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from __future__ import annotations |
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import argparse |
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import os |
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import sys |
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import random |
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import gradio as gr |
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import numpy as np |
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import uuid |
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import spaces |
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from diffusers import ConsistencyDecoderVAE, DPMSolverMultistepScheduler, Transformer2DModel, AutoencoderKL, SASolverScheduler |
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import torch |
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from typing import Tuple |
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from datetime import datetime |
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from peft import PeftModel |
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from diffusers_patches import pixart_sigma_init_patched_inputs, PixArtSigmaPipeline |
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DESCRIPTION = """ |
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# PixArt-Sigma 1024px |
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#### [PixArt-Sigma 1024px](https://github.com/PixArt-alpha/PixArt-sigma) is a transformer-based text-to-image diffusion system trained on text embeddings from T5. This demo uses the [PixArt-alpha/PixArt-Sigma-XL-2-1024-MS](https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS) checkpoint. |
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#### English prompts ONLY; 提示词仅限英文 |
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#### Welcome to Star🌟 our [GitHub](https://github.com/PixArt-alpha/PixArt-sigma) |
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### <span style='color: red;'>You may change the DPM-Solver inference steps from 14 to 20, or DPM-Solver Guidance scale from 4.5 to 3.5 if you didn't get satisfied results. |
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""" |
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if not torch.cuda.is_available(): |
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" |
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MAX_SEED = np.iinfo(np.int32).max |
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CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "1") == "1" |
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MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "6000")) |
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USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1" |
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ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1" |
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PORT = int(os.getenv("DEMO_PORT", "15432")) |
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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style_list = [ |
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{ |
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"name": "(No style)", |
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"prompt": "{prompt}", |
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"negative_prompt": "", |
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}, |
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{ |
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"name": "Cinematic", |
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"prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", |
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"negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", |
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}, |
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{ |
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"name": "Realistic", |
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"prompt": "Photorealistic {prompt} . Ulta-realistic, professional, 4k, highly detailed", |
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"negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly, disfigured", |
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}, |
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{ |
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"name": "Anime", |
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"prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed", |
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"negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast", |
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}, |
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{ |
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"name": "Manga", |
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"prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style", |
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"negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style", |
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}, |
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{ |
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"name": "Digital Art", |
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"prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed", |
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"negative_prompt": "photo, photorealistic, realism, ugly", |
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}, |
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{ |
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"name": "Pixel art", |
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"prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics", |
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"negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic", |
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}, |
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{ |
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"name": "Fantasy art", |
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"prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy", |
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"negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white", |
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}, |
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{ |
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"name": "Neonpunk", |
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"prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional", |
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"negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured", |
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}, |
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{ |
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"name": "3D Model", |
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"prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting", |
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"negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting", |
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}, |
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] |
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styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} |
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STYLE_NAMES = list(styles.keys()) |
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DEFAULT_STYLE_NAME = "Realistic" |
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SCHEDULE_NAME = ["DPM-Solver", "SA-Solver"] |
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DEFAULT_SCHEDULE_NAME = "DPM-Solver" |
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NUM_IMAGES_PER_PROMPT = 1 |
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def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]: |
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p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) |
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if not negative: |
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negative = "" |
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return p.replace("{prompt}", positive), n + negative |
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if torch.cuda.is_available(): |
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weight_dtype = torch.float16 |
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T5_token_max_length = 300 |
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print( |
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"Changing _init_patched_inputs method of diffusers.models.Transformer2DModel " |
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"using scripts.diffusers_patches.pixart_sigma_init_patched_inputs") |
