| import os |
| import gc |
| import gradio as gr |
| import numpy as np |
| import torch |
| import json |
| import spaces |
| import config |
| import utils |
| import logging |
| from PIL import Image, PngImagePlugin |
| from datetime import datetime |
| from diffusers.models import AutoencoderKL |
| from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline |
|
|
| logging.basicConfig(level=logging.INFO) |
| logger = logging.getLogger(__name__) |
|
|
| DESCRIPTION = "" |
| if not torch.cuda.is_available(): |
| DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU. </p>" |
| IS_COLAB = utils.is_google_colab() or os.getenv("IS_COLAB") == "1" |
| HF_TOKEN = os.getenv("HF_TOKEN") |
| CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1" |
| MIN_IMAGE_SIZE = int(os.getenv("MIN_IMAGE_SIZE", "512")) |
| MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048")) |
| USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1" |
| ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1" |
| OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./outputs") |
|
|
| MODEL = os.getenv( |
| "MODEL", |
| "https://huggingface.co/RunDiffusion/Juggernaut-XL-v9/blob/main/Juggernaut-XL_v9_RunDiffusionPhoto_v2.safetensors", |
| ) |
|
|
| torch.backends.cudnn.deterministic = True |
| torch.backends.cudnn.benchmark = False |
|
|
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
|
|
|
|
| def load_pipeline(model_name): |
| vae = AutoencoderKL.from_pretrained( |
| "madebyollin/sdxl-vae-fp16-fix", |
| torch_dtype=torch.float16, |
| ) |
| pipeline = ( |
| StableDiffusionXLPipeline.from_single_file |
| if MODEL.endswith(".safetensors") |
| else StableDiffusionXLPipeline.from_pretrained |
| ) |
|
|
| pipe = pipeline( |
| model_name, |
| vae=vae, |
| torch_dtype=torch.float16, |
| custom_pipeline="lpw_stable_diffusion_xl", |
| use_safetensors=True, |
| add_watermarker=False, |
| use_auth_token=HF_TOKEN, |
| variant="fp16", |
| ) |
|
|
| pipe.to(device) |
| return pipe |
|
|
|
|
| @spaces.GPU |
| def generate( |
| prompt: str, |
| negative_prompt: str = "", |
| seed: int = 0, |
| custom_width: int = 1024, |
| custom_height: int = 1024, |
| guidance_scale: float = 7.0, |
| num_inference_steps: int = 30, |
| sampler: str = "DPM++ 2M SDE Karras", |
| aspect_ratio_selector: str = "1024 x 1024", |
| use_upscaler: bool = False, |
| upscaler_strength: float = 0.55, |
| upscale_by: float = 1.5, |
| progress=gr.Progress(track_tqdm=True), |
| ) -> Image: |
| generator = utils.seed_everything(seed) |
|
|
| width, height = utils.aspect_ratio_handler( |
| aspect_ratio_selector, |
| custom_width, |
| custom_height, |
| ) |
|
|
| width, height = utils.preprocess_image_dimensions(width, height) |
|
|
| backup_scheduler = pipe.scheduler |
| pipe.scheduler = utils.get_scheduler(pipe.scheduler.config, sampler) |
|
|
| if use_upscaler: |
| upscaler_pipe = StableDiffusionXLImg2ImgPipeline(**pipe.components) |
| metadata = { |
| "prompt": prompt, |
| "negative_prompt": negative_prompt, |
| "resolution": f"{width} x {height}", |
| "guidance_scale": guidance_scale, |
| "num_inference_steps": num_inference_steps, |
| "seed": seed, |
| "sampler": sampler, |
| } |
|
|
| if use_upscaler: |
| new_width = int(width * upscale_by) |
| new_height = int(height * upscale_by) |
| metadata["use_upscaler"] = { |
| "upscale_method": "nearest-exact", |
| "upscaler_strength": upscaler_strength, |
| "upscale_by": upscale_by, |
| "new_resolution": f"{new_width} x {new_height}", |
| } |
| else: |
| metadata["use_upscaler"] = None |
| logger.info(json.dumps(metadata, indent=4)) |
|
|
| try: |
| if use_upscaler: |
| latents = pipe( |
| prompt=prompt, |
| negative_prompt=negative_prompt, |
| width=width, |
| height=height, |
| guidance_scale=guidance_scale, |
| num_inference_steps=num_inference_steps, |
| generator=generator, |
| output_type="latent", |
| ).images |
| upscaled_latents = utils.upscale(latents, "nearest-exact", upscale_by) |
| images = upscaler_pipe( |
| prompt=prompt, |
| negative_prompt=negative_prompt, |
| image=upscaled_latents, |
| guidance_scale=guidance_scale, |
| num_inference_steps=num_inference_steps, |
| strength=upscaler_strength, |
| generator=generator, |
| output_type="pil", |
| ).images |
| else: |
| images = pipe( |
| prompt=prompt, |
| negative_prompt=negative_prompt, |
| width=width, |
| height=height, |
| guidance_scale=guidance_scale, |
| num_inference_steps=num_inference_steps, |
| generator=generator, |
| output_type="pil", |
| ).images |
|
|
| if images and IS_COLAB: |
| for image in images: |
