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import os |
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import random |
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import zipfile |
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import findfile |
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import PIL.Image |
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import autocuda |
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from pyabsa.utils.pyabsa_utils import fprint |
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try: |
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for z_file in findfile.find_cwd_files(and_key=['.zip'], |
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exclude_key=['.ignore', 'git', 'SuperResolutionAnimeDiffusion'], |
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recursive=10): |
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fprint(f"Extracting {z_file}...") |
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with zipfile.ZipFile(z_file, 'r') as zip_ref: |
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zip_ref.extractall(os.path.dirname(z_file)) |
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except Exception as e: |
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os.system('unzip random_examples.zip') |
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|
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from diffusers import ( |
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AutoencoderKL, |
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UNet2DConditionModel, |
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StableDiffusionPipeline, |
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StableDiffusionImg2ImgPipeline, |
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DPMSolverMultistepScheduler, |
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) |
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import gradio as gr |
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import torch |
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from PIL import Image |
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import utils |
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import datetime |
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import time |
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import psutil |
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from Waifu2x.magnify import ImageMagnifier |
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from RealESRGANv030.interface import realEsrgan |
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magnifier = ImageMagnifier() |
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start_time = time.time() |
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is_colab = utils.is_google_colab() |
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CUDA_VISIBLE_DEVICES = "" |
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device = autocuda.auto_cuda() |
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dtype = torch.float16 if device != "cpu" else torch.float32 |
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class Model: |
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def __init__(self, name, path="", prefix=""): |
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self.name = name |
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self.path = path |
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self.prefix = prefix |
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self.pipe_t2i = None |
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self.pipe_i2i = None |
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models = [ |
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Model("anything v5", "stablediffusionapi/anything-v5", "anything v5 style"), |
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] |
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scheduler = DPMSolverMultistepScheduler( |
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beta_start=0.00085, |
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beta_end=0.012, |
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beta_schedule="scaled_linear", |
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num_train_timesteps=1000, |
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trained_betas=None, |
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predict_epsilon=True, |
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thresholding=False, |
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algorithm_type="dpmsolver++", |
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solver_type="midpoint", |
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solver_order=2, |
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|
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) |
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custom_model = None |
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if is_colab: |
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models.insert(0, Model("Custom model")) |
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custom_model = models[0] |
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last_mode = "txt2img" |
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current_model = models[1] if is_colab else models[0] |
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current_model_path = current_model.path |
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|
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if is_colab: |
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pipe = StableDiffusionPipeline.from_pretrained( |
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current_model.path, |
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torch_dtype=dtype, |
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scheduler=scheduler, |
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safety_checker=lambda images, clip_input: (images, False), |
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) |
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else: |
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print(f"{datetime.datetime.now()} Downloading vae...") |
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vae = AutoencoderKL.from_pretrained( |
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current_model.path, subfolder="vae", torch_dtype=dtype |
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) |
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for model in models: |
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try: |
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print(f"{datetime.datetime.now()} Downloading {model.name} model...") |
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unet = UNet2DConditionModel.from_pretrained( |
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model.path, subfolder="unet", torch_dtype=dtype |
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) |
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model.pipe_t2i = StableDiffusionPipeline.from_pretrained( |
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model.path, |
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unet=unet, |
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vae=vae, |
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torch_dtype=dtype, |
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scheduler=scheduler, |
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safety_checker=None, |
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) |
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model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained( |
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model.path, |
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unet=unet, |
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vae=vae, |
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torch_dtype=dtype, |
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scheduler=scheduler, |
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safety_checker=None, |
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) |
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except Exception as e: |
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print( |
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f"{datetime.datetime.now()} Failed to load model " |
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+ model.name |
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+ ": " |
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+ str(e) |
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) |
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models.remove(model) |
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pipe = models[0].pipe_t2i |
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if torch.cuda.is_available(): |
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pipe = pipe.to(device) |
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def error_str(error, title="Error"): |
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return ( |
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f"""#### {title} |
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{error}""" |
