Upload 3 files

app.py

@@ -4,7 +4,8 @@ import json
 import logging
 import torch
 from PIL import Image
-from diffusers import DiffusionPipeline
+from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL
+from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
 from diffusers import FluxControlNetPipeline, FluxControlNetModel, FluxMultiControlNetModel
 from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
 import copy
@@ -21,16 +22,29 @@ from flux import (search_civitai_lora, select_civitai_lora, search_civitai_lora_
 from tagger.tagger import predict_tags_wd, compose_prompt_to_copy
 from tagger.fl2flux import predict_tags_fl2_flux
 
+# Load LoRAs from JSON file
+with open('loras.json', 'r') as f:
+    loras = json.load(f)
+
+dtype = torch.bfloat16
+#dtype = torch.float8_e4m3fn
+device = "cuda" if torch.cuda.is_available() else "cpu"
 # Initialize the base model
 base_model = models[0]
 controlnet_model_union_repo = 'InstantX/FLUX.1-dev-Controlnet-Union'
 #controlnet_model_union_repo = 'InstantX/FLUX.1-dev-Controlnet-Union-alpha'
+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=torch.bfloat16)
 controlnet_union = None
 controlnet = None
 last_model = models[0]
 last_cn_on = False
 
+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)
+
 # https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union
 # https://huggingface.co/spaces/jiuface/FLUX.1-dev-Controlnet-Union
 def change_base_model(repo_id: str, cn_on: bool):
@@ -39,8 +53,6 @@ def change_base_model(repo_id: str, cn_on: bool):
     global controlnet
     global last_model
     global last_cn_on
-    dtype = torch.bfloat16
-    #dtype = torch.float8_e4m3fn
     try:
         if (repo_id == last_model and cn_on is last_cn_on) or not is_repo_name(repo_id) or not is_repo_exists(repo_id): return gr.update(visible=True)
         if cn_on:
@@ -50,6 +62,7 @@ def change_base_model(repo_id: str, cn_on: bool):
             controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union_repo, torch_dtype=dtype)
             controlnet = FluxMultiControlNetModel([controlnet_union])
             pipe = FluxControlNetPipeline.from_pretrained(repo_id, controlnet=controlnet, torch_dtype=dtype)
+            pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
             last_model = repo_id
             last_cn_on = cn_on
             #progress(1, desc=f"Model loaded: {repo_id} / ControlNet Loaded: {controlnet_model_union_repo}")
@@ -59,6 +72,7 @@ def change_base_model(repo_id: str, cn_on: bool):
             print(f"Loading model: {repo_id}")
             clear_cache()
             pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=dtype)
+            pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
             last_model = repo_id
             last_cn_on = cn_on
             #progress(1, desc=f"Model loaded: {repo_id}")
@@ -70,12 +84,6 @@ def change_base_model(repo_id: str, cn_on: bool):
 
 change_base_model.zerogpu = True
 
-# Load LoRAs from JSON file
-with open('loras.json', 'r') as f:
-    loras = json.load(f)
-
-MAX_SEED = 2**32-1
-
 class calculateDuration:
     def __init__(self, activity_name=""):
         self.activity_name = activity_name
@@ -126,7 +134,7 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scal
             modes, images, scales = get_control_params()
             if not cn_on or len(modes) == 0:
                 progress(0, desc="Start Inference.")
-                image = pipe(
+                for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
                     prompt=prompt_mash,
                     num_inference_steps=steps,
                     guidance_scale=cfg_scale,
@@ -134,12 +142,15 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scal
                     height=height,
                     generator=generator,
                     joint_attention_kwargs={"scale": lora_scale},
-                ).images[0]
+                    output_type="pil",
+                    good_vae=good_vae,
+                ):
+                    yield img
             else:
                 progress(0, desc="Start Inference with ControlNet.")
                 if controlnet is not None: controlnet.to("cuda")
                 if controlnet_union is not None: controlnet_union.to("cuda")
-                image = pipe(
+                for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
                     prompt=prompt_mash,
                     control_image=images,
                     control_mode=modes,
@@ -150,7 +161,10 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scal
                     controlnet_conditioning_scale=scales,
                     generator=generator,
                     joint_attention_kwargs={"scale": lora_scale},
-                ).images[0]
+                    output_type="pil",
+                    good_vae=good_vae,
+                ):
+                    yield img
     except Exception as e:
         print(e)
         raise gr.Error(f"Inference Error: {e}")
@@ -197,8 +211,12 @@ def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, wid
             seed = random.randint(0, MAX_SEED)
     
