2 LoRAs, remove one to select a new one.")
return gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), width, height, gr.update(), gr.update()
selected_info_1 = "Select a LoRA 1"
selected_info_2 = "Select a LoRA 2"
lora_scale_1 = 1.15
lora_scale_2 = 1.15
lora_image_1 = None
lora_image_2 = None
if len(selected_indices) >= 1:
lora1 = loras_state[selected_indices[0]]
selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
lora_image_1 = lora1['image']
if len(selected_indices) >= 2:
lora2 = loras_state[selected_indices[1]]
selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
lora_image_2 = lora2['image']
if selected_indices:
last_selected_lora = loras_state[selected_indices[-1]]
new_placeholder = f"Type a prompt for {last_selected_lora['title']}"
else:
new_placeholder = "Type a prompt"
return gr.update(placeholder=new_placeholder), selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, width, height, lora_image_1, lora_image_2
def remove_lora_1(selected_indices, loras_state):
if len(selected_indices) >= 1:
selected_indices.pop(0)
selected_info_1 = "Select LoRA 1"
selected_info_2 = "Select LoRA 2"
lora_scale_1 = 1.15
lora_scale_2 = 1.15
lora_image_1 = None
lora_image_2 = None
if len(selected_indices) >= 1:
lora1 = loras_state[selected_indices[0]]
selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
lora_image_1 = lora1['image']
if len(selected_indices) >= 2:
lora2 = loras_state[selected_indices[1]]
selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
lora_image_2 = lora2['image']
return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2
def remove_lora_2(selected_indices, loras_state):
if len(selected_indices) >= 2:
selected_indices.pop(1)
selected_info_1 = "Select LoRA 1"
selected_info_2 = "Select LoRA 2"
lora_scale_1 = 1.15
lora_scale_2 = 1.15
lora_image_1 = None
lora_image_2 = None
if len(selected_indices) >= 1:
lora1 = loras_state[selected_indices[0]]
selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
lora_image_1 = lora1['image']
if len(selected_indices) >= 2:
lora2 = loras_state[selected_indices[1]]
selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
lora_image_2 = lora2['image']
return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2
def randomize_loras(selected_indices, loras_state):
if len(loras_state) < 2:
raise gr.Error("Not enough LoRAs to randomize.")
selected_indices = random.sample(range(len(loras_state)), 2)
lora1 = loras_state[selected_indices[0]]
lora2 = loras_state[selected_indices[1]]
selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
lora_scale_1 = 1.15
lora_scale_2 = 1.15
lora_image_1 = lora1['image']
lora_image_2 = lora2['image']
random_prompt = random.choice(prompt_values)
return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2, random_prompt
def download_loras_images(loras_json_orig: list[dict]):
api = HfApi(token=HF_TOKEN)
loras_json = []
for lora in loras_json_orig:
repo = lora.get("repo", None)
if repo is None or not api.repo_exists(repo_id=repo, token=HF_TOKEN):
print(f"LoRA '{repo}' is not exsit.")
continue
if "title" not in lora.keys() or "trigger_word" not in lora.keys() or "image" not in lora.keys():
title, _repo, _path, trigger_word, image_def = check_custom_model(repo)
if "title" not in lora.keys(): lora["title"] = title
if "trigger_word" not in lora.keys(): lora["trigger_word"] = trigger_word
if "image" not in lora.keys(): lora["image"] = image_def
image = lora.get("image", None)
try:
if not is_repo_public(repo) and image is not None and "http" in image and repo in image: image = download_file_mod(image)
lora["image"] = image if image else "/home/user/app/custom.png"
except Exception as e:
print(f"Failed to download LoRA '{repo}''s image '{image if image else ''}'. {e}")
lora["image"] = "/home/user/app/custom.png"
loras_json.append(lora)
return loras_json
def add_custom_lora(custom_lora, selected_indices, current_loras, gallery):
if custom_lora:
try:
title, repo, path, trigger_word, image = check_custom_model(custom_lora)
if image is not None and "http" in image and not is_repo_public(repo) and repo in image:
try:
image = download_file_mod(image)
except Exception as e:
print(e)
image = None
print(f"Loaded custom LoRA: {repo}")
existing_item_index = next((index for (index, item) in enumerate(current_loras) if item['repo'] == repo), None)
if existing_item_index is None:
if repo.endswith(".safetensors") and repo.startswith("http"):
#repo = download_file(repo)
repo = download_file_mod(repo)
new_item = {
"image": image if image else "/home/user/app/custom.png",
"title": title,
"repo": repo,
"weights": path,
"trigger_word": trigger_word
}
print(f"New LoRA: {new_item}")
existing_item_index = len(current_loras)
current_loras.append(new_item)
# Update gallery
gallery_items = [(item["image"], item["title"]) for item in current_loras]
# Update selected_indices if there's room
if len(selected_indices) < 2:
selected_indices.append(existing_item_index)
else:
gr.Warning("You can select up to 2 LoRAs, remove one to select a new one.")
