import os import json import copy import time import random import logging import numpy as np from typing import Any, Dict, List, Optional, Union import torch from PIL import Image import gradio as gr from diffusers import ( DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image, FluxPipeline, FlowMatchEulerDiscreteScheduler ) from huggingface_hub import ( hf_hub_download, HfFileSystem, ModelCard, snapshot_download ) from diffusers.utils import load_image import spaces # Import the prompt enhancer generator from enhance.py from enhance import generate as enhance_generate # Attempt to import loras from lora.py; otherwise use a default placeholder. try: from lora import loras except ImportError: loras = [ {"image": "placeholder.jpg", "title": "Placeholder LoRA", "repo": "placeholder/repo", "weights": None, "trigger_word": ""} ] #---if workspace = local or colab--- # (Optional: add Hugging Face login code here) def calculate_shift( image_seq_len, base_seq_len: int = 256, max_seq_len: int = 4096, base_shift: float = 0.5, max_shift: float = 1.16, ): m = (max_shift - base_shift) / (max_seq_len - base_seq_len) b = base_shift - m * base_seq_len mu = image_seq_len * m + b return mu def retrieve_timesteps( scheduler, num_inference_steps: Optional[int] = None, device: Optional[Union[str, torch.device]] = None, timesteps: Optional[List[int]] = None, sigmas: Optional[List[float]] = None, **kwargs, ): if timesteps is not None and sigmas is not None: raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") if timesteps is not None: scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) elif sigmas is not None: scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) else: scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) timesteps = scheduler.timesteps return timesteps, num_inference_steps # FLUX pipeline @torch.inference_mode() def flux_pipe_call_that_returns_an_iterable_of_images( self, prompt: Union[str, List[str]] = None, prompt_2: Optional[Union[str, List[str]]] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 28, timesteps: List[int] = None, guidance_scale: float = 3.5, num_images_per_prompt: Optional[int] = 1, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, pooled_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, joint_attention_kwargs: Optional[Dict[str, Any]] = None, max_sequence_length: int = 512, good_vae: Optional[Any] = None, ): height = height or self.default_sample_size * self.vae_scale_factor width = width or self.default_sample_size * self.vae_scale_factor self.check_inputs( prompt, prompt_2, height, width, prompt_embeds=prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds, max_sequence_length=max_sequence_length, ) self._guidance_scale = guidance_scale self._joint_attention_kwargs = joint_attention_kwargs self._interrupt = False batch_size = 1 if isinstance(prompt, str) else len(prompt) device = self._execution_device lora_scale = joint_attention_kwargs.get("scale", None) if joint_attention_kwargs is not None else None prompt_embeds, pooled_prompt_embeds, text_ids = self.encode_prompt( prompt=prompt, prompt_2=prompt_2, prompt_embeds=prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds, device=device, num_images_per_prompt=num_images_per_prompt, max_sequence_length=max_sequence_length, lora_scale=lora_scale, ) num_channels_latents = self.transformer.config.in_channels // 4 latents, latent_image_ids = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) image_seq_len = latents.shape[1] mu = calculate_shift( image_seq_len, self.scheduler.config.base_image_seq_len, self.scheduler.config.max_image_seq_len, self.scheduler.config.base_shift, self.scheduler.config.max_shift, ) timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, timesteps, sigmas, mu=mu, ) self._num_timesteps = len(timesteps) guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32).expand(latents.shape[0]) if self.transformer.config.guidance_embeds else None for i, t in enumerate(timesteps): if self.interrupt: continue timestep = t.expand(latents.shape[0]).to(latents.dtype) noise_pred = self.transformer( hidden_states=latents, timestep=timestep / 1000, guidance=guidance, pooled_projections=pooled_prompt_embeds, encoder_hidden_states=prompt_embeds, txt_ids=text_ids, img_ids=latent_image_ids, joint_attention_kwargs=self.joint_attention_kwargs, return_dict=False, )[0] latents_for_image = self._unpack_latents(latents, height, width, self.vae_scale_factor) latents_for_image = (latents_for_image / self.vae.config.scaling_factor) + self.vae.config.shift_factor image = self.vae.decode(latents_for_image, return_dict=False)[0] yield self.image_processor.postprocess(image, output_type=output_type)[0] latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] torch.cuda.empty_cache() latents = self._unpack_latents(latents, height, width, self.vae_scale_factor) latents = (latents / good_vae.config.scaling_factor) + good_vae.config.shift_factor image = good_vae.decode(latents, return_dict=False)[0] self.maybe_free_model_hooks() torch.cuda.empty_cache() yield self.image_processor.postprocess(image, output_type=output_type)[0] #--------------------------------------------------Model Initialization-----------------------------------------------------------------------------------------# dtype = torch.bfloat16 device = "cuda" if torch.cuda.is_available() else "cpu" base_model = "black-forest-labs/FLUX.1-dev" # TAEF1 