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setattr(Transformer2DModel, '_init_patched_inputs', pixart_sigma_init_patched_inputs) |
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transformer = Transformer2DModel.from_pretrained( |
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"PixArt-alpha/PixArt-Sigma-XL-2-1024-MS", |
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subfolder='transformer', |
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torch_dtype=weight_dtype, |
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) |
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pipe = PixArtSigmaPipeline.from_pretrained( |
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"PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers", |
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transformer=transformer, |
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torch_dtype=weight_dtype, |
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use_safetensors=True, |
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) |
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if os.getenv('CONSISTENCY_DECODER', False): |
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print("Using DALL-E 3 Consistency Decoder") |
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pipe.vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder", torch_dtype=torch.float16) |
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if ENABLE_CPU_OFFLOAD: |
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pipe.enable_model_cpu_offload() |
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else: |
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pipe.to(device) |
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print("Loaded on Device!") |
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pipe.text_encoder.to_bettertransformer() |
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if USE_TORCH_COMPILE: |
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pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=True) |
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print("Model Compiled!") |
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def save_image(img): |
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unique_name = str(uuid.uuid4()) + ".png" |
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img.save(unique_name) |
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return unique_name |
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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return seed |
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@torch.no_grad() |
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@torch.inference_mode() |
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@spaces.GPU(duration=120) |
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def generate( |
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prompt: str, |
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negative_prompt: str = "", |
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style: str = DEFAULT_STYLE_NAME, |
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use_negative_prompt: bool = False, |
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num_imgs: int = 1, |
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seed: int = 0, |
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width: int = 400, |
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height: int = 400, |
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schedule: str = 'DPM-Solver', |
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dpms_guidance_scale: float = 3.5, |
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sas_guidance_scale: float = 3, |
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dpms_inference_steps: int = 9, |
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sas_inference_steps: int = 25, |
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randomize_seed: bool = False, |
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use_resolution_binning: bool = True, |
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progress=gr.Progress(track_tqdm=True), |
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): |
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seed = int(randomize_seed_fn(seed, randomize_seed)) |
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generator = torch.Generator().manual_seed(seed) |
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if schedule == 'DPM-Solver': |
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if not isinstance(pipe.scheduler, DPMSolverMultistepScheduler): |
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pipe.scheduler = DPMSolverMultistepScheduler() |
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num_inference_steps = dpms_inference_steps |
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guidance_scale = dpms_guidance_scale |
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elif schedule == "SA-Solver": |
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if not isinstance(pipe.scheduler, SASolverScheduler): |
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pipe.scheduler = SASolverScheduler.from_config(pipe.scheduler.config, algorithm_type='data_prediction', tau_func=lambda t: 1 if 200 <= t <= 800 else 0, predictor_order=2, corrector_order=2) |
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num_inference_steps = sas_inference_steps |
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guidance_scale = sas_guidance_scale |
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else: |
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raise ValueError(f"Unknown schedule: {schedule}") |
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if not use_negative_prompt: |
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negative_prompt = None |
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prompt, negative_prompt = apply_style(style, prompt, negative_prompt) |
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images = pipe( |
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prompt=prompt, |
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width=width, |
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height=height, |
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negative_prompt=negative_prompt, |
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guidance_scale=guidance_scale, |
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num_inference_steps=num_inference_steps, |
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generator=generator, |
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num_images_per_prompt=num_imgs, |
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use_resolution_binning=use_resolution_binning, |
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output_type="pil", |
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max_sequence_length=T5_token_max_length, |
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).images |
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image_paths = [save_image(img) for img in images] |
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print(image_paths) |
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return image_paths, seed |
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examples = [ |
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"A Monkey with a happy face in the Sahara desert.", |
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"Eiffel Tower was Made up of ICE to look like a cloud, with the bell tower at the top of the building.", |
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"3D small, round, fluffy creature with big, expressive eyes explores a vibrant, enchanted forest. The creature, a whimsical blend of a rabbit and a squirrel, has soft blue fur and a bushy, striped tail. It hops along a sparkling stream, its eyes wide with wonder. The forest is alive with magical elements: flowers that glow and change colors, trees with leaves in shades of purple and silver, and small floating lights that resemble fireflies. The creature stops to interact playfully with a group of tiny, fairy-like beings dancing around a mushroom ring. The creature looks up in awe at a large, glowing tree that seems to be the heart of the forest.", |
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"Color photo of a corgi made of transparent glass, standing on the riverside in Yosemite National Park.", |
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"A close-up photo of a woman. She wore a blue coat with a gray dress underneath. She has blue eyes and blond hair, and wears a pair of earrings. Behind are blurred city buildings and streets.", |
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"A litter of golden retriever puppies playing in the snow. Their heads pop out of the snow, covered in.", |
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"a handsome young boy in the middle with sky color background wearing eye glasses, it's super detailed with anime style, it's a portrait with delicated eyes and nice looking face", |
