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| 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) | |
| import spaces | |
| 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 | |
| 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] | |
| #-----------------------------------------------------------------------------------LoRA's--------------------------------------------------------------------------# | |
| loras = [ | |
| #1 | |
| { | |
| "image": "https://huggingface.co./prithivMLmods/Canopus-LoRA-Flux-FaceRealism/resolve/main/images/11.png", | |
| "title": "Flux Face Realism", | |
| "repo": "prithivMLmods/Canopus-LoRA-Flux-FaceRealism", | |
| "trigger_word": "Realism" | |
| }, | |
| #2 | |
| { | |
| "image": "https://huggingface.co./alvdansen/softserve_anime/resolve/main/images/ComfyUI_00134_.png", | |
| "title": "Softserve Anime", | |
| "repo": "alvdansen/softserve_anime", | |
| "trigger_word": "sftsrv style illustration" | |
| }, | |
| #3 | |
| { | |
| "image": "https://huggingface.co./prithivMLmods/Canopus-LoRA-Flux-Anime/resolve/main/assets/4.png", | |
| "title": "Flux Anime", | |
| "repo": "prithivMLmods/Canopus-LoRA-Flux-Anime", | |
| "trigger_word": "Anime" | |
| }, | |
| #4 | |
| { | |
| "image": "https://huggingface.co./Shakker-Labs/FLUX.1-dev-LoRA-One-Click-Creative-Template/resolve/main/images/f2cc649985648e57b9b9b14ca7a8744ac8e50d75b3a334ed4df0f368.jpg", | |
| "title": "Creative Template", | |
| "repo": "Shakker-Labs/FLUX.1-dev-LoRA-One-Click-Creative-Template", | |
| "trigger_word": "The background is 4 real photos, and in the middle is a cartoon picture summarizing the real photos." | |
| }, | |
| #5 | |
| { | |
| "image": "https://huggingface.co./prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0/resolve/main/images/3.png", | |
| "title": "Ultra Realism", | |
| "repo": "prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0", | |
| "trigger_word": "Ultra realistic" | |
| }, | |
| #6 | |
| { | |
| "image": "https://huggingface.co./gokaygokay/Flux-Game-Assets-LoRA-v2/resolve/main/images/example_y2bqpuphc.png", | |
| "title": "Game Assets", | |
| "repo": "gokaygokay/Flux-Game-Assets-LoRA-v2", | |
| "trigger_word": "wbgmsst, white background" | |
| }, | |
| #7 | |
| { | |
| "image": "https://huggingface.co./alvdansen/softpasty-flux-dev/resolve/main/images/ComfyUI_00814_%20(2).png", | |
| "title": "Softpasty", | |
| "repo": "alvdansen/softpasty-flux-dev", | |
| "trigger_word": "araminta_illus illustration style" | |
| }, | |
| #8 | |
| { | |
| "image": "https://huggingface.co./Shakker-Labs/FLUX.1-dev-LoRA-add-details/resolve/main/images/0.png", | |
| "title": "Details Add", | |
| "repo": "Shakker-Labs/FLUX.1-dev-LoRA-add-details", | |
| "trigger_word": "" | |
| }, | |
| #9 | |
| { | |
| "image": "https://huggingface.co./alvdansen/frosting_lane_flux/resolve/main/images/content%20-%202024-08-11T010011.238.jpeg", | |
| "title": "Frosting Lane", | |
| "repo": "alvdansen/frosting_lane_flux", | |
| "trigger_word": "frstingln illustration" | |
| }, | |
| #10 | |
| { | |
| "image": "https://huggingface.co./aleksa-codes/flux-ghibsky-illustration/resolve/main/images/example5.jpg", | |
| "title": "Ghibsky Illustration", | |
| "repo": "aleksa-codes/flux-ghibsky-illustration", | |
| "trigger_word": "GHIBSKY style painting" | |
| }, | |
| #11 | |
| { | |
| "image": "https://huggingface.co./Shakker-Labs/FLUX.1-dev-LoRA-Dark-Fantasy/resolve/main/images/c2215bd73da9f14fcd63cc93350e66e2901bdafa6fb8abaaa2c32a1b.jpg", | |