     progress(0, desc="Running Inference.")
-
-    image = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress)
+    image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress)
+    # Consume the generator to get the final image
+    final_image = None
+    for image in image_generator:
+        final_image = image
+        yield image, seed  # Yield intermediate images and seed
     if is_valid_lora(lora_json):
         pipe.unfuse_lora()
         pipe.unload_lora_weights()
@@ -207,7 +225,7 @@ def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, wid
     if controlnet is not None: controlnet.to("cpu")
     if controlnet_union is not None: controlnet_union.to("cpu")
     clear_cache()
-    return image, seed  
+    return final_image, seed  # Return the final image and seed
 
 def get_huggingface_safetensors(link):
   split_link = link.split("/")

live_preview_helpers.py

@@ -0,0 +1,166 @@
+import torch
+import numpy as np
+from diffusers import FluxPipeline, AutoencoderTiny, FlowMatchEulerDiscreteScheduler
+from typing import Any, Dict, List, Optional, Union
+
+# Helper functions
+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 function
+@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
+
+    # 1. Check inputs
+    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
+
+    # 2. Define call parameters
+    batch_size = 1 if isinstance(prompt, str) else len(prompt)
+    device = self._execution_device
+
+    # 3. Encode prompt
+    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,
+    )
+    # 4. Prepare latent variables
+    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,
+    )
+    # 5. Prepare timesteps
+    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)
+
+    # Handle guidance
+    guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32).expand(latents.shape[0]) if self.transformer.config.guidance_embeds else None
+
+    # 6. Denoising loop
+    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 = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+        # Yield intermediate result
+        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]
+        torch.cuda.empty_cache()
+
+    # Final image using good_vae
+    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]