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if error |
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else "" |
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) |
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def custom_model_changed(path): |
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models[0].path = path |
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global current_model |
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current_model = models[0] |
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|
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def on_model_change(model_name): |
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prefix = ( |
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'Enter prompt. "' |
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+ next((m.prefix for m in models if m.name == model_name), None) |
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+ '" is prefixed automatically' |
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if model_name != models[0].name |
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else "Don't forget to use the custom model prefix in the prompt!" |
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) |
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return ( |
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gr.update(visible=model_name == models[0].name), |
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gr.update(placeholder=prefix), |
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) |
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|
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def inference( |
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model_name, |
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prompt, |
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guidance, |
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steps, |
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width=512, |
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height=512, |
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seed=0, |
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img=None, |
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strength=0.5, |
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neg_prompt="", |
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scale="ESRGAN4x", |
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scale_factor=2, |
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): |
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fprint(psutil.virtual_memory()) |
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|
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fprint(f"Prompt: {prompt}") |
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global current_model |
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for model in models: |
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if model.name == model_name: |
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current_model = model |
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model_path = current_model.path |
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generator = torch.Generator(device).manual_seed(seed) if seed != 0 else None |
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try: |
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if img is not None: |
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return ( |
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img_to_img( |
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model_path, |
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prompt, |
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neg_prompt, |
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img, |
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strength, |
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guidance, |
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steps, |
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width, |
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height, |
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generator, |
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scale, |
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scale_factor, |
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), |
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None, |
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) |
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else: |
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return ( |
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txt_to_img( |
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model_path, |
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prompt, |
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neg_prompt, |
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guidance, |
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steps, |
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width, |
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height, |
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generator, |
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scale, |
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scale_factor, |
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), |
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None, |
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) |
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except Exception as e: |
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return None, error_str(e) |
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def txt_to_img( |
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model_path, |
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prompt, |
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neg_prompt, |
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guidance, |
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steps, |
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width, |
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height, |
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generator, |
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scale, |
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scale_factor, |
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): |
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print(f"{datetime.datetime.now()} txt_to_img, model: {current_model.name}") |
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global last_mode |
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global pipe |
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global current_model_path |
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if model_path != current_model_path or last_mode != "txt2img": |
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current_model_path = model_path |
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if is_colab or current_model == custom_model: |
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pipe = StableDiffusionPipeline.from_pretrained( |
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current_model_path, |
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torch_dtype=dtype, |
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scheduler=scheduler, |
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safety_checker=lambda images, clip_input: (images, False), |
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) |
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else: |
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pipe = current_model.pipe_t2i |
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|
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if torch.cuda.is_available(): |
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pipe = pipe.to(device) |
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last_mode = "txt2img" |
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|
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prompt = current_model.prefix + prompt |
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result = pipe( |
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prompt, |
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negative_prompt=neg_prompt, |
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num_inference_steps=int(steps), |
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guidance_scale=guidance, |
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width=width, |
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height=height, |
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generator=generator, |
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) |
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if scale_factor > 1: |
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if scale == "ESRGAN4x": |
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fp32 = True if device == "cpu" else False |
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result.images[0] = realEsrgan( |
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input_dir=result.images[0], |
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suffix="", |
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output_dir="imgs", |
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fp32=fp32, |