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"an astronaut sitting in a diner, eating fries, cinematic, analog film", |
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"Pirate ship trapped in a cosmic maelstrom nebula, rendered in cosmic beach whirlpool engine, volumetric lighting, spectacular, ambient lights, intricate detail.", |
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"professional portrait photo of an anthropomorphic cat wearing fancy gentleman hat and jacket walking in autumn forest.", |
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"Outside View from Hotel Made up of Chocolate in space.", |
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] |
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with gr.Blocks(css="style.css") as demo: |
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gr.Markdown(DESCRIPTION) |
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gr.DuplicateButton( |
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value="Duplicate Space for private use", |
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elem_id="duplicate-button", |
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visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", |
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) |
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with gr.Row(equal_height=False): |
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with gr.Group(): |
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with gr.Row(): |
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prompt = gr.Text( |
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label="Prompt", |
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show_label=False, |
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max_lines=1, |
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placeholder="Enter your prompt", |
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container=False, |
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) |
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run_button = gr.Button("Run", scale=0) |
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result = gr.Gallery(label="Result", columns=NUM_IMAGES_PER_PROMPT, show_label=False) |
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with gr.Group(): |
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with gr.Row(): |
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use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False, visible=True) |
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num_imgs = gr.Slider( |
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label="Num Images", |
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minimum=1, |
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maximum=8, |
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step=1, |
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value=1, |
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) |
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style_selection = gr.Radio( |
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show_label=True, |
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container=True, |
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interactive=True, |
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choices=STYLE_NAMES, |
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value=DEFAULT_STYLE_NAME, |
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label="Image Style", |
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) |
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negative_prompt = gr.Text( |
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label="Negative prompt", |
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max_lines=1, |
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placeholder="Enter a negative prompt", |
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visible=True, |
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) |
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seed = gr.Slider( |
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label="Seed", |
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minimum=0, |
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maximum=MAX_SEED, |
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step=1, |
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value=0, |
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) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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with gr.Row(visible=True): |
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width = gr.Slider( |
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label="Width", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=32, |
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value=400, |
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) |
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height = gr.Slider( |
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label="Height", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=32, |
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value=400, |
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) |
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with gr.Row(): |
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dpms_guidance_scale = gr.Slider( |
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label="Temprature", |
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minimum=3, |
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maximum=4, |
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step=0.1, |
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value=3.5, |
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) |
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dpms_inference_steps = gr.Slider( |
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label="Steps", |
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minimum=5, |
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maximum=25, |
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step=1, |
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value=9, |
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) |
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gr.Examples( |
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examples=examples, |
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inputs=prompt, |
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outputs=[result, seed], |
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fn=generate, |
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cache_examples=CACHE_EXAMPLES, |
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) |
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use_negative_prompt.change( |
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fn=lambda x: gr.update(visible=x), |
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inputs=use_negative_prompt, |
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outputs=negative_prompt, |
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api_name=False, |
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) |
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gr.on( |
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triggers=[ |
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prompt.submit, |
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negative_prompt.submit, |
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run_button.click, |
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], |
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fn=generate, |
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inputs=[ |
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prompt, |
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negative_prompt, |
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style_selection, |
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use_negative_prompt, |
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num_imgs, |
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seed, |
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width, |
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height, |
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schedule, |
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dpms_guidance_scale, |
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sas_guidance_scale, |
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dpms_inference_steps, |
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sas_inference_steps, |
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randomize_seed, |
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], |
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outputs=[result, seed], |
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api_name="run", |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=20).launch() |
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