is a very tiny autoencoder which uses the same "latent API" as FLUX.1's VAE. taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device) good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device) pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device) 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, ).to(device) MAX_SEED = 2**32-1 pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe) class calculateDuration: def __init__(self, activity_name=""): self.activity_name = activity_name def __enter__(self): self.start_time = time.time() return self def __exit__(self, exc_type, exc_value, traceback): self.end_time = time.time() self.elapsed_time = self.end_time - self.start_time if self.activity_name: print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds") else: print(f"Elapsed time: {self.elapsed_time:.6f} seconds") def update_selection(evt: gr.SelectData, width, height): selected_lora = loras[evt.index] new_placeholder = f"Type a prompt for {selected_lora['title']}" lora_repo = selected_lora["repo"] updated_text = f"### Selected: [{lora_repo}](https://huggingface.co./{lora_repo}) ✅" if "aspect" in selected_lora: if selected_lora["aspect"] == "portrait": width = 768 height = 1024 elif selected_lora["aspect"] == "landscape": width = 1024 height = 768 else: width = 1024 height = 1024 return ( gr.update(placeholder=new_placeholder), updated_text, evt.index, width, height, ) @spaces.GPU(duration=100) def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress): pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(seed) with calculateDuration("Generating image"): for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images( prompt=prompt_mash, num_inference_steps=steps, guidance_scale=cfg_scale, width=width, height=height, generator=generator, joint_attention_kwargs={"scale": lora_scale}, output_type="pil", good_vae=good_vae, ): yield img def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed): generator = torch.Generator(device="cuda").manual_seed(seed) pipe_i2i.to("cuda") image_input = load_image(image_input_path) final_image = pipe_i2i( prompt=prompt_mash, image=image_input, strength=image_strength, num_inference_steps=steps, guidance_scale=cfg_scale, width=width, height=height, generator=generator, joint_attention_kwargs={"scale": lora_scale}, output_type="pil", ).images[0] return final_image @spaces.GPU(duration=100) def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, use_enhancer, progress=gr.Progress(track_tqdm=True)): # Check if a LoRA is selected. if selected_index is None: return None, seed, gr.update(value="Please select a LoRA from the 'LoRA DLC's gallery above before generating images.", visible=False), "" # Return None for image and update prompt box selected_lora = loras[selected_index] lora_path = selected_lora["repo"] trigger_word = selected_lora["trigger_word"] # Prepare prompt by appending/prepending trigger word if available. if trigger_word: if "trigger_position" in selected_lora and selected_lora["trigger_position"] == "prepend": prompt_mash = f"{trigger_word} {prompt}" else: prompt_mash = f"{prompt} {trigger_word}" else: prompt_mash = prompt # If prompt enhancer is enabled, stream the enhanced prompt. enhanced_text = "" if use_enhancer: for enhanced_chunk in enhance_generate(prompt_mash): enhanced_text = enhanced_chunk # Yield intermediate output (no image yet, but update enhanced prompt textbox) yield None, seed, gr.update(visible=False), enhanced_text prompt_mash = enhanced_text # Use final enhanced prompt for generation # Else, leave prompt_mash as is. with calculateDuration("Unloading LoRA"): pipe.unload_lora_weights() pipe_i2i.unload_lora_weights() with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"): pipe_to_use = pipe_i2i if image_input is not None else pipe weight_name = selected_lora.get("weights", None) pipe_to_use.load_lora_weights( lora_path, weight_name=weight_name, low_cpu_mem_usage=True ) with calculateDuration("Randomizing seed"): if randomize_seed: seed = random.randint(0, MAX_SEED) if image_input is not None: final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed) yield final_image, seed, gr.update(visible=False), enhanced_text else: image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress) 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), enhanced_text yield final_image, seed, gr.update(value=progress_bar, visible=False), enhanced_text def get_huggingface_safetensors(link): split_link = link.split("/") if len(split_link) == 2: model_card = ModelCard.load(link) base_model = model_card.data.get("base_model") print(base_model) if (base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "black-forest-labs/FLUX.1-schnell"): raise Exception("Flux LoRA Not Found!") image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None) trigger_word = model_card.data.get("instance_prompt", "") image_url = f"https://huggingface.co./{link}/resolve/main/{image_path}" if image_path else None fs = HfFileSystem() try: list_of_files = fs.ls(link, detail=False) for file in list_of_files: if file.endswith(".safetensors"): safetensors_name = file.split("/")[-1] if not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp")): image_elements = file.split("/") image_url = f"https://huggingface.co./{link}/resolve/main/{image_elements[-1]}" except Exception as e: print(e) gr.Warning("You didn't include a link nor a valid Hugging Face repository with a *.safetensors LoRA") raise Exception("Invalid LoRA repository") return split_link[1], link, safetensors_name, trigger_word, image_url else: raise Exception("Invalid LoRA link format") def check_custom_model(link): if link.startswith("https://"): if link.startswith("https://huggingface.co.") or link.startswith("https://www.huggingface.co"): link_split = link.split("huggingface.co/") return get_huggingface_safetensors(link_split[1]) else: return get_huggingface_safetensors(link) def add_custom_lora(custom_lora): global loras if custom_lora: try: title, repo, path, trigger_word, image = check_custom_model(custom_lora) print(f"Loaded custom LoRA: {repo}") card = f'''