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outscale=scale_factor, |
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)[0] |
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else: |
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result.images[0] = magnifier.magnify( |
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result.images[0], scale_factor=scale_factor |
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) |
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result.images[0].save( |
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"imgs/result-{}.png".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S")) |
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) |
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return replace_nsfw_images(result) |
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def img_to_img( |
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model_path, |
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prompt, |
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neg_prompt, |
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img, |
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strength, |
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guidance, |
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steps, |
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width, |
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height, |
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generator, |
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scale, |
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scale_factor, |
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): |
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fprint(f"{datetime.datetime.now()} img_to_img, model: {model_path}") |
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global last_mode |
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global pipe |
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global current_model_path |
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if model_path != current_model_path or last_mode != "img2img": |
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current_model_path = model_path |
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if is_colab or current_model == custom_model: |
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained( |
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current_model_path, |
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torch_dtype=dtype, |
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scheduler=scheduler, |
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safety_checker=lambda images, clip_input: (images, False), |
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) |
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else: |
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pipe = current_model.pipe_i2i |
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|
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if torch.cuda.is_available(): |
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pipe = pipe.to(device) |
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last_mode = "img2img" |
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prompt = current_model.prefix + prompt |
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ratio = min(height / img.height, width / img.width) |
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img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS) |
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result = pipe( |
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prompt, |
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negative_prompt=neg_prompt, |
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image=img, |
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num_inference_steps=int(steps), |
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strength=strength, |
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guidance_scale=guidance, |
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generator=generator, |
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) |
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if scale_factor > 1: |
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if scale == "ESRGAN4x": |
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fp32 = True if device == "cpu" else False |
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result.images[0] = realEsrgan( |
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input_dir=result.images[0], |
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suffix="", |
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output_dir="imgs", |
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fp32=fp32, |
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outscale=scale_factor, |
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)[0] |
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else: |
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result.images[0] = magnifier.magnify( |
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result.images[0], scale_factor=scale_factor |
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) |
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result.images[0].save( |
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"imgs/result-{}.png".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S")) |
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) |
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return replace_nsfw_images(result) |
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def replace_nsfw_images(results): |
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if is_colab: |
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return results.images[0] |
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if hasattr(results, "nsfw_content_detected") and results.nsfw_content_detected: |
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for i in range(len(results.images)): |
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if results.nsfw_content_detected[i]: |
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results.images[i] = Image.open("nsfw.png") |
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return results.images[0] |
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css = """.finetuned-diffusion-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.finetuned-diffusion-div div h1{font-weight:900;margin-bottom:7px}.finetuned-diffusion-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem} |
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""" |
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with gr.Blocks(css=css) as demo: |
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if not os.path.exists("imgs"): |
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os.mkdir("imgs") |
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|
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gr.Markdown("# Super Resolution Anime Diffusion") |
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gr.Markdown( |
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"## Author: [yangheng95](https://github.com/yangheng95) Github:[Github](https://github.com/yangheng95/stable-diffusion-webui)" |
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) |
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gr.Markdown( |
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"### This demo is running on a CPU, so it will take at least 20 minutes. " |
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"If you have a GPU, you can clone from [Github](https://github.com/yangheng95/SuperResolutionAnimeDiffusion) and run it locally." |
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) |
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gr.Markdown( |
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"### FYI: to generate a 512*512 image and magnify 4x, it only takes 5~8 seconds on a RTX 2080 GPU" |
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) |
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gr.Markdown( |
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"### You can duplicate this demo on HuggingFace Spaces, click [here](https://huggingface.co/spaces/yangheng/Super-Resolution-Anime-Diffusion?duplicate=true)" |
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) |
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|
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with gr.Row(): |
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with gr.Column(scale=55): |
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with gr.Group(): |
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gr.Markdown("Text to image") |
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|
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model_name = gr.Dropdown( |
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label="Model", |
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choices=[m.name for m in models], |
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value=current_model.name, |
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) |
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|
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with gr.Box(visible=False) as custom_model_group: |
