Update app.py

app.py

@@ -1,57 +1,69 @@
-##############################
-# ===== Standard Imports =====
-##############################
 import os
-import sys
+import json
+import copy
 import time
 import random
-import json
+import logging
+import numpy as np
 from typing import Any, Dict, List, Optional, Union
 
 import torch
-import numpy as np
 from PIL import Image
 import gradio as gr
-import spaces
 
-# Diffusers imports
 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
 
-# Hugging Face Hub imports
-from huggingface_hub import ModelCard, HfFileSystem
-
-##############################
-# ===== config.py =====
-##############################
-DTYPE = torch.bfloat16
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-BASE_MODEL = "black-forest-labs/FLUX.1-dev"
-TAEF1_MODEL = "madebyollin/taef1"
-MAX_SEED = 2**32 - 1
-
-##############################
-# ===== utilities.py =====
-##############################
-def calculate_shift(image_seq_len, base_seq_len=256, max_seq_len=4096, base_shift=0.5, max_shift=1.16):
+import spaces
+
+# 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):
+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.")
+        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
@@ -65,119 +77,31 @@ def retrieve_timesteps(scheduler,
         timesteps = scheduler.timesteps
     return timesteps, num_inference_steps
 
-def load_image_from_path(image_path: str):
-    return load_image(image_path)
-
-def randomize_seed_if_needed(randomize_seed: bool, seed: int, max_seed: int) -> int:
-    if randomize_seed:
-        return random.randint(0, max_seed)
-    return seed
-
-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()
-        elapsed = self.end_time - self.start_time
-        if self.activity_name:
-            print(f"Elapsed time for {self.activity_name}: {elapsed:.6f} seconds")
-        else:
-            print(f"Elapsed time: {elapsed:.6f} seconds")
-
-##############################
-# ===== Helper: truncate_prompt =====
-##############################
-def truncate_prompt(prompt: str) -> str:
-    """
-    Uses the global pipeline's tokenizer (assumed available as `pipe.tokenizer`)
-    to truncate the prompt to the maximum allowed length.
-    """
-    try:
-        tokenizer = pipe.tokenizer
-        tokenized = tokenizer(prompt, truncation=True, max_length=tokenizer.model_max_length, return_tensors="pt")
-        return tokenizer.decode(tokenized.input_ids[0], skip_special_tokens=True)
-    except Exception as e:
-        print(f"Error in truncate_prompt: {e}")
-        return prompt
-
-##############################
-# ===== enhance.py =====
-##############################
-def generate(message, max_new_tokens=256, temperature=0.9, top_p=0.95, repetition_penalty=1.0):
-    SYSTEM_PROMPT = (
-        "You are a prompt enhancer and your work is to enhance the given prompt under 100 words "
-        "without changing the essence, only write the enhanced prompt and nothing else."
-    )
-    timestamp = time.time()
-    formatted_prompt = f"<s>[INST] SYSTEM: {SYSTEM_PROMPT} [/INST][INST] {message} {timestamp} [/INST]"
-    api_url = "https://ruslanmv-hf-llm-api.hf.space/api/v1/chat/completions"
-    headers = {"Content-Type": "application/json"}
-    payload = {
-        "model": "mixtral-8x7b",
-        "messages": [{"role": "user", "content": formatted_prompt}],
-        "temperature": temperature,
-        "top_p": top_p,
-        "max_tokens": max_new_tokens,
-        "use_cache": False,
-        "stream": True
-    }
-    try:
-        response = requests.post(api_url, headers=headers, json=payload, stream=True)
-        response.raise_for_status()
-        full_output = ""
-        for line in response.iter_lines():
-            if not line:
-                continue
-            decoded_line = line.decode("utf-8").strip()
-            if decoded_line.startswith("data:"):
-                decoded_line = decoded_line[len("data:"):].strip()
-            if decoded_line == "[DONE]":
-                break
-            try:
-                json_data = json.loads(decoded_line)
-                for choice in json_data.get("choices", []):
-                    delta = choice.get("delta", {})
-                    content = delta.get("content", "")
-                    full_output += content
-                    yield full_output
-                    if choice.get("finish_reason") == "stop":
-                        return
-            except json.JSONDecodeError:
-                continue
-    except requests.exceptions.RequestException as e:
-        yield f"Error during generation: {str(e)}"
-
-##############################
-# ===== lora_handling.py =====
-##############################
-loras = [
-    {"image": "placeholder.jpg", "title": "Placeholder LoRA", "repo": "placeholder/repo", "weights": None, "trigger_word": ""}
-]
-
+# 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):
+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,
@@ -187,11 +111,14 @@ def flux_pipe_call_that_returns_an_iterable_of_images(self,
         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,
@@ -203,6 +130,7 @@ def flux_pipe_call_that_returns_an_iterable_of_images(self,
         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,
@@ -214,6 +142,7 @@ def flux_pipe_call_that_returns_an_iterable_of_images(self,
         generator,
         latents,
     )
+    
     sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
     image_seq_len = latents.shape[1]
     mu = calculate_shift(
@@ -232,13 +161,15 @@ def flux_pipe_call_that_returns_an_iterable_of_images(self,
         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)
+
+    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,
@@ -250,12 +181,14 @@ def flux_pipe_call_that_returns_an_iterable_of_images(self,
             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]
@@ -263,13 +196,157 @@ def flux_pipe_call_that_returns_an_iterable_of_images(self,
     torch.cuda.empty_cache()
     yield self.image_processor.postprocess(image, output_type=output_type)[0]
 