# Update selected_info and images
selected_info_1 = "Select a LoRA 1"
selected_info_2 = "Select a LoRA 2"
lora_scale_1 = 1.15
lora_scale_2 = 1.15
lora_image_1 = None
lora_image_2 = None
if len(selected_indices) >= 1:
lora1 = current_loras[selected_indices[0]]
selected_info_1 = f"### LoRA 1 Selected: {lora1['title']} ✨"
lora_image_1 = lora1['image'] if lora1['image'] else None
if len(selected_indices) >= 2:
lora2 = current_loras[selected_indices[1]]
selected_info_2 = f"### LoRA 2 Selected: {lora2['title']} ✨"
lora_image_2 = lora2['image'] if lora2['image'] else None
print("Finished adding custom LoRA")
return (
current_loras,
gr.update(value=gallery_items),
selected_info_1,
selected_info_2,
selected_indices,
lora_scale_1,
lora_scale_2,
lora_image_1,
lora_image_2
)
except Exception as e:
print(e)
gr.Warning(str(e))
return current_loras, gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update()
else:
return current_loras, gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update()
def remove_custom_lora(selected_indices, current_loras, gallery):
if current_loras:
custom_lora_repo = current_loras[-1]['repo']
# Remove from loras list
current_loras = current_loras[:-1]
# Remove from selected_indices if selected
custom_lora_index = len(current_loras)
if custom_lora_index in selected_indices:
selected_indices.remove(custom_lora_index)
# Update gallery
gallery_items = [(item["image"], item["title"]) for item in current_loras]
# Update selected_info and images
selected_info_1 = "Select a LoRA 1"
selected_info_2 = "Select a LoRA 2"
lora_scale_1 = 1.15
lora_scale_2 = 1.15
lora_image_1 = None
lora_image_2 = None
if len(selected_indices) >= 1:
lora1 = current_loras[selected_indices[0]]
selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
lora_image_1 = lora1['image']
if len(selected_indices) >= 2:
lora2 = current_loras[selected_indices[1]]
selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
lora_image_2 = lora2['image']
return (
current_loras,
gr.update(value=gallery_items),
selected_info_1,
selected_info_2,
selected_indices,
lora_scale_1,
lora_scale_2,
lora_image_1,
lora_image_2
)
@spaces.GPU(duration=70)
@torch.inference_mode()
def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, sigmas_factor, cn_on, progress=gr.Progress(track_tqdm=True)):
global pipe, taef1, good_vae, controlnet, controlnet_union
try:
good_vae.to(device)
taef1.to(device)
generator = torch.Generator(device=device).manual_seed(int(float(seed)))
sigmas = calc_sigmas(steps, sigmas_factor)
with calculateDuration("Generating image"):
# Generate image
modes, images, scales = get_control_params()
if not cn_on or len(modes) == 0:
pipe.to(device)
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": 1.0},
output_type="pil",
good_vae=good_vae,
sigmas=sigmas,
):
yield img
else:
pipe.to(device)
pipe.vae = good_vae
if controlnet_union is not None: controlnet_union.to(device)
if controlnet is not None: controlnet.to(device)
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": 1.0},
sigmas=sigmas,
).images:
yield img
except Exception as e:
print(e)
raise gr.Error(f"Inference Error: {e}") from e
@spaces.GPU(duration=70)
@torch.inference_mode()
def generate_image_to_image(prompt_mash, image_input_path_dict, image_strength, is_inpaint, blur_mask, blur_factor, steps, cfg_scale, width, height,
sigmas_factor, seed, cn_on, progress=gr.Progress(track_tqdm=True)):
global pipe_i2i, pipe_ip, good_vae, controlnet, controlnet_union
try:
good_vae.to(device)
generator = torch.Generator(device=device).manual_seed(int(float(seed)))
image_input_path = image_input_path_dict['background']
mask_path = image_input_path_dict['layers'][0]
sigmas = calc_sigmas(steps, sigmas_factor)
with calculateDuration("Generating image"):
# Generate image
modes, images, scales = get_control_params()
if not cn_on or len(modes) == 0:
if is_inpaint: # Inpainting
pipe_ip.to(device)
pipe_ip.vae = good_vae
image_input = load_image(image_input_path)
mask_input = load_image(mask_path)
if blur_mask: mask_input = pipe_ip.mask_processor.blur(mask_input, blur_factor=blur_factor)
progress(0, desc="Start Inpainting Inference.")
