app.py

import spaces
import os
import torch
import random
from huggingface_hub import snapshot_download
from diffusers import StableDiffusionXLPipeline, AutoencoderKL
from diffusers import (
    EulerAncestralDiscreteScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSDEScheduler,
    HeunDiscreteScheduler,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UniPCMultistepScheduler,
)
from diffusers.models.attention_processor import AttnProcessor2_0
import gradio as gr
from PIL import Image
import numpy as np
from transformers import AutoProcessor, AutoModelForCausalLM, pipeline
import requests
from RealESRGAN import RealESRGAN

import os

from unittest.mock import patch
from typing import Union
from transformers.dynamic_module_utils import get_imports


def fixed_get_imports(filename):
    """Work around for https://huggingface.co/microsoft/phi-1_5/discussions/72."""
    if not str(filename).endswith("/modeling_florence2.py"):
        return get_imports(filename)
    imports = get_imports(filename)
    imports.remove("flash_attn")
    return imports


import subprocess
#subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

def download_file(url, folder_path, filename):
    if not os.path.exists(folder_path):
        os.makedirs(folder_path)
    file_path = os.path.join(folder_path, filename)

    if os.path.isfile(file_path):
        print(f"File already exists: {file_path}")
    else:
        response = requests.get(url, stream=True)
        if response.status_code == 200:
            with open(file_path, 'wb') as file:
                for chunk in response.iter_content(chunk_size=1024):
                    file.write(chunk)
            print(f"File successfully downloaded and saved: {file_path}")
        else:
            print(f"Error downloading the file. Status code: {response.status_code}")

# Download ESRGAN models
download_file("https://huggingface.co/ai-forever/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth?download=true", "models/upscalers/", "RealESRGAN_x2.pth")
download_file("https://huggingface.co/ai-forever/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth?download=true", "models/upscalers/", "RealESRGAN_x4.pth")

# Download the model files
ckpt_dir_realpony = snapshot_download(repo_id="silveroxides/RNS_RealPonyV20")
ckpt_dir_ultpony = snapshot_download(repo_id="silveroxides/RNS_PonyUltimateV20")
ckpt_dir_hybridpony = snapshot_download(repo_id="silveroxides/RealHybridPony")

# Load the models
vae_realpony = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir_realpony, "vae"), torch_dtype=torch.float16)
vae_ultpony = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir_ultpony, "vae"), torch_dtype=torch.float16)
vae_hybridpony = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir_hybridpony, "vae"), torch_dtype=torch.float16)

pipe_realpony = StableDiffusionXLPipeline.from_pretrained(
    ckpt_dir_realpony,
    vae=vae_realpony,
    custom_pipeline="multimodalart/sdxl_perturbed_attention_guidance",
    torch_dtype=torch.float16,
    use_safetensors=True,
)
pipe_ultpony = StableDiffusionXLPipeline.from_pretrained(
    ckpt_dir_ultpony,
    vae=vae_ultpony,
    custom_pipeline="multimodalart/sdxl_perturbed_attention_guidance",
    torch_dtype=torch.float16,
    use_safetensors=True,
)
pipe_hybridpony = StableDiffusionXLPipeline.from_pretrained(
    ckpt_dir_hybridpony,
    vae=vae_hybridpony,
    custom_pipeline="multimodalart/sdxl_perturbed_attention_guidance",
    torch_dtype=torch.float16,
    use_safetensors=True,
)

pipe_realpony = pipe_realpony.to("cuda")
pipe_ultpony = pipe_ultpony.to("cuda")
pipe_hybridpony = pipe_hybridpony.to("cuda")

pipe_realpony.unet.set_attn_processor(AttnProcessor2_0())
pipe_ultpony.unet.set_attn_processor(AttnProcessor2_0())
pipe_hybridpony.unet.set_attn_processor(AttnProcessor2_0())

# Define samplers
samplers = {
    "Euler a": EulerAncestralDiscreteScheduler.from_config(pipe_realpony.scheduler.config),
    "DPM++ SDE Karras": DPMSolverSDEScheduler.from_config(pipe_realpony.scheduler.config, use_karras_sigmas=True),
    "Heun": HeunDiscreteScheduler.from_config(pipe_realpony.scheduler.config),
    # New samplers
    "DPM++ 2M SDE Karras": DPMSolverMultistepScheduler.from_config(pipe_realpony.scheduler.config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++"),
    "DPM++ 2M": DPMSolverMultistepScheduler.from_config(pipe_realpony.scheduler.config),
    "DDIM": DDIMScheduler.from_config(pipe_realpony.scheduler.config),
    "LMS": LMSDiscreteScheduler.from_config(pipe_realpony.scheduler.config),
    "PNDM": PNDMScheduler.from_config(pipe_realpony.scheduler.config),
    "UniPC": UniPCMultistepScheduler.from_config(pipe_realpony.scheduler.config),
}