Loaded custom LoRA:

{title}

{"Using: " + trigger_word + " as the trigger word" if trigger_word else "No trigger word found. Include it in your prompt"}
''' existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None) if not existing_item_index: new_item = { "image": image, "title": title, "repo": repo, "weights": path, "trigger_word": trigger_word } print(new_item) existing_item_index = len(loras) loras.append(new_item) return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word except Exception as e: gr.Warning("Invalid LoRA: either you entered an invalid link or a non-FLUX LoRA") return gr.update(visible=True, value="Invalid LoRA"), gr.update(visible=False), gr.update(), "", None, "" else: return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, "" def remove_custom_lora(): return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, "" run_lora.zerogpu = True css = ''' #gen_btn { height: 100%; } #gen_column { align-self: stretch; } #title { text-align: center; } #title h1 { font-size: 3em; display:inline-flex; align-items:center; } #title img { width: 100px; margin-right: 0.5em; } #gallery .grid-wrap { height: 10vh; } #lora_list { background: var(--block-background-fill); padding: 0 1em .3em; font-size: 90%; } .card_internal { display: flex; height: 100px; margin-top: .5em; } .card_internal img { margin-right: 1em; } .styler { --form-gap-width: 0px !important; } #progress { height:30px; } #progress .generating { display:none; } .progress-container { width: 100%; height: 30px; background-color: #f0f0f0; border-radius: 15px; overflow: hidden; margin-bottom: 20px; } .progress-bar { height: 100%; background-color: #4f46e5; width: calc(var(--current) / var(--total) * 100%); transition: width 0.5s ease-in-out; } ''' with gr.Blocks(theme=gr.themes.Base(), css=css, delete_cache=(60, 60)) as app: title = gr.HTML( """

Flux LoRA Generation

""", elem_id="title", ) selected_index = gr.State(None) with gr.Row(): with gr.Column(scale=3): prompt = gr.Textbox(label="Prompt", lines=1, placeholder=":/ choose the LoRA and type the prompt ") with gr.Column(scale=1, elem_id="gen_column"): generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn") with gr.Row(): with gr.Column(): selected_info = gr.Markdown("") gallery = gr.Gallery( [(item["image"], item["title"]) for item in loras], label="LoRA DLC's", allow_preview=False, columns=3, elem_id="gallery", show_share_button=False ) with gr.Group(): custom_lora = gr.Textbox(label="Enter Custom LoRA", placeholder="prithivMLmods/Canopus-LoRA-Flux-Anime") gr.Markdown("[Check the list of FLUX LoRA's](https://huggingface.co./models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list") custom_lora_info = gr.HTML(visible=False) custom_lora_button = gr.Button("Remove custom LoRA", visible=False) with gr.Column(): progress_bar = gr.Markdown(elem_id="progress", visible=False) result = gr.Image(label="Generated Image") with gr.Row(): with gr.Accordion("Advanced Settings", open=False): with gr.Row(): input_image = gr.Image(label="Input image", type="filepath") image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75) with gr.Column(): with gr.Row(): cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5) steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28) with gr.Row(): width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024) height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024) with gr.Row(): randomize_seed = gr.Checkbox(True, label="Randomize seed") seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True) lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=3, step=0.01, value=0.95) with gr.Row(): use_enhancer = gr.Checkbox(value=False, label="Use Prompt Enhancer") show_enhanced_prompt = gr.Checkbox(value=False, label="Display Enhanced Prompt") enhanced_prompt_box = gr.Textbox(label="Enhanced Prompt", visible=False) # Add the change event so that the enhanced prompt box visibility toggles. show_enhanced_prompt.change(fn=lambda show: gr.update(visible=show), inputs=show_enhanced_prompt, outputs=enhanced_prompt_box) gallery.select( update_selection, inputs=[width, height], outputs=[prompt, selected_info, selected_index, width, height] ) custom_lora.input( add_custom_lora, inputs=[custom_lora], outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt] ) custom_lora_button.click( remove_custom_lora, outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora] ) gr.on( triggers=[generate_button.click, prompt.submit], fn=run_lora, inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, use_enhancer], outputs=[result, seed, progress_bar, enhanced_prompt_box] ) with gr.Row(): gr.HTML("
Credits: ruslanmv.com
") app.queue() app.launch(debug=True)