loras.json

@@ -1,150 +1,183 @@
-[
-  {
-    "image": "https://cdn-uploads.huggingface.co/production/uploads/641498f7479c98a0b36f9e3e/2hT_tW_DCcF60lNyCBE_8.png",
-    "title": "Abe Shinzo Flux",
-    "repo": "AbeShinzo0708/AbeShinzo_flux_lora_test",
-    "weights": "AbeShinzo.safetensors",
-    "trigger_word": ", Shinzo Abe"
-  },
-  {
-    "image": "https://huggingface.co/p1atdev/flux.1-schnell-pvc-style-lora/resolve/main/images/flux_lora_00221_.png",
-    "title": "FLUX.1 schnell PVC style",
-    "repo": "p1atdev/flux.1-schnell-pvc-style-lora",
-    "weights": "pvc-shnell-7250+7500.safetensors",
-    "trigger_word": ", pvc figure"
-  },
-  {
-    "image": "https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/sCvp6zDbBTXBEHh3ZSav6.png",
-    "title": "Flux Pastel Anime",
-    "repo": "Raelina/Flux-Pastel-Anime",
-    "weights": "lora_pastel_anime_flux.safetensors",
-    "trigger_word": ""
-  },
-  {
-    "image": "https://huggingface.co/wavymulder/OverlordStyleFLUX/resolve/main/imgs/ComfyUI_00725_.png",
-    "title": "Overlord Style FLUX",
-    "repo": "wavymulder/OverlordStyleFLUX",
-    "weights": "ovld_style_overlord_wavymulder.safetensors",
-    "trigger_word": ", ovld style anime"
-  },
-  {
-    "image": "https://huggingface.co/multimodalart/flux-tarot-v1/resolve/main/images/e5f2761e5d474e6ba492d20dca0fa26f_e78f1524074b42b6ac49643ffad50ac6.png",
-    "title": "Tarot v1",
-    "repo": "multimodalart/flux-tarot-v1",
-    "trigger_word": "in the style of TOK a trtcrd, tarot style",
-    "aspect": "portrait"
-  },
-  {
-    "image": "https://huggingface.co/alvdansen/frosting_lane_flux/resolve/main/images/content%20-%202024-08-11T005936.346.jpeg",
-    "title": "Frosting Lane Flux",
-    "repo": "alvdansen/frosting_lane_flux",
-    "trigger_word": ""
-  },
-  {
-    "image": "https://huggingface.co/AIWarper/RubberCore1920sCartoonStyle/resolve/main/images/Rub_00006_.png",
-    "title": "1920s cartoon",
-    "repo": "AIWarper/RubberCore1920sCartoonStyle",
-    "trigger_word": "RU883R style",
-    "trigger_position": "prepend"
-  },
-  {
-    "image": "https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/picture-6-rev1.png?raw=true",
-    "title": "flux-Realism",
-    "repo": "XLabs-AI/flux-RealismLora",
-    "trigger_word": ""
-  },
-  {
-    "image": "https://huggingface.co/nerijs/animation2k-flux/resolve/main/images/Q8-oVxNnXvZ9HNrgbNpGw_02762aaaba3b47859ee5fe9403a371e3.png",
-    "title": "animation2k",
-    "repo": "nerijs/animation2k-flux",
-    "trigger_word": ""
-  },
-  {
-    "image":"https://huggingface.co/alvdansen/softserve_anime/resolve/main/images/ComfyUI_00062_.png",
-    "title":"SoftServe Anime", 
-    "repo": "alvdansen/softserve_anime",
-    "trigger_word": ""
-  },
-  {
-    "image": "https://huggingface.co/veryVANYA/ps1-style-flux/resolve/main/24439220.jpeg",
-    "title": "PS1 style",
-    "repo": "veryVANYA/ps1-style-flux",
-    "trigger_word": "ps1 game screenshot"
-  },
-  {
-    "image": "https://huggingface.co/alvdansen/flux-koda/resolve/main/images/ComfyUI_00566_%20(2).png",
-    "title": "flux koda",
-    "repo": "alvdansen/flux-koda",
-    "trigger_word": "flmft style"
-  },
-  {
-    "image": "https://pbs.twimg.com/media/GU7NsZPa8AA4Ddl?format=jpg&name=4096x4096",
-    "title": "Half Illustration",
-    "repo": "davisbro/half_illustration",
-    "trigger_word": "in the style of TOK"
-  },
-  {
-    "image":"https://pbs.twimg.com/media/GVRiSH7WgAAnI4P?format=jpg&name=medium",
-    "title":"wrong", 
-    "repo": "fofr/flux-wrong",
-    "trigger_word": "WRNG"
-  },
-  {
-    "image":"https://huggingface.co/linoyts/yarn_art_Flux_LoRA/resolve/main/yarn_art_2.png",
-    "title":"Yarn Art", 
-    "repo": "linoyts/yarn_art_Flux_LoRA",
-    "trigger_word": ", yarn art style"
-  },
-  {
-    "image": "https://huggingface.co/Norod78/Flux_1_Dev_LoRA_Paper-Cutout-Style/resolve/main/08a19840b6214b76b0607b2f9d5a7e28_63159b9d98124c008efb1d36446a615c.png",
-    "title": "Paper Cutout",
-    "repo": "Norod78/Flux_1_Dev_LoRA_Paper-Cutout-Style",
-    "trigger_word": ", Paper Cutout Style"
-  },
-  {
-    "image": "https://huggingface.co/SebastianBodza/flux_lora_aquarel_watercolor/resolve/main/images/ascend.webp",
-    "title": "Aquarell Watercolor",
-    "repo": "SebastianBodza/Flux_Aquarell_Watercolor_v2",
-    "trigger_word": "in a watercolor style, AQUACOLTOK. White background."