| filepath = utils.save_image(image, metadata, OUTPUT_DIR) |
| logger.info(f"Image saved as {filepath} with metadata") |
|
|
| return images, metadata |
| except Exception as e: |
| logger.exception(f"An error occurred: {e}") |
| raise |
| finally: |
| if use_upscaler: |
| del upscaler_pipe |
| pipe.scheduler = backup_scheduler |
| utils.free_memory() |
|
|
|
|
| if torch.cuda.is_available(): |
| pipe = load_pipeline(MODEL) |
| logger.info("Loaded on Device!") |
| else: |
| pipe = None |
|
|
| with gr.Blocks(css="style.css") as demo: |
| title = gr.HTML( |
| f"""<h1><span>{DESCRIPTION}</span></h1>""", |
| elem_id="title", |
| ) |
| |
| |
| with gr.Group(): |
| with gr.Row(): |
| prompt = gr.Text( |
| label="Prompt", |
| show_label=False, |
| max_lines=5, |
| placeholder="Enter your prompt", |
| container=False, |
| ) |
| run_button = gr.Button( |
| "Generate", |
| variant="primary", |
| scale=0 |
| ) |
| result = gr.Gallery( |
| label="Result", |
| columns=1, |
| preview=True, |
| show_label=False |
| ) |
| with gr.Accordion(label="Advanced Settings", open=False): |
| negative_prompt = gr.Text( |
| label="Negative Prompt", |
| max_lines=5, |
| placeholder="Enter a negative prompt", |
| value="(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)" |
| ) |
| aspect_ratio_selector = gr.Radio( |
| label="Aspect Ratio", |
| choices=config.aspect_ratios, |
| value="1024 x 1024", |
| container=True, |
| ) |
| with gr.Group(visible=False) as custom_resolution: |
| with gr.Row(): |
| custom_width = gr.Slider( |
| label="Width", |
| minimum=MIN_IMAGE_SIZE, |
| maximum=MAX_IMAGE_SIZE, |
| step=8, |
| value=1024, |
| ) |
| custom_height = gr.Slider( |
| label="Height", |
| minimum=MIN_IMAGE_SIZE, |
| maximum=MAX_IMAGE_SIZE, |
| step=8, |
| value=1024, |
| ) |
| use_upscaler = gr.Checkbox(label="Use Upscaler", value=False) |
| with gr.Row() as upscaler_row: |
| upscaler_strength = gr.Slider( |
| label="Strength", |
| minimum=0, |
| maximum=1, |
| step=0.05, |
| value=0.55, |
| visible=False, |
| ) |
| upscale_by = gr.Slider( |
| label="Upscale by", |
| minimum=1, |
| maximum=1.5, |
| step=0.1, |
| value=1.5, |
| visible=False, |
| ) |
|
|
| sampler = gr.Dropdown( |
| label="Sampler", |
| choices=config.sampler_list, |
| interactive=True, |
| value="DPM++ 2M SDE Karras", |
| ) |
| with gr.Row(): |
| seed = gr.Slider( |
| label="Seed", minimum=0, maximum=utils.MAX_SEED, step=1, value=0 |
| ) |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
| with gr.Group(): |
| with gr.Row(): |
| guidance_scale = gr.Slider( |
| label="Guidance scale", |
| minimum=1, |
| maximum=12, |
| step=0.1, |
| value=7.0, |
| ) |
| num_inference_steps = gr.Slider( |
| label="Number of inference steps", |
| minimum=1, |
| maximum=50, |
| step=1, |
| value=28, |
| ) |
| with gr.Accordion(label="Generation Parameters", open=False): |
| gr_metadata = gr.JSON(label="Metadata", show_label=False) |
| gr.Examples( |
| examples=config.examples, |
| inputs=prompt, |
| outputs=[result, gr_metadata], |
| fn=lambda *args, **kwargs: generate(*args, use_upscaler=True, **kwargs), |
| cache_examples=CACHE_EXAMPLES, |
| ) |
| use_upscaler.change( |
| fn=lambda x: [gr.update(visible=x), gr.update(visible=x)], |
| inputs=use_upscaler, |
| outputs=[upscaler_strength, upscale_by], |
| queue=False, |
| api_name=False, |
| ) |
| aspect_ratio_selector.change( |
| fn=lambda x: gr.update(visible=x == "Custom"), |
| inputs=aspect_ratio_selector, |
| outputs=custom_resolution, |
| queue=False, |
| api_name=False, |
| ) |
|
|
| inputs = [ |
| prompt, |
| negative_prompt, |
| seed, |
| custom_width, |
| custom_height, |
| guidance_scale, |
| num_inference_steps, |
| sampler, |
| aspect_ratio_selector, |
| use_upscaler, |
| upscaler_strength, |
| upscale_by, |
| ] |
|
|
| prompt.submit( |
| fn=utils.randomize_seed_fn, |
| inputs=[seed, randomize_seed], |
| outputs=seed, |
| queue=False, |
| api_name=False, |
| ).then( |
| fn=generate, |
| inputs=inputs, |
| outputs=result, |
| api_name="run", |
| ) |
| negative_prompt.submit( |
| fn=utils.randomize_seed_fn, |
| inputs=[seed, randomize_seed], |
| outputs=seed, |
| queue=False, |
| api_name=False, |
| ).then( |
| fn=generate, |
| inputs=inputs, |
| outputs=result, |
| api_name=False, |
| ) |
| run_button.click( |
| fn=utils.randomize_seed_fn, |
| inputs=[seed, randomize_seed], |
| outputs=seed, |
| queue=False, |
| api_name=False, |
| ).then( |
| fn=generate, |
| inputs=inputs, |
| outputs=[result, gr_metadata], |
| api_name=False, |
| ) |
| demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB) |
