Create app-v1-working.py

app-v1-working.py

@@ -0,0 +1,515 @@
+import os
+import json
+import copy
+import time
+import random
+import logging
+import numpy as np
+from typing import Any, Dict, List, Optional, Union
+
+import torch
+from PIL import Image
+import gradio as gr
+
+from diffusers import (
+    DiffusionPipeline,
+    AutoencoderTiny,
+    AutoencoderKL,
+    AutoPipelineForImage2Image,
+    FluxPipeline,
+    FlowMatchEulerDiscreteScheduler
+)
+
+from huggingface_hub import (
+    hf_hub_download,
+    HfFileSystem,
+    ModelCard,
+    snapshot_download
+)
+
+from diffusers.utils import load_image
+
+import spaces
+
+# Attempt to import loras from lora.py; otherwise use a default placeholder.
+try:
+    from lora import loras
+except ImportError:
+    loras = [
+        {"image": "placeholder.jpg", "title": "Placeholder LoRA", "repo": "placeholder/repo", "weights": None, "trigger_word": ""}
+    ]
+
+#---if workspace = local or colab---
+# (Optional: add Hugging Face login code here)
+
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.16,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+# FLUX pipeline
+@torch.inference_mode()
+def flux_pipe_call_that_returns_an_iterable_of_images(
+    self,
+    prompt: Union[str, List[str]] = None,
+    prompt_2: Optional[Union[str, List[str]]] = None,
+    height: Optional[int] = None,
+    width: Optional[int] = None,
+    num_inference_steps: int = 28,
+    timesteps: List[int] = None,
+    guidance_scale: float = 3.5,
+    num_images_per_prompt: Optional[int] = 1,
+    generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+    latents: Optional[torch.FloatTensor] = None,
+    prompt_embeds: Optional[torch.FloatTensor] = None,
+    pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+    output_type: Optional[str] = "pil",
+    return_dict: bool = True,
+    joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+    max_sequence_length: int = 512,
+    good_vae: Optional[Any] = None,
+):
+    height = height or self.default_sample_size * self.vae_scale_factor
+    width = width or self.default_sample_size * self.vae_scale_factor
+    
+    self.check_inputs(
+        prompt,
+        prompt_2,
+        height,
+        width,
+        prompt_embeds=prompt_embeds,
+        pooled_prompt_embeds=pooled_prompt_embeds,
+        max_sequence_length=max_sequence_length,
+    )
+
+    self._guidance_scale = guidance_scale
+    self._joint_attention_kwargs = joint_attention_kwargs
+    self._interrupt = False
+
+    batch_size = 1 if isinstance(prompt, str) else len(prompt)
+    device = self._execution_device
+
+    lora_scale = joint_attention_kwargs.get("scale", None) if joint_attention_kwargs is not None else None
+    prompt_embeds, pooled_prompt_embeds, text_ids = self.encode_prompt(
+        prompt=prompt,
+        prompt_2=prompt_2,
+        prompt_embeds=prompt_embeds,
+        pooled_prompt_embeds=pooled_prompt_embeds,
+        device=device,
+        num_images_per_prompt=num_images_per_prompt,
+        max_sequence_length=max_sequence_length,
+        lora_scale=lora_scale,
+    )
+    
+    num_channels_latents = self.transformer.config.in_channels // 4
+    latents, latent_image_ids = self.prepare_latents(
+        batch_size * num_images_per_prompt,
+        num_channels_latents,
+        height,
+        width,
+        prompt_embeds.dtype,
+        device,
+        generator,
+        latents,
+    )
+    
+    sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+    image_seq_len = latents.shape[1]
+    mu = calculate_shift(
+        image_seq_len,
+        self.scheduler.config.base_image_seq_len,
+        self.scheduler.config.max_image_seq_len,
+        self.scheduler.config.base_shift,
+        self.scheduler.config.max_shift,
+    )
+    timesteps, num_inference_steps = retrieve_timesteps(
+        self.scheduler,
+        num_inference_steps,
+        device,
+        timesteps,
+        sigmas,
+        mu=mu,
+    )
+    self._num_timesteps = len(timesteps)
+
+    guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32).expand(latents.shape[0]) if self.transformer.config.guidance_embeds else None
+
+    for i, t in enumerate(timesteps):
+        if self.interrupt:
+            continue
+
+        timestep = t.expand(latents.shape[0]).to(latents.dtype)
+
+        noise_pred = self.transformer(
+            hidden_states=latents,
+            timestep=timestep / 1000,
+            guidance=guidance,
+            pooled_projections=pooled_prompt_embeds,
+            encoder_hidden_states=prompt_embeds,
+            txt_ids=text_ids,
+            img_ids=latent_image_ids,
+            joint_attention_kwargs=self.joint_attention_kwargs,