| "title": "Dark Fantasy", | |
| "repo": "Shakker-Labs/FLUX.1-dev-LoRA-Dark-Fantasy", | |
| "trigger_word": "" | |
| }, | |
| #12 | |
| { | |
| "image": "https://huggingface.co./Norod78/Flux_1_Dev_LoRA_Paper-Cutout-Style/resolve/main/d13591878d5043f3989dd6eb1c25b710_233c18effb4b491cb467ca31c97e90b5.png", | |
| "title": "Paper Cutout", | |
| "repo": "Norod78/Flux_1_Dev_LoRA_Paper-Cutout-Style", | |
| "trigger_word": "Paper Cutout Style" | |
| }, | |
| #13 | |
| { | |
| "image": "https://huggingface.co./alvdansen/mooniverse/resolve/main/images/out-0%20(17).webp", | |
| "title": "Mooniverse", | |
| "repo": "alvdansen/mooniverse", | |
| "trigger_word": "surreal style" | |
| }, | |
| #14 | |
| { | |
| "image": "https://huggingface.co./alvdansen/pola-photo-flux/resolve/main/images/out-0%20-%202024-09-22T130819.351.webp", | |
| "title": "Pola Photo", | |
| "repo": "alvdansen/pola-photo-flux", | |
| "trigger_word": "polaroid style" | |
| }, | |
| #15 | |
| { | |
| "image": "https://huggingface.co./multimodalart/flux-tarot-v1/resolve/main/images/7e180627edd846e899b6cd307339140d_5b2a09f0842c476b83b6bd2cb9143a52.png", | |
| "title": "Flux Tarot", | |
| "repo": "multimodalart/flux-tarot-v1", | |
| "trigger_word": "in the style of TOK a trtcrd tarot style" | |
| }, | |
| #16 | |
| { | |
| "image": "https://huggingface.co./prithivMLmods/Flux-Dev-Real-Anime-LoRA/resolve/main/images/111.png", | |
| "title": "Real Anime", | |
| "repo": "prithivMLmods/Flux-Dev-Real-Anime-LoRA", | |
| "trigger_word": "Real Anime" | |
| }, | |
| #17 | |
| { | |
| "image": "https://huggingface.co./diabolic6045/Flux_Sticker_Lora/resolve/main/images/example_s3pxsewcb.png", | |
| "title": "Stickers", | |
| "repo": "diabolic6045/Flux_Sticker_Lora", | |
| "trigger_word": "5t1cker 5ty1e" | |
| }, | |
| #18 | |
| { | |
| "image": "https://huggingface.co./VideoAditor/Flux-Lora-Realism/resolve/main/images/feel-the-difference-between-using-flux-with-lora-from-xlab-v0-j0ehybmvxehd1.png", | |
| "title": "Realism", | |
| "repo": "XLabs-AI/flux-RealismLora", | |
| "trigger_word": "" | |
| }, | |
| #19 | |
| { | |
| "image": "https://huggingface.co./alvdansen/flux-koda/resolve/main/images/ComfyUI_00583_%20(1).png", | |
| "title": "Koda", | |
| "repo": "alvdansen/flux-koda", | |
| "trigger_word": "flmft style" | |
| }, | |
| #20 | |
| { | |
| "image": "https://huggingface.co./mgwr/Cine-Aesthetic/resolve/main/images/00019-1333633802.png", | |
| "title": "Cine Aesthetic", | |
| "repo": "mgwr/Cine-Aesthetic", | |
| "trigger_word": "mgwr/cine" | |
| }, | |
| #21 | |
| { | |
| "image": "https://huggingface.co./SebastianBodza/flux_cute3D/resolve/main/images/astronaut.webp", | |
| "title": "Cute 3D", | |
| "repo": "SebastianBodza/flux_cute3D", | |
| "trigger_word": "NEOCUTE3D" | |
| }, | |
| #22 | |
| { | |
| "image": "https://huggingface.co./bingbangboom/flux_dreamscape/resolve/main/images/3.jpg", | |
| "title": "Dreamscape", | |
| "repo": "bingbangboom/flux_dreamscape", | |
| "trigger_word": "in the style of BSstyle004" | |
| }, | |
| #23 | |
| { | |
| "image": "https://huggingface.co./prithivMLmods/Canopus-Cute-Kawaii-Flux-LoRA/resolve/main/images/11.png", | |
| "title": "Cute Kawaii", | |
| "repo": "prithivMLmods/Canopus-Cute-Kawaii-Flux-LoRA", | |
| "trigger_word": "cute-kawaii" | |
| }, | |
| #24 | |
| { | |
