mod.py

import gradio as gr
import torch
import spaces
from diffusers import DiffusionPipeline
from diffusers.pipelines.flux.pipeline_flux_controlnet import FluxControlNetPipeline
from diffusers.models.controlnet_flux import FluxControlNetModel, FluxMultiControlNetModel
from pathlib import Path
import gc
import subprocess
from PIL import Image


subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
subprocess.run('pip cache purge', shell=True)
device = "cuda" if torch.cuda.is_available() else "cpu"
torch.set_grad_enabled(False)


models = [
    "camenduru/FLUX.1-dev-diffusers",
    "black-forest-labs/FLUX.1-schnell",
    "sayakpaul/FLUX.1-merged",
    "John6666/blue-pencil-flux1-v001-fp8-flux",
    "John6666/copycat-flux-test-fp8-v11-fp8-flux",
    "John6666/nepotism-fuxdevschnell-v3aio-fp8-flux",
    "John6666/niji-style-flux-devfp8-fp8-flux",
    "John6666/fluxunchained-artfulnsfw-fut516xfp8e4m3fnv11-fp8-flux",
    "John6666/fastflux-unchained-t5f16-fp8-flux",
    "John6666/the-araminta-flux1a1-fp8-flux",
    "John6666/acorn-is-spinning-flux-v11-fp8-flux",
    "John6666/fluxescore-dev-v10fp16-fp8-flux",
    # "",
]


num_loras = 3
num_cns = 2
# Initialize the base model
base_model = models[0]
controlnet_model_union_repo = 'InstantX/FLUX.1-dev-Controlnet-Union'
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
controlnet = None
control_images = [None] * num_cns
control_modes = [-1] * num_cns
control_scales = [0] * num_cns
last_model = models[0]
last_cn_on = False

def is_repo_name(s):
    import re
    return re.fullmatch(r'^[^/,\s\"\']+/[^/,\s\"\']+$', s)


def is_repo_exists(repo_id):
    from huggingface_hub import HfApi
    api = HfApi()
    try:
        if api.repo_exists(repo_id=repo_id): return True
        else: return False
    except Exception as e:
        print(f"Error: Failed to connect {repo_id}. ")
        print(e)
        return True # for safe


def clear_cache():
    torch.cuda.empty_cache()
    gc.collect()


def get_repo_safetensors(repo_id: str):
    from huggingface_hub import HfApi
    api = HfApi()
    try:
        if not is_repo_name(repo_id) or not is_repo_exists(repo_id): return gr.update(value="", choices=[])
        files = api.list_repo_files(repo_id=repo_id)
    except Exception as e:
        print(f"Error: Failed to get {repo_id}'s info.")
        print(e)
        return gr.update(choices=[])
    files = [f for f in files if f.endswith(".safetensors")]
    if len(files) == 0: return gr.update(value="", choices=[])
    else: return gr.update(value=files[0], choices=files)


# https://huggingface.co/spaces/DamarJati/FLUX.1-DEV-Canny
# 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, progress=gr.Progress(track_tqdm=True)):
    global pipe
    global controlnet
    global last_model
    global last_cn_on
    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
        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=torch.bfloat16)
            controlnet = FluxMultiControlNetModel([controlnet_union])
            pipe = FluxControlNetPipeline.from_pretrained(repo_id, controlnet=controlnet, torch_dtype=torch.bfloat16)
            last_model = repo_id
            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=torch.bfloat16)
            last_model = repo_id
            progress(1, desc=f"Model loaded: {repo_id}")
            print(f"Model loaded: {repo_id}")
    except Exception as e:
        print(e)
    return gr.update(visible=True)


# https://huggingface.co/spaces/DamarJati/FLUX.1-DEV-Canny/blob/main/app.py
def resize_image(image, target_width, target_height, crop=True):
    from image_datasets.canny_dataset import c_crop
    if crop:
        image = c_crop(image)  # Crop the image to square
        original_width, original_height = image.size

        # Resize to match the target size without stretching
        scale = max(target_width / original_width, target_height / original_height)
        resized_width = int(scale * original_width)
        resized_height = int(scale * original_height)

        image = image.resize((resized_width, resized_height), Image.LANCZOS)
        
        # Center crop to match the target dimensions
        left = (resized_width - target_width) // 2
        top = (resized_height - target_height) // 2
        image = image.crop((left, top, left + target_width, top + target_height))
    else:
        image = image.resize((target_width, target_height), Image.LANCZOS)
    
    return image


# https://huggingface.co/spaces/jiuface/FLUX.1-dev-Controlnet-Union/blob/main/app.py
controlnet_union_modes = {
    "None": -1,
    #"scribble_hed": 0,
    "canny": 0, # supported
    "mlsd": 0, #supported
    "tile": 1, #supported
    "depth_midas": 2, # supported
    "blur": 3, # supported
    "openpose": 4,  # supported
    "gray": 5,  # supported
    "low_quality": 6,  # supported
}


def get_control_params():
    modes = []
    images = []
    scales = []
    for i, mode in enumerate(control_modes):
        if mode == -1 or control_images[i] is None: continue
        modes.append(control_modes[i])
        images.append(control_images[i])
        scales.append(control_scales[i])
    return modes, images, scales