DEFAULT_POSITIVE_PREFIX = "score_8_up, score_7_up, accurate, genuine"
DEFAULT_POSITIVE_SUFFIX = "perfect composition, detailed, photorealism, real life, raw, reality, cinematic"
DEFAULT_NEGATIVE_PREFIX = "score_1, score_2, score_3, text, artist name, signature, watermark, logo, url, web address"
DEFAULT_NEGATIVE_SUFFIX = "low quality, low resolution, simple background, bad composition, deformed, disfigured, sketch, unfinished"

# Initialize Florence model
device = "cuda" if torch.cuda.is_available() else "cpu"

#def load_models():
    #with patch("transformers.dynamic_module_utils.get_imports", fixed_get_imports):
florence_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval()
florence_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True)
    #return florence_model, florence_processor

#florence_model, florence_processor = load_models()

# Prompt Enhancer
enhancer_medium = pipeline("summarization", model="gokaygokay/Lamini-Prompt-Enchance", device=device)
enhancer_long = pipeline("summarization", model="gokaygokay/Lamini-Prompt-Enchance-Long", device=device)

class LazyRealESRGAN:
    def __init__(self, device, scale):
        self.device = device
        self.scale = scale
        self.model = None

    def load_model(self):
        if self.model is None:
            self.model = RealESRGAN(self.device, scale=self.scale)
            self.model.load_weights(f'models/upscalers/RealESRGAN_x{self.scale}.pth', download=False)

    def predict(self, img):
        self.load_model()
        return self.model.predict(img)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

lazy_realesrgan_x2 = LazyRealESRGAN(device, scale=2)
lazy_realesrgan_x4 = LazyRealESRGAN(device, scale=4)

# Florence caption function
def florence_caption(image):
    # Convert image to PIL if it's not already
    if not isinstance(image, Image.Image):
        image = Image.fromarray(image)
    
    inputs = florence_processor(text="<DETAILED_CAPTION>", images=image, return_tensors="pt").to(device)
    generated_ids = florence_model.generate(
        input_ids=inputs["input_ids"],
        pixel_values=inputs["pixel_values"],
        max_new_tokens=1024,
        early_stopping=False,
        do_sample=False,
        num_beams=3,
    )
    generated_text = florence_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
    parsed_answer = florence_processor.post_process_generation(
        generated_text,
        task="<DETAILED_CAPTION>",
        image_size=(image.width, image.height)
    )
    return parsed_answer["<DETAILED_CAPTION>"]

# Prompt Enhancer function
def enhance_prompt(input_prompt, model_choice):
    if model_choice == "Medium":
        result = enhancer_medium("Enhance the description: " + input_prompt)
        enhanced_text = result[0]['summary_text']
    else:  # Long
        result = enhancer_long("Enhance the description: " + input_prompt)
        enhanced_text = result[0]['summary_text']
    
    return enhanced_text

def upscale_image(image, scale):
    # Ensure image is a PIL Image object
    if not isinstance(image, Image.Image):
        if isinstance(image, np.ndarray):
            image = Image.fromarray(image)
        else:
            raise ValueError("Input must be a PIL Image or a numpy array")

    if scale == 2:
        return lazy_realesrgan_x2.predict(image)
    elif scale == 4:
        return lazy_realesrgan_x4.predict(image)
    else:
        return image

@spaces.GPU(duration=120)
def generate_image(model_choice, additional_positive_prompt, additional_negative_prompt, height, width, num_inference_steps,
                   guidance_scale, pag_scale, pag_layers, num_images_per_prompt, use_random_seed, seed, sampler, clip_skip, 
                   use_florence2, use_medium_enhancer, use_long_enhancer,
                   use_positive_prefix, use_positive_suffix, use_negative_prefix, use_negative_suffix,
                   use_upscaler, upscale_factor,
                   input_image=None, progress=gr.Progress(track_tqdm=True)):
    