-def get_huggingface_safetensors(link: str) -> tuple:
+#--------------------------------------------------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
+                                                     )
+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, 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()
+        
+    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:
+    if(len(split_link) == 2):
         model_card = ModelCard.load(link)
-        base_model_card = model_card.data.get("base_model")
-        print(base_model_card)
-        if base_model_card not in ("black-forest-labs/FLUX.1-dev", "black-forest-labs/FLUX.1-schnell"):
+        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", "")
@@ -278,50 +355,47 @@ def get_huggingface_safetensors(link: str) -> tuple:
         try:
             list_of_files = fs.ls(link, detail=False)
             for file in list_of_files:
-                if file.endswith(".safetensors"):
+                if(file.endswith(".safetensors")):
                     safetensors_name = file.split("/")[-1]
-                if not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp")):
+                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)
-            raise Exception("Invalid LoRA repository")
+            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
     else:
         raise Exception("Invalid LoRA link format")
 
-def check_custom_model(link: str) -> tuple:
-    if link.startswith("https://"):
-        if link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co"):
+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])
-    return get_huggingface_safetensors(link)
-
-def create_lora_card(title: str, repo: str, trigger_word: str, image: str) -> str:
-    trigger_word_info = (f"Using: <code><b>{trigger_word}</b></code> as the trigger word"
-                         if trigger_word else "No trigger word found. Include it in your prompt")
-    return f'''
-    <div class="custom_lora_card">
-        <span>Loaded custom LoRA:</span>
-        <div class="card_internal">
-            <img src="{image}" />
-            <div>
-                <h3>{title}</h3>
-                <small>{trigger_word_info}<br></small>
-            </div>
-        </div>
-    </div>
-    '''
+    else: 
+        return get_huggingface_safetensors(link)
 
-def add_custom_lora(custom_lora: str) -> tuple:
+def add_custom_lora(custom_lora):
     global loras
-    if custom_lora:
+    if(custom_lora):
         try:
             title, repo, path, trigger_word, image = check_custom_model(custom_lora)
             print(f"Loaded custom LoRA: {repo}")
-            card = create_lora_card(title, repo, trigger_word, image)
+            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 existing_item_index is None:
+            if(not existing_item_index):
                 new_item = {
                     "image": image,
                     "title": title,
@@ -330,334 +404,112 @@ def add_custom_lora(custom_lora: str) -> tuple:
                     "trigger_word": trigger_word
                 }
                 print(new_item)
+                existing_item_index = len(loras)
                 loras.append(new_item)
-                existing_item_index = len(loras) - 1
+        
             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:
-            print(f"Error loading LoRA: {e}")
-            return gr.update(visible=True, value="Invalid LoRA"), gr.update(visible=False), gr.update(), "", None, ""
+            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() -> tuple:
+def remove_custom_lora():
     return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
 