final_image = pipe_ip(
prompt=prompt_mash,
image=image_input,
mask_image=mask_input,
strength=image_strength,
num_inference_steps=steps,
guidance_scale=cfg_scale,
width=width,
height=height,
generator=generator,
joint_attention_kwargs={"scale": 1.0},
output_type="pil",
#sigmas=sigmas,
).images[0]
return final_image
else:
pipe_i2i.to(device)
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": 1.0},
output_type="pil",
#sigmas=sigmas,
).images[0]
return final_image
else:
if is_inpaint: # Inpainting
pipe_ip.to(device)
pipe_ip.vae = good_vae
image_input = load_image(image_input_path)
mask_input = load_image(mask_path)
if blur_mask: mask_input = pipe_ip.mask_processor.blur(mask_input, blur_factor=blur_factor)
if controlnet_union is not None: controlnet_union.to(device)
if controlnet is not None: controlnet.to(device)
pipe_ip.enable_model_cpu_offload()
progress(0, desc="Start Inpainting Inference with ControlNet.")
final_image = pipe_ip(
prompt=prompt_mash,
control_image=images,
control_mode=modes,
image=image_input,
mask_image=mask_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": 1.0},
output_type="pil",
#sigmas=sigmas,
).images[0]
return final_image
else:
pipe_i2i.to(device)
pipe_i2i.vae = good_vae
image_input = load_image(image_input_path['background'])
if controlnet_union is not None: controlnet_union.to(device)
if controlnet is not None: controlnet.to(device)
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": 1.0},
output_type="pil",
#sigmas=sigmas,
).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, task_type, blur_mask, blur_factor, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2,
randomize_seed, seed, width, height, sigmas_factor, loras_state, lora_json, cn_on, translate_on, progress=gr.Progress(track_tqdm=True)):
global pipe, pipe_i2i, pipe_ip
if not selected_indices 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.")
selected_loras = [loras_state[idx] for idx in selected_indices]
if task_type == "Inpainting":
is_inpaint = True
is_i2i = True
elif task_type == "Image-to-Image":
is_inpaint = False
is_i2i = True
else: # "Text-to-Image"
is_inpaint = False
is_i2i = False
if translate_on: prompt = translate_to_en(prompt)
# Build the prompt with trigger words
prepends = []
appends = []
for lora in selected_loras:
trigger_word = lora.get('trigger_word', '')
if trigger_word:
if lora.get("trigger_position") == "prepend":
prepends.append(trigger_word)
else:
appends.append(trigger_word)
prompt_mash = " ".join(prepends + [prompt] + appends)
print("Prompt Mash: ", prompt_mash) #
# Unload previous LoRA weights
with calculateDuration("Unloading LoRA"):
unload_lora()
print(pipe.get_active_adapters()) #
print(pipe_i2i.get_active_adapters()) #
print(pipe_ip.get_active_adapters()) #
clear_cache() #
# Build the prompt for External LoRAs
prompt_mash = prompt_mash + get_model_trigger(last_model)
lora_names = []
lora_weights = []
if is_valid_lora(lora_json): # Load External LoRA weights
with calculateDuration("Loading External LoRA weights"):
if is_inpaint:
pipe_ip, lora_names, lora_weights = fuse_loras(pipe_ip, lora_json)
elif is_i2i:
pipe_i2i, lora_names, lora_weights = fuse_loras(pipe_i2i, lora_json)
else: pipe, lora_names, lora_weights = fuse_loras(pipe, lora_json)
trigger_word = get_trigger_word(lora_json)
prompt_mash = f"{prompt_mash} {trigger_word}"
print("Prompt Mash: ", prompt_mash) #
# Load LoRA weights with respective scales
if selected_indices:
with calculateDuration("Loading LoRA weights"):
for idx, lora in enumerate(selected_loras):
lora_name = f"lora_{idx}"
lora_names.append(lora_name)
print(f"Lora Name: {lora_name}")
lora_weights.append(lora_scale_1 if idx == 0 else lora_scale_2)
lora_path = lora['repo']
weight_name = lora.get("weights")
print(f"Lora Path: {lora_path}")
if is_inpaint:
pipe_ip.load_lora_weights(
lora_path,
weight_name=weight_name if weight_name else None,
low_cpu_mem_usage=False,
adapter_name=lora_name,
token=HF_TOKEN
)
elif is_i2i:
pipe_i2i.load_lora_weights(
lora_path,
weight_name=weight_name if weight_name else None,
low_cpu_mem_usage=False,
adapter_name=lora_name,
token=HF_TOKEN
)
else:
pipe.load_lora_weights(
lora_path,
weight_name=weight_name if weight_name else None,
low_cpu_mem_usage=False,
adapter_name=lora_name,
token=HF_TOKEN
)
print("Loaded LoRAs:", lora_names)
if selected_indices or is_valid_lora(lora_json):
if is_inpaint:
pipe_ip.set_adapters(lora_names, adapter_weights=lora_weights)
elif is_i2i:
pipe_i2i.set_adapters(lora_names, adapter_weights=lora_weights)
else:
pipe.set_adapters(lora_names, adapter_weights=lora_weights)
print(pipe.get_active_adapters()) #
print(pipe_i2i.get_active_adapters()) #
print(pipe_ip.get_active_adapters()) #
# Set random seed for reproducibility
with calculateDuration("Randomizing seed"):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
# Generate image
progress(0, desc="Running Inference.")
if is_i2i:
final_image = generate_image_to_image(prompt_mash, image_input, image_strength, is_inpaint, blur_mask, blur_factor,
steps, cfg_scale, width, height, sigmas_factor, seed, cn_on)
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, sigmas_factor, cn_on)
# Consume the generator to get the final image
final_image = None
step_counter = 0
for image in image_generator:
step_counter+=1
final_image = image
progress_bar = f''
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)
run_lora.zerogpu = True
def get_huggingface_safetensors(link):
split_link = link.split("/")
if len(split_link) == 2:
model_card = ModelCard.load(link, token=HF_TOKEN)
base_model = model_card.data.get("base_model")
print(f"Base model: {base_model}")
if base_model not in ["black-forest-labs/FLUX.1-dev", "black-forest-labs/FLUX.1-schnell"]:
#raise Exception("Not a FLUX LoRA!")
gr.Warning("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(token=HF_TOKEN)
safetensors_name = None
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)
raise gr.Error("Invalid Hugging Face repository with a *.safetensors LoRA")
if not safetensors_name:
raise gr.Error("No *.safetensors file found in the repository")
return split_link[1], link, safetensors_name, trigger_word, image_url
else:
raise gr.Error("Invalid Hugging Face repository link")
def check_custom_model(link):
if link.endswith(".safetensors"):
# Treat as direct link to the LoRA weights
title = os.path.basename(link)
repo = link
path = None # No specific weight name
trigger_word = ""
image_url = None
return title, repo, path, trigger_word, image_url
elif link.startswith("https://"):
if "huggingface.co" in link:
link_split = link.split("huggingface.co/")
return get_huggingface_safetensors(link_split[1])
else:
raise Exception("Unsupported URL")
else:
# Assume it's a Hugging Face model path
return get_huggingface_safetensors(link)
def update_history(new_image, history):
"""Updates the history gallery with the new image."""