    # Select the appropriate pipe based on the model choice
    if model_choice == "Real Pony RNS":
        pipe = pipe_realpony
    elif model_choice == "Ultimate Pony RNS":
        pipe = pipe_ultpony
    else:  # "Hybrid Pony SDXL"
        pipe = pipe_hybridpony
    
    if use_random_seed:
        seed = random.randint(0, 2**32 - 1)
    else:
        seed = int(seed)  # Ensure seed is an integer
    
    # Set the scheduler based on the selected sampler
    pipe.scheduler = samplers[sampler]
    
    # Set clip skip
    pipe.text_encoder.config.num_hidden_layers -= (clip_skip - 1)
    
    # Start with the default positive prompt prefix if enabled
    full_positive_prompt = DEFAULT_POSITIVE_PREFIX + ", " if use_positive_prefix else ""

    # Add Florence-2 caption if enabled and image is provided
    if use_florence2 and input_image is not None:
        florence2_caption = florence_caption(input_image)
        florence2_caption = florence2_caption.lower().replace('.', ',')
        additional_positive_prompt = f"{florence2_caption}, {additional_positive_prompt}" if additional_positive_prompt else florence2_caption

    # Enhance only the additional positive prompt if enhancers are enabled
    if additional_positive_prompt:
        enhanced_prompt = additional_positive_prompt
        if use_medium_enhancer:
            medium_enhanced = enhance_prompt(enhanced_prompt, "Medium")
            medium_enhanced = medium_enhanced.lower().replace('.', ',')
            enhanced_prompt = f"{enhanced_prompt}, {medium_enhanced}"
        if use_long_enhancer:
            long_enhanced = enhance_prompt(enhanced_prompt, "Long")
            long_enhanced = long_enhanced.lower().replace('.', ',')
            enhanced_prompt = f"{enhanced_prompt}, {long_enhanced}"
        full_positive_prompt += enhanced_prompt

    # Add the default positive suffix if enabled
    if use_positive_suffix:
        full_positive_prompt += f", {DEFAULT_POSITIVE_SUFFIX}"
    
    # Combine default negative prompt with additional negative prompt
    full_negative_prompt = ""
    if use_negative_prefix:
        full_negative_prompt += f"{DEFAULT_NEGATIVE_PREFIX}, "
    full_negative_prompt += additional_negative_prompt if additional_negative_prompt else ""
    if use_negative_suffix:
        full_negative_prompt += f", {DEFAULT_NEGATIVE_SUFFIX}"
    
    try:
        images = pipe(
            prompt=full_positive_prompt,
            negative_prompt=full_negative_prompt,
            height=height,
            width=width,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            pag_scale=pag_scale,
            pag_layers=pag_layers,
            num_images_per_prompt=num_images_per_prompt,
            generator=torch.Generator(pipe.device).manual_seed(seed)
        ).images

        if use_upscaler:
            print("Upscaling images")
            upscaled_images = []
            for i, img in enumerate(images):
                print(f"Upscaling image {i+1}")
                if not isinstance(img, Image.Image):
                    print(f"Converting image {i+1} to PIL Image")
                    img = Image.fromarray(np.uint8(img))
                upscaled_img = upscale_image(img, upscale_factor)
                upscaled_images.append(upscaled_img)
            images = upscaled_images

        print("Returning results")
        return images, seed, full_positive_prompt, full_negative_prompt
    except Exception as e:
        print(f"Error during image generation: {str(e)}")
        import traceback
        traceback.print_exc()
        return None, seed, full_positive_prompt, full_negative_prompt

# Gradio interface
with gr.Blocks(theme='bethecloud/storj_theme') as demo:
    gr.HTML("""
    <h1 align="center">Real Pony RNS / Ultimate Pony RNS / Hybrid Pony SDXL</h1>
    <p align="center">
    <a href="https://huggingface.co/silveroxides/RNS_RealPonyV20" target="_blank">[Pony Realism]</a>
    <a href="https://huggingface.co/silveroxides/RNS_PonyUltimateV20" target="_blank">[Cyberrealistic Pony]</a>
    <a href="https://huggingface.co/silveroxides/RealHybridPony" target="_blank">[Stallion Dreams]</a><br>
    <a href="https://huggingface.co/microsoft/Florence-2-base" target="_blank">[Florence-2 Model]</a>
    <a href="https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance-Long" target="_blank">[Prompt Enhancer Long]</a>
    <a href="https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance" target="_blank">[Prompt Enhancer Medium]</a>
    </p>
    """)