-def prepare_prompt(prompt: str, selected_index: Optional[int], loras_list: list) -> str:
-    if selected_index is None:
-        raise gr.Error("You must select a LoRA before proceeding.🧨")
-    selected_lora = loras_list[selected_index]
-    trigger_word = selected_lora.get("trigger_word")
-    if trigger_word:
-        trigger_position = selected_lora.get("trigger_position", "append")
-        if trigger_position == "prepend":
-            prompt_mash = f"{trigger_word} {prompt}"
-        else:
-            prompt_mash = f"{prompt} {trigger_word}"
-    else:
-        prompt_mash = prompt
-    # Truncate the prompt using the tokenizer to ensure token indices are in range.
-    prompt_mash = truncate_prompt(prompt_mash)
-    return prompt_mash
-
-def unload_lora_weights(pipe, pipe_i2i):
-    if pipe is not None:
-        pipe.unload_lora_weights()
-    if pipe_i2i is not None:
-        pipe_i2i.unload_lora_weights()
-
-def load_lora_weights_into_pipeline(pipe_to_use, lora_path: str, weight_name: Optional[str]):
-    pipe_to_use.load_lora_weights(
-        lora_path,
-        weight_name=weight_name,
-        low_cpu_mem_usage=True
+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="YTheme/Minecraft", css=css, delete_cache=(60, 60)) as app:
+    title = gr.HTML(
+        """<h1>FLUX LoRA DLC🥳</h1>""",
+        elem_id="title",
     )
-
-def update_selection(evt: gr.SelectData, width, height) -> tuple:
-    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,
-    )
-
-##############################
-# ===== backend.py =====
-##############################
-class ModelManager:
-    def __init__(self, hf_token=None):
-        self.hf_token = hf_token
-        self.pipe = None
-        self.pipe_i2i = None
-        self.good_vae = None
-        self.taef1 = None
-        self.initialize_models()
-
-    def initialize_models(self):
-        self.taef1 = AutoencoderTiny.from_pretrained(TAEF1_MODEL, torch_dtype=DTYPE).to(DEVICE)
-        self.good_vae = AutoencoderKL.from_pretrained(BASE_MODEL, subfolder="vae", torch_dtype=DTYPE).to(DEVICE)
-        self.pipe = DiffusionPipeline.from_pretrained(BASE_MODEL, torch_dtype=DTYPE, vae=self.taef1).to(DEVICE)
-        self.pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
-            BASE_MODEL,
-            vae=self.good_vae,
-            transformer=self.pipe.transformer,
-            text_encoder=self.pipe.text_encoder,
-            tokenizer=self.pipe.tokenizer,
-            text_encoder_2=self.pipe.text_encoder_2,
-            tokenizer_2=self.pipe.tokenizer_2,
-            torch_dtype=DTYPE,
-        ).to(DEVICE)
-        # Bind the custom LoRA method to the pipeline class (to avoid __slots__ issues)
-        self.pipe.__class__.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images
-
-    @spaces.GPU(duration=100)
-    def generate_image(self, prompt_mash, steps, seed, cfg_scale, width, height, lora_scale):
-        generator = torch.Generator(device=DEVICE).manual_seed(seed)
-        with calculateDuration("Generating image"):
-            for img in self.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=self.good_vae,
-            ):
-                yield img
-
-    @spaces.GPU(duration=100)
-    def generate_image_to_image(self, prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed):
-        generator = torch.Generator(device=DEVICE).manual_seed(seed)
-        image_input = load_image_from_path(image_input_path)
-        with calculateDuration("Generating image to image"):
-            final_image = self.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
-
-##############################
-# ===== frontend.py =====
-##############################
-class Frontend:
-    def __init__(self, model_manager: ModelManager):
-        self.model_manager = model_manager
-        self.loras = loras
-        self.load_initial_loras()
-        self.css = self.define_css()
-
-    def define_css(self):
-        return '''
-        /* Title Styling */
-        #title {
-            text-align: center;
-            margin-bottom: 20px;
-        }
-        #title h1 {
-            font-size: 2.5rem;
-            margin: 0;
-            color: #333;
-        }
-        /* Button and Column Styling */
-        #gen_btn {
-            width: 100%;
-            padding: 12px;
-            font-weight: bold;
-            border-radius: 5px;
-        }
-        #gen_column {
-            display: flex;
-            align-items: center;
-            justify-content: center;
-        }
-        /* Gallery and List Styling */