| "image": "https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/_jyra-jKP_prXhzxYkg1O.png", | |
| "title": "Pastel Anime", | |
| "repo": "Raelina/Flux-Pastel-Anime", | |
| "trigger_word": "Anime" | |
| }, | |
| #25 | |
| { | |
| "image": "https://huggingface.co./Shakker-Labs/FLUX.1-dev-LoRA-Vector-Journey/resolve/main/images/f7a66b51c89896854f31bef743dc30f33c6ea3c0ed8f9ff04d24b702.jpg", | |
| "title": "Vector", | |
| "repo": "Shakker-Labs/FLUX.1-dev-LoRA-Vector-Journey", | |
| "trigger_word": "artistic style blends reality and illustration elements" | |
| }, | |
| #26 | |
| { | |
| "image": "https://huggingface.co./bingbangboom/flux-miniature-worlds/resolve/main/images/2.jpg", | |
| "title": "Miniature", | |
| "repo": "bingbangboom/flux-miniature-worlds", | |
| "trigger_word": "Image in the style of MNTRWRLDS" | |
| }, | |
| #27 | |
| { | |
| "image": "https://huggingface.co./glif-loradex-trainer/bingbangboom_flux_surf/resolve/main/samples/1729012111574__000002000_0.jpg", | |
| "title": "Surf Bingbangboom", | |
| "repo": "glif-loradex-trainer/bingbangboom_flux_surf", | |
| "trigger_word": "SRFNGV01" | |
| }, | |
| #28 | |
| { | |
| "image": "https://huggingface.co./prithivMLmods/Canopus-Snoopy-Charlie-Brown-Flux-LoRA/resolve/main/000.png", | |
| "title": "Snoopy Charlie", | |
| "repo": "prithivMLmods/Canopus-Snoopy-Charlie-Brown-Flux-LoRA", | |
| "trigger_word": "Snoopy Charlie Brown" | |
| }, | |
| #29 | |
| { | |
| "image": "https://huggingface.co./alvdansen/sonny-anime-fixed/resolve/main/images/uqAuIMqA6Z7mvPkHg4qJE_f4c3cbe64e0349e7b946d02adeacdca3.png", | |
| "title": "Fixed Sonny", | |
| "repo": "alvdansen/sonny-anime-fixed", | |
| "trigger_word": "nm22 style" | |
| }, | |
| #30 | |
| { | |
| "image": "https://huggingface.co./davisbro/flux-multi-angle/resolve/main/multi-angle-examples/3.png", | |
| "title": "Multi Angle", | |
| "repo": "davisbro/flux-multi-angle", | |
| "trigger_word": "in the style of TOK" | |
| } | |
| #add--new LoRA Below ↓ - Before that Use(,) | |
| ] | |
| #--------------------------------------------------Model Initialization-----------------------------------------------------------------------------------------# | |
| dtype = torch.bfloat16 | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| base_model = "black-forest-labs/FLUX.1-dev" | |
| #TAEF1 is very tiny autoencoder which uses the same "latent API" as FLUX.1's VAE. FLUX.1 is useful for real-time previewing of the FLUX.1 generation process.# | |
| 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 | |
| ) | |
| 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, | |
| ) | |
| 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"): | |
| # Generate 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 | |
| def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)): | |
| if selected_index is None: | |
| raise gr.Error("You must select a LoRA before proceeding.") | |
| selected_lora = loras[selected_index] | |
| lora_path = selected_lora["repo"] | |
| trigger_word = selected_lora["trigger_word"] | |
| if(trigger_word): | |
| if "trigger_position" in selected_lora: | |
| if selected_lora["trigger_position"] == "prepend": | |
| prompt_mash = f"{trigger_word} {prompt}" | |
| else: | |
| prompt_mash = f"{prompt} {trigger_word}" | |
| else: | |
| prompt_mash = f"{trigger_word} {prompt}" | |
| else: | |
| prompt_mash = prompt | |
| with calculateDuration("Unloading LoRA"): | |
| pipe.unload_lora_weights() | |
