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