from preprocessor import Preprocessor
def preprocess_image(image: Image.Image, control_mode: str, height: int, width: int, preprocess_resolution: int):
    image_resolution = max(width, height)
    image_before = resize_image(image, image_resolution, image_resolution, True)
    # generated control_
    print("start to generate control image")
    preprocessor = Preprocessor()
    if control_mode == "depth_midas":
        preprocessor.load("Midas")
        control_image = preprocessor(
            image=image_before,
            image_resolution=image_resolution,
            detect_resolution=preprocess_resolution,
        )
    if control_mode == "openpose":
        preprocessor.load("Openpose")
        control_image = preprocessor(
            image=image_before,
            hand_and_face=True,
            image_resolution=image_resolution,
            detect_resolution=preprocess_resolution,
        )
    if control_mode == "canny":
        preprocessor.load("Canny")
        control_image = preprocessor(
            image=image_before,
            image_resolution=image_resolution,
            detect_resolution=preprocess_resolution,
        )

    if control_mode == "mlsd":
        preprocessor.load("MLSD")
        control_image = preprocessor(
            image=image_before,
            image_resolution=image_resolution,
            detect_resolution=preprocess_resolution,
        )

    if control_mode == "scribble_hed":
        preprocessor.load("HED")
        control_image = preprocessor(
            image=image_before,
            image_resolution=image_resolution,
            detect_resolution=preprocess_resolution,
        )
    
    if control_mode == "low_quality" or  control_mode == "gray" or  control_mode == "blur" or control_mode == "tile":
        control_image = image_before
        image_width = 768
        image_height = 768
    else:
        # make sure control image size is same as resized_image
        image_width, image_height =  control_image.size
    
    image_after = resize_image(control_image, width, height, True)
    print(f"generate control image success: {image_width}x{image_height} => {width}x{height}")

    return image_after


def get_control_union_mode():
    return list(controlnet_union_modes.keys())


def set_control_union_mode(i: int, mode: str, scale: str):
    global control_modes
    global control_scales
    control_modes[i] = controlnet_union_modes.get(mode, 0)
    control_scales[i] = scale
    if mode != "None": return True
    else: return gr.update(visible=True)


def set_control_union_image(i: int, mode: str, image: Image.Image, height: int, width: int, preprocess_resolution: int):
    global control_images
    control_images[i] = preprocess_image(image, mode, height, width, preprocess_resolution)
    return control_images[i]


def compose_lora_json(lorajson: list[dict], i: int, name: str, scale: float, filename: str, trigger: str):
    lorajson[i]["name"] = str(name) if name != "None" else ""
    lorajson[i]["scale"] = float(scale)
    lorajson[i]["filename"] = str(filename)
    lorajson[i]["trigger"] = str(trigger)
    return lorajson


def is_valid_lora(lorajson: list[dict]):
    valid = False
    for d in lorajson:
        if "name" in d.keys() and d["name"] and d["name"] != "None": valid = True
    return valid


def get_trigger_word(lorajson: list[dict]):
    trigger = ""
    for d in lorajson:
        if "name" in d.keys() and d["name"] and d["name"] != "None" and d["trigger"]:
            trigger += ", " + d["trigger"]
    return trigger


# https://huggingface.co/docs/diffusers/v0.23.1/en/api/loaders#diffusers.loaders.LoraLoaderMixin.fuse_lora
# https://github.com/huggingface/diffusers/issues/4919
def fuse_loras(pipe, lorajson: list[dict]):
    if not lorajson or not isinstance(lorajson, list): return
    a_list = []
    w_list = []
    for d in lorajson:
        if not d or not isinstance(d, dict) or not d["name"] or d["name"] == "None": continue
        k = d["name"]
        if is_repo_name(k) and is_repo_exists(k):
            a_name = Path(k).stem
            pipe.load_lora_weights(k, weight_name=d["filename"], adapter_name = a_name)
        elif not Path(k).exists():
            print(f"LoRA not found: {k}")
            continue
        else:
            w_name = Path(k).name
            a_name = Path(k).stem
            pipe.load_lora_weights(k, weight_name = w_name, adapter_name = a_name)
        a_list.append(a_name)
        w_list.append(d["scale"])
    if not a_list: return
    pipe.set_adapters(a_list, adapter_weights=w_list)
    pipe.fuse_lora(adapter_names=a_list, lora_scale=1.0)
    #pipe.unload_lora_weights()


def description_ui():
    gr.Markdown(
        """
- Mod of [multimodalart/flux-lora-the-explorer](https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer),
 [jiuface/FLUX.1-dev-Controlnet-Union](https://huggingface.co/spaces/jiuface/),
 [DamarJati/FLUX.1-DEV-Canny](https://huggingface.co/spaces/DamarJati/FLUX.1-DEV-Canny),
 [gokaygokay/FLUX-Prompt-Generator](https://huggingface.co/spaces/gokaygokay/FLUX-Prompt-Generator).
"""
    )


from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
def load_prompt_enhancer():
    try:
        model_checkpoint = "gokaygokay/Flux-Prompt-Enhance"
        tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
        model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint).eval().to(device=device)
        enhancer_flux = pipeline('text2text-generation', model=model, tokenizer=tokenizer, repetition_penalty=1.5, device=device)
    except Exception as e:
        print(e)
        enhancer_flux = None
    return enhancer_flux


enhancer_flux = load_prompt_enhancer()


@spaces.GPU(duration=30)
def enhance_prompt(input_prompt):
    result = enhancer_flux("enhance prompt: " + input_prompt, max_length = 256)
    enhanced_text = result[0]['generated_text']
    return enhanced_text


load_prompt_enhancer.zerogpu = True
change_base_model.zerogpu = True
fuse_loras.zerogpu = True