| pipe_i2i.unload_lora_weights() | |
| #LoRA weights flow | |
| 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) | |
| 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'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>' | |
| yield image, seed, gr.update(value=progress_bar, visible=True) | |
| yield final_image, seed, gr.update(value=progress_bar, visible=False) | |
| 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!") | |
| # Only allow "black-forest-labs/FLUX.1-dev" | |
| #if base_model != "black-forest-labs/FLUX.1-dev": | |
| #raise Exception("Only FLUX.1-dev is supported, other LoRA models are not allowed!") | |
| image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None) | |
| trigger_word = model_card.data.get("instance_prompt", "") | |
| image_url = f"https://huggingface.co./{link}/resolve/main/{image_path}" if image_path else None | |
| fs = HfFileSystem() | |
| try: | |
| list_of_files = fs.ls(link, detail=False) | |
| for file in list_of_files: | |
| if(file.endswith(".safetensors")): | |
| safetensors_name = file.split("/")[-1] | |
| if (not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))): | |
| image_elements = file.split("/") | |
| image_url = f"https://huggingface.co./{link}/resolve/main/{image_elements[-1]}" | |
| except Exception as e: | |
| print(e) | |
| gr.Warning(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA") | |
| raise Exception(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA") | |
| return split_link[1], link, safetensors_name, trigger_word, image_url | |
| def check_custom_model(link): | |
| if(link.startswith("https://")): | |
| if(link.startswith("https://huggingface.co.") or link.startswith("https://www.huggingface.co")): | |
| link_split = link.split("huggingface.co/") | |
| return get_huggingface_safetensors(link_split[1]) | |
| else: | |
| return get_huggingface_safetensors(link) | |
| def add_custom_lora(custom_lora): | |
| global loras | |
| if(custom_lora): | |
| try: | |
| title, repo, path, trigger_word, image = check_custom_model(custom_lora) | |
| print(f"Loaded custom LoRA: {repo}") | |
| card = f''' | |
| <div class="custom_lora_card"> | |
| <span>Loaded custom LoRA:</span> | |
| <div class="card_internal"> | |
| <img src="{image}" /> | |
| <div> | |
| <h3>{title}</h3> | |
| <small>{"Using: <code><b>"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small> | |
| </div> | |
| </div> | |
| </div> | |
| ''' | |
| existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None) | |
| if(not existing_item_index): | |
| new_item = { | |
| "image": image, | |
| "title": title, | |
| "repo": repo, | |
| "weights": path, | |
| "trigger_word": trigger_word | |
| } | |
| print(new_item) | |
| existing_item_index = len(loras) | |
| loras.append(new_item) | |
| return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word | |
| except Exception as e: | |
| gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-FLUX LoRA") | |
| return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-FLUX LoRA"), gr.update(visible=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="prithivMLmods/Minecraft-Theme", css=css, delete_cache=(60, 3600)) as app: | |
| title = gr.HTML( | |
| """<h1>FLUX LoRA DLC🥳</h1>""", | |
| 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) | |
| 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], | |
| outputs=[result, seed, progress_bar] | |
| ) | |
| app.queue() | |
| app.launch() |