if history is None:
history = []
history.insert(0, new_image)
return history
loras = download_loras_images(loras)
css = '''
#gen_column{align-self: stretch}
#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: 0.25em}
#gallery .grid-wrap{height: 5vh}
#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}
.custom_lora_card{margin-bottom: 1em}
.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}
#component-8, .button_total{height: 100%; align-self: stretch;}
#loaded_loras [data-testid="block-info"]{font-size:80%}
#custom_lora_structure{background: var(--block-background-fill)}
#custom_lora_btn{margin-top: auto;margin-bottom: 11px}
#random_btn{font-size: 300%}
#component-11{align-self: stretch;}
.info { align-items: center; text-align: center; }
.desc [src$='#float'] { float: right; margin: 20px; }
'''
with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', fill_width=True, css=css, delete_cache=(60, 3600)) as app:
with gr.Tab("FLUX LoRA the Explorer"):
title = gr.HTML(
"""
FLUX LoRA the Explorer Mod
""",
elem_id="title",
)
loras_state = gr.State(loras)
selected_indices = gr.State([])
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", elem_classes=["button_total"])
with gr.Row(elem_id="loaded_loras"):
with gr.Column(scale=1, min_width=25):
randomize_button = gr.Button("🎲", variant="secondary", scale=1, elem_id="random_btn")
with gr.Column(scale=8):
with gr.Row():
with gr.Column(scale=0, min_width=50):
lora_image_1 = gr.Image(label="LoRA 1 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
with gr.Column(scale=3, min_width=100):
selected_info_1 = gr.Markdown("Select a LoRA 1")
with gr.Column(scale=5, min_width=50):
lora_scale_1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
with gr.Row():
remove_button_1 = gr.Button("Remove", size="sm")
with gr.Column(scale=8):
with gr.Row():
with gr.Column(scale=0, min_width=50):
lora_image_2 = gr.Image(label="LoRA 2 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
with gr.Column(scale=3, min_width=100):
selected_info_2 = gr.Markdown("Select a LoRA 2")
with gr.Column(scale=5, min_width=50):
lora_scale_2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
with gr.Row():
remove_button_2 = gr.Button("Remove", size="sm")
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=4, elem_id="gallery", show_share_button=False, interactive=False)
with gr.Group():
with gr.Row(elem_id="custom_lora_structure"):
custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path or *.safetensors public URL", placeholder="multimodalart/vintage-ads-flux", scale=3, min_width=150)
add_custom_lora_button = gr.Button("Add Custom LoRA", elem_id="custom_lora_btn", scale=2, min_width=150)
remove_custom_lora_button = gr.Button("Remove Custom LoRA", visible=False)
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")
with gr.Column():
progress_bar = gr.Markdown(elem_id="progress",visible=False)
result = gr.Image(label="Generated Image", format="png", type="filepath", show_share_button=False, interactive=False)
with gr.Accordion("History", open=False):
history_files = gr.Files(interactive=False, visible=False)
history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False, format="png",
show_share_button=False, show_download_button=True)
history_clear_button = gr.Button(value="Clear History", variant="secondary")
history_clear_button.click(lambda: ([], []), None, [history_gallery, history_files], queue=False, show_api=False)
with gr.Row():
with gr.Accordion("Advanced Settings", open=True):
with gr.Tab("Generation Settings"):
with gr.Column():
with gr.Group():
with gr.Row():
model_name = gr.Dropdown(label="Base Model", info="You can enter a huggingface model repo_id or path of single safetensors file to want to use.",
choices=models, value=models[0], allow_custom_value=True, min_width=320, scale=5)
model_type = gr.Radio(label="Model type", info="Model type of single safetensors file",
choices=list(single_file_base_models.keys()), value=list(single_file_base_models.keys())[0], scale=1)
model_info = gr.Markdown(elem_classes="info")
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)
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)
sigmas_factor = gr.Slider(label="Sigmas factor", minimum=0.01, maximum=1.00, step=0.01, value=0.95)
disable_model_cache = gr.Checkbox(False, label="Disable model caching")
with gr.Tab("Image-to-Image"):
with gr.Row():
with gr.Column():
#input_image = gr.Image(label="Input image", type="filepath", height=256, sources=["upload", "clipboard"], show_share_button=False)
input_image = gr.ImageEditor(label='Input image', type='filepath', sources=["upload", "clipboard"], image_mode='RGB', show_share_button=False, show_fullscreen_button=False,
layers=False, brush=gr.Brush(colors=["white"], color_mode="fixed", default_size=32), eraser=gr.Eraser(default_size="32"), value=None,
canvas_size=(384, 384), width=384, height=512)
with gr.Column():
task_type = gr.Radio(label="Task", choices=["Text-to-Image", "Image-to-Image", "Inpainting"], value="Text-to-Image")
image_strength = gr.Slider(label="Strength", info="Lower means more image influence in I2I, opposite in Inpaint", minimum=0.01, maximum=1.0, step=0.01, value=0.75)
blur_mask = gr.Checkbox(label="Blur mask", value=False)
blur_factor = gr.Slider(label="Blur factor", minimum=0, maximum=50, step=1, value=33)
input_image_preprocess = gr.Checkbox(True, label="Preprocess Input image")
