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 import gradio as gr from PIL import Image from transformers import AutoProcessor, AutoModelForCausalLM, pipeline import subprocess subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) # Download the model files ckpt_dir = snapshot_download(repo_id="John6666/pony-realism-v21main-sdxl") # Load the models vae = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir, "vae"), torch_dtype=torch.float16) pipe = StableDiffusionXLPipeline.from_pretrained( ckpt_dir, vae=vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16" ) pipe = pipe.to("cuda") # Define samplers samplers = { "Euler a": EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config), "DPM++ 2M": DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, algorithm_type="dpmsolver++", use_karras_sigmas=True), "DPM++ SDE Karras": DPMSolverSDEScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True) } DEFAULT_POSITIVE_PREFIX = "score_9, score_8_up, score_7_up, BREAK," DEFAULT_POSITIVE_SUFFIX = "(masterpiece), best quality, very aesthetic, perfect face" DEFAULT_NEGATIVE_PREFIX = "score_1, score_2, score_3, text" DEFAULT_NEGATIVE_SUFFIX = "nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn" # Initialize Florence model device = "cuda" if torch.cuda.is_available() else "cpu" 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) # 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) # 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 @spaces.GPU(duration=120) def generate_image(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, input_image=None, progress=gr.Progress(track_tqdm=True)): 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 full_positive_prompt = DEFAULT_POSITIVE_PREFIX # 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 += f"{enhanced_prompt}" # Add the default positive suffix full_positive_prompt += f", {DEFAULT_POSITIVE_SUFFIX}" # Combine default negative prompt with additional negative prompt full_negative_prompt = f"{DEFAULT_NEGATIVE_PREFIX}, {additional_negative_prompt}, {DEFAULT_NEGATIVE_SUFFIX}" if additional_negative_prompt else f"{DEFAULT_NEGATIVE_PREFIX}, {DEFAULT_NEGATIVE_SUFFIX}" try: image = 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 return image, seed, full_positive_prompt except Exception as e: print(f"Error during image generation: {str(e)}") return None, seed, full_positive_prompt # Gradio interface with gr.Blocks(theme='bethecloud/storj_theme') as demo: gr.HTML("""

Pony Realism v21 SDXL - Text-to-Image Generation

[HF Model Page] [civitai Model Page] [Florence-2 Model] [Prompt Enhancer Long] [Prompt Enhancer Medium]

""") with gr.Row(): with gr.Column(scale=1): 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, 50, 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="DPM++ SDE Karras") 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) generate_btn = gr.Button("Generate Image") 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_prompt_used = gr.Textbox(label="Full Positive Prompt Used") generate_btn.click( fn=generate_image, inputs=[ 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, input_image ], outputs=[output_gallery, seed_used, full_prompt_used] ) demo.launch(debug=True)