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, torch_dtype=torch.float16, use_safetensors=True, ) pipe_ultpony = StableDiffusionXLPipeline.from_pretrained( ckpt_dir_ultpony, vae=vae_ultpony, torch_dtype=torch.float16, use_safetensors=True, ) pipe_hybridpony = StableDiffusionXLPipeline.from_pretrained( ckpt_dir_hybridpony, vae=vae_hybridpony, 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="", 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="", image_size=(image.width, image.height) ) return parsed_answer[""] # 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, 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, 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("""

Real Pony RNS / Ultimate Pony RNS / Hybrid Pony SDXL

[Pony Realism] [Cyberrealistic Pony] [Stallion Dreams]
[Florence-2 Model] [Prompt Enhancer Long] [Prompt Enhancer Medium]

""") 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") 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, 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)