-        #gallery .grid-wrap {
-            margin-top: 15px;
-        }
-        #lora_list {
-            background-color: #f5f5f5;
-            padding: 10px;
-            border-radius: 4px;
-            font-size: 0.9rem;
-        }
-        .card_internal {
-            display: flex;
-            align-items: center;
-            height: 100px;
-            margin-top: 10px;
-        }
-        .card_internal img {
-            margin-right: 10px;
-        }
-        .styler {
-            --form-gap-width: 0px !important;
-        }
-        /* Progress Bar Styling */
-        .progress-container {
-            width: 100%;
-            height: 20px;
-            background-color: #e0e0e0;
-            border-radius: 10px;
-            overflow: hidden;
-            margin-bottom: 20px;
-        }
-        .progress-bar {
-            height: 100%;
-            background-color: #4f46e5;
-            transition: width 0.3s ease-in-out;
-            width: calc(var(--current) / var(--total) * 100%);
-        }
-        '''
-
-    def load_initial_loras(self):
-        try:
-            from lora import loras as loras_list
-            self.loras = loras_list
-        except ImportError:
-            print("Warning: lora.py not found, using placeholder LoRAs.")
-
-    @spaces.GPU(duration=100)
-    def run_lora(self, 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)):
-        seed = randomize_seed_if_needed(randomize_seed, seed, MAX_SEED)
-        prompt_mash = prepare_prompt(prompt, selected_index, self.loras)
-        enhanced_text = ""
-        if use_enhancer:
-            for enhanced_chunk in generate(prompt_mash):
-                enhanced_text = enhanced_chunk
-                yield None, seed, gr.update(visible=False), enhanced_text
-            prompt_mash = enhanced_text
-        else:
-            enhanced_text = ""
-        selected_lora = self.loras[selected_index]
-        unload_lora_weights(self.model_manager.pipe, self.model_manager.pipe_i2i)
-        pipe_to_use = self.model_manager.pipe_i2i if image_input is not None else self.model_manager.pipe
-        load_lora_weights_into_pipeline(pipe_to_use, selected_lora["repo"], selected_lora.get("weights"))
-        if image_input is not None:
-            final_image = self.model_manager.generate_image_to_image(
-                prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed
+    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
             )
-            yield final_image, seed, gr.update(visible=False), enhanced_text
-        else:
-            image_generator = self.model_manager.generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale)
-            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), enhanced_text
-            yield final_image, seed, gr.update(value=progress_bar, visible=False), enhanced_text
-
-    def create_ui(self):
-        with gr.Blocks(theme=gr.themes.Base(), css=self.css, title="Flux LoRA Generation") as app:
-            title = gr.HTML("<h1>Flux LoRA Generation</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.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():
-                with gr.Column():
-                    selected_info = gr.Markdown("")
-                    gallery = gr.Gallery(
-                        [(item["image"], item["title"]) for item in self.loras],
-                        label="LoRA Collection",
-                        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)
-            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]
-            )
+                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)
-            gr.on(
-                triggers=[generate_button.click, prompt.submit],
-                fn=self.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("<div style='text-align:center; font-size:0.9em; margin-top:20px;'>Credits: <a href='https://ruslanmv.com' target='_blank'>ruslanmv.com</a></div>")
-            return app
-
-##############################
-# ===== Main app.py =====
-##############################
-if __name__ == "__main__":
-    hf_token = os.environ.get("HF_TOKEN")
-    if not hf_token:
-        raise ValueError("Hugging Face token (HF_TOKEN) not found in environment variables. Please set it.")
-    model_manager = ModelManager(hf_token=hf_token)
-    frontend = Frontend(model_manager)
-    app = frontend.create_ui()
-    app.queue()
-    app.launch(share=False, debug=True)
+    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(debug=True)