    with gr.Row():
        with gr.Column(scale=1):
            model_choice = gr.Dropdown(
                    ["Real Pony RNS", "Ultimate Pony RNS", "Hybrid Pony SDXL"], 
                    label="Model Choice", 
                    value="Real Pony RNS")
            positive_prompt = gr.Textbox(label="Positive Prompt", placeholder="Add your positive prompt here")
            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Add your negative prompt here")
            
            with gr.Accordion("Advanced settings", open=False):
                height = gr.Slider(512, 2048, 1024, step=64, label="Height")
                width = gr.Slider(512, 2048, 1024, step=64, label="Width")
                num_inference_steps = gr.Slider(20, 100, 30, step=1, label="Number of Inference Steps")
                guidance_scale = gr.Slider(1, 20, 6, step=0.1, label="Guidance Scale")
                pag_scale = gr.Number(label="Pag Scale", value=3.0)
                pag_layers = gr.Dropdown(label="Model layers to apply Pag to", info="mid is the one used on the paper, up and down blocks seem unstable", choices=["up", "mid", "down"], multiselect=True, value="mid")
                num_images_per_prompt = gr.Slider(1, 4, 1, step=1, label="Number of images per prompt")
                use_random_seed = gr.Checkbox(label="Use Random Seed", value=True)
                seed = gr.Number(label="Seed", value=0, precision=0)
                sampler = gr.Dropdown(label="Sampler", choices=list(samplers.keys()), value="Euler a")
                clip_skip = gr.Slider(1, 4, 2, step=1, label="Clip skip")
            
            with gr.Accordion("Captioner and Enhancers", open=False):
                input_image = gr.Image(label="Input Image for Florence-2 Captioner")
                use_florence2 = gr.Checkbox(label="Use Florence-2 Captioner", value=False)
                use_medium_enhancer = gr.Checkbox(label="Use Medium Prompt Enhancer", value=False)
                use_long_enhancer = gr.Checkbox(label="Use Long Prompt Enhancer", value=False)

            with gr.Accordion("Upscaler Settings", open=False):
                use_upscaler = gr.Checkbox(label="Use Upscaler", value=False)
                upscale_factor = gr.Radio(label="Upscale Factor", choices=[2, 4], value=2)

            generate_btn = gr.Button("Generate Image")
            
            with gr.Accordion("Prefix and Suffix Settings", open=True):
                use_positive_prefix = gr.Checkbox(
                    label="Use Positive Prefix", 
                    value=True, 
                    info=f"Prefix: {DEFAULT_POSITIVE_PREFIX}"
                )
                use_positive_suffix = gr.Checkbox(
                    label="Use Positive Suffix", 
                    value=True, 
                    info=f"Suffix: {DEFAULT_POSITIVE_SUFFIX}"
                )
                use_negative_prefix = gr.Checkbox(
                    label="Use Negative Prefix", 
                    value=True, 
                    info=f"Prefix: {DEFAULT_NEGATIVE_PREFIX}"
                )
                use_negative_suffix = gr.Checkbox(
                    label="Use Negative Suffix", 
                    value=True, 
                    info=f"Suffix: {DEFAULT_NEGATIVE_SUFFIX}"
                )

        with gr.Column(scale=1):
            output_gallery = gr.Gallery(label="Result", elem_id="gallery", show_label=False)
            seed_used = gr.Number(label="Seed Used")
            full_positive_prompt_used = gr.Textbox(label="Full Positive Prompt Used")
            full_negative_prompt_used = gr.Textbox(label="Full Negative Prompt Used")

    generate_btn.click(
        fn=generate_image,
        inputs=[
            model_choice,  # Add this new input
            positive_prompt, negative_prompt, height, width, num_inference_steps,
            guidance_scale, pag_scale, pag_layers, num_images_per_prompt, use_random_seed, seed, sampler,
            clip_skip, use_florence2, use_medium_enhancer, use_long_enhancer,
            use_positive_prefix, use_positive_suffix, use_negative_prefix, use_negative_suffix,
            use_upscaler, upscale_factor,
            input_image
        ],
        outputs=[output_gallery, seed_used, full_positive_prompt_used, full_negative_prompt_used]
    )

demo.launch(debug=True)