+            return_dict=False,
+        )[0]
+
+        latents_for_image = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+        latents_for_image = (latents_for_image / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+        image = self.vae.decode(latents_for_image, return_dict=False)[0]
+        yield self.image_processor.postprocess(image, output_type=output_type)[0]
+        latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+        torch.cuda.empty_cache()
+        
+    latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+    latents = (latents / good_vae.config.scaling_factor) + good_vae.config.shift_factor
+    image = good_vae.decode(latents, return_dict=False)[0]
+    self.maybe_free_model_hooks()
+    torch.cuda.empty_cache()
+    yield self.image_processor.postprocess(image, output_type=output_type)[0]
+
+#--------------------------------------------------Model Initialization-----------------------------------------------------------------------------------------#
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+base_model = "black-forest-labs/FLUX.1-dev"
+
+# TAEF1 is a very tiny autoencoder which uses the same "latent API" as FLUX.1's VAE.
+taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
+good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
+pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
+pipe_i2i = AutoPipelineForImage2Image.from_pretrained(base_model,
+                                                      vae=good_vae,
+                                                      transformer=pipe.transformer,
+                                                      text_encoder=pipe.text_encoder,
+                                                      tokenizer=pipe.tokenizer,
+                                                      text_encoder_2=pipe.text_encoder_2,
+                                                      tokenizer_2=pipe.tokenizer_2,
+                                                      torch_dtype=dtype
+                                                     )
+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):
+        model_card = ModelCard.load(link)
+        base_model = model_card.data.get("base_model")
+        print(base_model)
+        if((base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "black-forest-labs/FLUX.1-schnell")):
+            raise Exception("Flux LoRA Not Found!")
+        image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
+        trigger_word = model_card.data.get("instance_prompt", "")
+        image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
+        fs = HfFileSystem()
+        try:
+            list_of_files = fs.ls(link, detail=False)
+            for file in list_of_files:
+                if(file.endswith(".safetensors")):
+                    safetensors_name = file.split("/")[-1]
+                if (not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))):
+                    image_elements = file.split("/")
+                    image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}"
+        except Exception as e:
+            print(e)
+            gr.Warning(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):
+    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="YTheme/Minecraft", css=css, delete_cache=(60, 60)) 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)
+                with gr.Row():
+                    use_enhancer = gr.Checkbox(value=False, label="Use Prompt Enhancer")
+                    show_enhanced_prompt = gr.Checkbox(value=False, label="Display Enhanced Prompt")
+                enhanced_prompt_box = gr.Textbox(label="Enhanced Prompt", visible=False)
+            # Add the change event so that the enhanced prompt box visibility toggles.
+            show_enhanced_prompt.change(fn=lambda show: gr.update(visible=show),
+                                        inputs=show_enhanced_prompt,
+                                        outputs=enhanced_prompt_box)
+    gallery.select(
+        update_selection,
+        inputs=[width, height],
+        outputs=[prompt, selected_info, selected_index, width, height]
+    )
+    custom_lora.input(
+        add_custom_lora,
+        inputs=[custom_lora],
+        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt]
+    )
+    custom_lora_button.click(
+        remove_custom_lora,
+        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora]
+    )
+    gr.on(
+        triggers=[generate_button.click, prompt.submit],
+        fn=run_lora,
+        inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale],
+        outputs=[result, seed, progress_bar]
+    )
+
+app.queue()
+app.launch(debug=True)