with gr.Tab("More LoRA"):
with gr.Accordion("External LoRA", open=True):
with gr.Column():
deselect_lora_button = gr.Button("Remove External LoRAs", variant="secondary")
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, min_width=320)
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_basemodel = gr.CheckboxGroup(label="Search LoRA for", choices=["Flux.1 D", "Flux.1 S"], value=["Flux.1 D"])
lora_search_civitai_sort = gr.Radio(label="Sort", choices=CIVITAI_SORT, value="Most Downloaded")
lora_search_civitai_period = gr.Radio(label="Period", choices=CIVITAI_PERIOD, value="Month")
with gr.Row():
lora_search_civitai_query = gr.Textbox(label="Query", placeholder="flux", lines=1)
lora_search_civitai_tag = gr.Dropdown(label="Tag", choices=get_civitai_tag(), value=get_civitai_tag()[0], allow_custom_value=True)
lora_search_civitai_user = gr.Textbox(label="Username", lines=1)
lora_search_civitai_submit = gr.Button("Search on Civitai")
with gr.Row():
lora_search_civitai_json = gr.JSON(value={}, visible=False)
lora_search_civitai_desc = gr.Markdown(value="", visible=False, elem_classes="desc")
with gr.Accordion("Select from Gallery", open=False):
lora_search_civitai_gallery = gr.Gallery([], label="Results", allow_preview=False, columns=5, show_share_button=False, interactive=False)
lora_search_civitai_result = gr.Dropdown(label="Search Results", choices=[("", "")], value="", allow_custom_value=True, visible=False)
lora_download_url = gr.Textbox(label="LoRA URL", placeholder="https://civitai.com/api/download/models/28907", 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.Tab("ControlNet", visible=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():
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=[selected_indices, loras_state, width, height],
outputs=[prompt, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, width, height, lora_image_1, lora_image_2])
remove_button_1.click(
remove_lora_1,
inputs=[selected_indices, loras_state],
outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
)
remove_button_2.click(
remove_lora_2,
inputs=[selected_indices, loras_state],
outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
)
randomize_button.click(
randomize_loras,
inputs=[selected_indices, loras_state],
outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2, prompt]
)
add_custom_lora_button.click(
add_custom_lora,
inputs=[custom_lora, selected_indices, loras_state, gallery],
outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
)
remove_custom_lora_button.click(
remove_custom_lora,
inputs=[selected_indices, loras_state, gallery],
outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
)
gr.on(
triggers=[generate_button.click, prompt.submit],
fn=change_base_model,
inputs=[model_name, cn_on, disable_model_cache, model_type],
outputs=[result],
queue=True,
show_api=False,
trigger_mode="once",
).success(
fn=run_lora,
inputs=[prompt, input_image, image_strength, task_type, blur_mask, blur_factor, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2,
randomize_seed, seed, width, height, sigmas_factor, loras_state, lora_repo_json, cn_on, auto_trans],
outputs=[result, seed, progress_bar],
queue=True,
show_api=True,
#).then( # Update the history gallery
# fn=lambda x, history: update_history(x, history),
# inputs=[result, history_gallery],
# outputs=history_gallery,
).success(save_image_history, [result, history_gallery, history_files, model_name], [history_gallery, history_files], queue=False, show_api=False)
input_image.clear(lambda: gr.update(value="Text-to-Image"), None, [task_type], queue=False, show_api=False)
input_image.upload(preprocess_i2i_image, [input_image, input_image_preprocess, height, width], [input_image], queue=False, show_api=False)\
.success(lambda: gr.update(value="Image-to-Image"), None, [task_type], 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, model_type], [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, lora_search_civitai_sort, lora_search_civitai_period,
lora_search_civitai_tag, lora_search_civitai_user, lora_search_civitai_gallery],
outputs=[lora_search_civitai_result, lora_search_civitai_desc, lora_search_civitai_submit, lora_search_civitai_query, lora_search_civitai_gallery],
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)
lora_search_civitai_gallery.select(update_civitai_selection, None, [lora_search_civitai_result], 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_flux,
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_flux,
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_flux, [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)
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
]
)
with gr.Tab("PNG Info"):
def extract_exif_data(image):
if image is None: return ""
try:
metadata_keys = ['parameters', 'metadata', 'prompt', 'Comment']
for key in metadata_keys:
if key in image.info:
return image.info[key]
return str(image.info)
except Exception as e:
return f"Error extracting metadata: {str(e)}"
with gr.Row():
with gr.Column():
image_metadata = gr.Image(label="Image with metadata", type="pil", sources=["upload"])
with gr.Column():
result_metadata = gr.Textbox(label="Metadata", show_label=True, show_copy_button=True, interactive=False, container=True, max_lines=99)
image_metadata.change(
fn=extract_exif_data,
inputs=[image_metadata],
outputs=[result_metadata],
)
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(ssr_mode=False)