-  },
-  {
-    "image": "https://huggingface.co/dataautogpt3/FLUX-SyntheticAnime/resolve/main/assets/angel.png",
-    "title": "SyntheticAnime",
-    "repo": "dataautogpt3/FLUX-SyntheticAnime",
-    "trigger_word": "1980s anime screengrab, VHS quality"
-  },
-  {
-    "image": "https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/result_14.png?raw=true",
-    "title": "flux-anime",
-    "repo": "XLabs-AI/flux-lora-collection",
-    "weights": "anime_lora.safetensors",
-    "trigger_word": ", anime"
-  },
-  {
-    "image": "https://huggingface.co/kudzueye/Boreal/resolve/main/images/ComfyUI_00845_.png",
-    "title": "Boreal",
-    "repo": "kudzueye/Boreal",
-    "weights": "boreal-flux-dev-lora-v04_1000_steps.safetensors",
-    "trigger_word": "phone photo"
-  },
-  {
-    "image": "https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/result_18.png?raw=true",
-    "title": "flux-disney",
-    "repo": "XLabs-AI/flux-lora-collection",
-    "weights": "disney_lora.safetensors",
-    "trigger_word": ", disney style"
-  },
-  {
-    "image": "https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/result_23.png?raw=true",
-    "title": "flux-art",
-    "repo": "XLabs-AI/flux-lora-collection",
-    "weights": "art_lora.safetensors",
-    "trigger_word": ", art"
-  },
-  {
-    "image": "https://huggingface.co/martintomov/retrofuturism-flux/resolve/main/images/2e40deba-858e-454f-ae1c-d1ba2adb6a65.jpeg",
-    "title": "Retrofuturism Flux",
-    "repo": "martintomov/retrofuturism-flux",
-    "trigger_word": ", retrofuturism"
-  }
+[
+  {
+    "image": "https://cdn-uploads.huggingface.co/production/uploads/641498f7479c98a0b36f9e3e/2hT_tW_DCcF60lNyCBE_8.png",
+    "title": "Abe Shinzo Flux",
+    "repo": "AbeShinzo0708/AbeShinzo_flux_lora_test",
+    "weights": "AbeShinzo.safetensors",
+    "trigger_word": ", Shinzo Abe"
+  },
+  {
+    "image": "https://huggingface.co/p1atdev/flux.1-schnell-pvc-style-lora/resolve/main/images/flux_lora_00221_.png",
+    "title": "FLUX.1 schnell PVC style",
+    "repo": "p1atdev/flux.1-schnell-pvc-style-lora",
+    "weights": "pvc-shnell-7250+7500.safetensors",
+    "trigger_word": ", pvc figure"
+  },
+  {
+    "image": "https://cdn-uploads.huggingface.co/production/uploads/64b24543eec33e27dc9a6eca/sCvp6zDbBTXBEHh3ZSav6.png",
+    "title": "Flux Pastel Anime",
+    "repo": "Raelina/Flux-Pastel-Anime",
+    "weights": "lora_pastel_anime_flux.safetensors",
+    "trigger_word": ""
+  },
+  {
+    "image": "https://huggingface.co/wavymulder/OverlordStyleFLUX/resolve/main/imgs/ComfyUI_00725_.png",
+    "title": "Overlord Style FLUX",
+    "repo": "wavymulder/OverlordStyleFLUX",
+    "weights": "ovld_style_overlord_wavymulder.safetensors",
+    "trigger_word": ", ovld style anime"
+  },
+  {
+    "image": "https://huggingface.co/multimodalart/flux-tarot-v1/resolve/main/images/e5f2761e5d474e6ba492d20dca0fa26f_e78f1524074b42b6ac49643ffad50ac6.png",
+    "title": "Tarot v1",
+    "repo": "multimodalart/flux-tarot-v1",
+    "trigger_word": "in the style of TOK a trtcrd, tarot style",
+    "aspect": "portrait"
+  },
+  {
+    "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"
+  },
+  {
+    "image": "https://huggingface.co/AIWarper/RubberCore1920sCartoonStyle/resolve/main/images/Rub_00006_.png",
+    "title": "1920s cartoon",
+    "repo": "AIWarper/RubberCore1920sCartoonStyle",
+    "trigger_word": "RU883R style",
+    "trigger_position": "prepend"
+  },
+  {
+    "image": "https://huggingface.co/mgwr/Cine-Aesthetic/resolve/main/images/00030-1333633802.png",
+    "title": "Cine Aesthetic",
+    "repo": "mgwr/Cine-Aesthetic",
+    "trigger_word": "mgwr/cine",
+    "trigger_position": "prepend"
+  },
+  {
+    "image": "https://huggingface.co/Shakker-Labs/FLUX.1-dev-LoRA-blended-realistic-illustration/resolve/main/images/example3.png",
+    "title": "Blended Realistic Illustration",
+    "repo": "Shakker-Labs/FLUX.1-dev-LoRA-blended-realistic-illustration",
+    "trigger_word": "artistic style blends reality and illustration elements"
+  },
+  {
+    "image": "https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/picture-6-rev1.png?raw=true",
+    "title": "flux-Realism",
+    "repo": "XLabs-AI/flux-RealismLora",
+    "trigger_word": ""
+  },
+  {
+    "image": "https://huggingface.co/multimodalart/vintage-ads-flux/resolve/main/samples/j_XNU6Oe0mgttyvf9uPb3_dc244dd3d6c246b4aff8351444868d66.png",
+    "title": "Vintage Ads",
+    "repo":"multimodalart/vintage-ads-flux",
+    "trigger_word": "a vintage ad of",
+    "trigger_position": "prepend"
+  },
+  {
+    "image": "https://huggingface.co/nerijs/animation2k-flux/resolve/main/images/Q8-oVxNnXvZ9HNrgbNpGw_02762aaaba3b47859ee5fe9403a371e3.png",
+    "title": "animation2k",
+    "repo": "nerijs/animation2k-flux",
+    "trigger_word": ""
+  },
+  {
+    "image":"https://huggingface.co/alvdansen/softserve_anime/resolve/main/images/ComfyUI_00062_.png",
+    "title":"SoftServe Anime", 
+    "repo": "alvdansen/softserve_anime",
+    "trigger_word": ""
+  },
+  {
+    "image": "https://huggingface.co/veryVANYA/ps1-style-flux/resolve/main/24439220.jpeg",
+    "title": "PS1 style",
+    "repo": "veryVANYA/ps1-style-flux",
+    "trigger_word": "ps1 game screenshot"
+  },
+  {
+    "image": "https://huggingface.co/alvdansen/flux-koda/resolve/main/images/ComfyUI_00566_%20(2).png",
+    "title": "flux koda",
+    "repo": "alvdansen/flux-koda",
+    "trigger_word": "flmft style"
+  },
+  {
+    "image": "https://huggingface.co/alvdansen/frosting_lane_flux/resolve/main/images/content%20-%202024-08-11T005936.346.jpeg",
+    "title": "Frosting Lane Flux",
+    "repo": "alvdansen/frosting_lane_flux",
+    "trigger_word": ""
+  },
+  {
+    "image": "https://pbs.twimg.com/media/GU7NsZPa8AA4Ddl?format=jpg&name=4096x4096",
+    "title": "Half Illustration",
+    "repo": "davisbro/half_illustration",
+    "trigger_word": "in the style of TOK"
+  },
+  {
+    "image":"https://pbs.twimg.com/media/GVRiSH7WgAAnI4P?format=jpg&name=medium",
+    "title":"wrong", 
+    "repo": "fofr/flux-wrong",
+    "trigger_word": "WRNG"
+  },
+  {
+    "image":"https://huggingface.co/linoyts/yarn_art_Flux_LoRA/resolve/main/yarn_art_2.png",
+    "title":"Yarn Art", 
+    "repo": "linoyts/yarn_art_Flux_LoRA",
+    "trigger_word": ", yarn art style"
+  },
+  {
+    "image": "https://huggingface.co/Norod78/Flux_1_Dev_LoRA_Paper-Cutout-Style/resolve/main/08a19840b6214b76b0607b2f9d5a7e28_63159b9d98124c008efb1d36446a615c.png",
+    "title": "Paper Cutout",
+    "repo": "Norod78/Flux_1_Dev_LoRA_Paper-Cutout-Style",
+    "trigger_word": ", Paper Cutout Style"
+  },
+  {
+    "image": "https://huggingface.co/SebastianBodza/flux_lora_aquarel_watercolor/resolve/main/images/ascend.webp",
+    "title": "Aquarell Watercolor",
+    "repo": "SebastianBodza/Flux_Aquarell_Watercolor_v2",
+    "trigger_word": "in a watercolor style, AQUACOLTOK. White background."
+  },
+  {
+    "image": "https://huggingface.co/dataautogpt3/FLUX-SyntheticAnime/resolve/main/assets/angel.png",
+    "title": "SyntheticAnime",
+    "repo": "dataautogpt3/FLUX-SyntheticAnime",
+    "trigger_word": "1980s anime screengrab, VHS quality"
+  },
+  {
+    "image": "https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/result_14.png?raw=true",
+    "title": "flux-anime",
+    "repo": "XLabs-AI/flux-lora-collection",
+    "weights": "anime_lora.safetensors",
+    "trigger_word": ", anime"
+  },
+  {
+    "image": "https://replicate.delivery/yhqm/QD8Ioy5NExqSCtBS8hG04XIRQZFaC9pxJemINT1bibyjZfSTA/out-0.webp",
+    "title": "80s Cyberpunk",
+    "repo": "fofr/flux-80s-cyberpunk",
+    "trigger_word": "style of 80s cyberpunk",
+    "trigger_position": "prepend"
+  },
+  {
+    "image": "https://huggingface.co/kudzueye/Boreal/resolve/main/images/ComfyUI_00845_.png",
+    "title": "Boreal",
+    "repo": "kudzueye/Boreal",
+    "weights": "boreal-flux-dev-lora-v04_1000_steps.safetensors",
+    "trigger_word": "phone photo"
+  },
+  {
+    "image": "https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/result_18.png?raw=true",
+    "title": "flux-disney",
+    "repo": "XLabs-AI/flux-lora-collection",
+    "weights": "disney_lora.safetensors",
+    "trigger_word": ", disney style"
+  },
+  {
+    "image": "https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/result_23.png?raw=true",
+    "title": "flux-art",
+    "repo": "XLabs-AI/flux-lora-collection",
+    "weights": "art_lora.safetensors",
+    "trigger_word": ", art"
+  },
+  {
+    "image": "https://huggingface.co/martintomov/retrofuturism-flux/resolve/main/images/2e40deba-858e-454f-ae1c-d1ba2adb6a65.jpeg",
+    "title": "Retrofuturism Flux",
+    "repo": "martintomov/retrofuturism-flux",
+    "trigger_word": ", retrofuturism"
+  }
 ]