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custom_model_path = gr.Textbox( |
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label="Custom model path", |
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placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", |
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interactive=True, |
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) |
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gr.HTML( |
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"<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>" |
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) |
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|
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with gr.Row(): |
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prompt = gr.Textbox( |
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label="Prompt", |
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show_label=False, |
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max_lines=2, |
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placeholder="Enter prompt. Style applied automatically", |
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).style(container=False) |
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with gr.Row(): |
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generate = gr.Button(value="Generate") |
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|
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with gr.Row(): |
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with gr.Group(): |
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neg_prompt = gr.Textbox( |
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label="Negative prompt", |
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value="bad result, worst, random, invalid, inaccurate, imperfect, blurry, deformed," |
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" disfigured, mutation, mutated, ugly, out of focus, bad anatomy, text, error," |
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" extra digit, fewer digits, worst quality, low quality, normal quality, noise, " |
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"jpeg artifact, compression artifact, signature, watermark, username, logo, " |
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"low resolution, worst resolution, bad resolution, normal resolution, bad detail," |
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" bad details, bad lighting, bad shadow, bad shading, bad background," |
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" worst background.", |
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) |
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|
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image_out = gr.Image(height="auto", width="auto") |
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error_output = gr.Markdown() |
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|
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with gr.Row(): |
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gr.Markdown( |
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"# Random Image Generation Preview (512*768)x4 magnified" |
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) |
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for f_img in findfile.find_cwd_files(".png", recursive=2): |
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with gr.Row(): |
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image = gr.Image(height=512, value=PIL.Image.open(f_img)) |
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|
|
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|
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with gr.Column(scale=45): |
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with gr.Group(): |
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gr.Markdown("Image to Image") |
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|
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with gr.Row(): |
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with gr.Group(): |
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image = gr.Image( |
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label="Image", height=256, tool="editor", type="pil" |
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) |
|
strength = gr.Slider( |
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label="Transformation strength", |
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minimum=0, |
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maximum=1, |
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step=0.01, |
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value=0.5, |
|
) |
|
|
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with gr.Row(): |
|
with gr.Group(): |
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|
|
|
|
with gr.Row(): |
|
guidance = gr.Slider( |
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label="Guidance scale", value=7.5, maximum=15 |
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) |
|
steps = gr.Slider( |
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label="Steps", value=15, minimum=2, maximum=75, step=1 |
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) |
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|
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with gr.Row(): |
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width = gr.Slider( |
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label="Width", |
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value=512, |
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minimum=64, |
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maximum=1024, |
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step=8, |
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) |
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height = gr.Slider( |
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label="Height", |
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value=768, |
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minimum=64, |
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maximum=1024, |
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step=8, |
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) |
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with gr.Row(): |
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scale = gr.Radio( |
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label="Scale", |
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choices=["Waifu2x", "ESRGAN4x"], |
|
value="Waifu2x", |
|
) |
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with gr.Row(): |
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scale_factor = gr.Slider( |
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1, |
|
8, |
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label="Scale factor (to magnify image) (1, 2, 4, 8)", |
|
value=1, |
|
step=1, |
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) |
|
|
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seed = gr.Slider( |
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0, 2147483647, label="Seed (0 = random)", value=0, step=1 |
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) |
|
|
|
if is_colab: |
|
model_name.change( |
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on_model_change, |
|
inputs=model_name, |
|
outputs=[custom_model_group, prompt], |
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queue=False, |
|
) |
|
custom_model_path.change( |
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custom_model_changed, inputs=custom_model_path, outputs=None |
|
) |
|
|
|
|
|
gr.Markdown( |
|
"### based on [Anything V5]" |
|
) |
|
|
|
inputs = [ |
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model_name, |
|
prompt, |
|
guidance, |
|
steps, |
|
width, |
|
height, |
|
seed, |
|
image, |
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strength, |
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neg_prompt, |
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scale, |
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scale_factor, |
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] |
|
outputs = [image_out, error_output] |
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prompt.submit(inference, inputs=inputs, outputs=outputs) |
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generate.click(inference, inputs=inputs, outputs=outputs, api_name="generate") |
|
|
|
prompt_keys = [ |
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"girl", |
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"lovely", |
|
"cute", |
|
"beautiful eyes", |
|
"cumulonimbus clouds", |
|
random.choice(["dress"]), |
|
random.choice(["white hair"]), |
|
random.choice(["blue eyes"]), |
|
random.choice(["flower meadow"]), |
|
random.choice(["Elif", "Angel"]), |
|
] |
|
prompt.value = ",".join(prompt_keys) |
|
ex = gr.Examples( |
|
[ |
|
[models[0].name, prompt.value, 7.5, 15], |
|
], |
|
inputs=[model_name, prompt, guidance, steps, seed], |
|
outputs=outputs, |
|
fn=inference, |
|
cache_examples=False, |
|
) |
|
|
|
print(f"Space built in {time.time() - start_time:.2f} seconds") |
|
|
|
if not is_colab: |
|
demo.queue(concurrency_count=2) |
|
demo.launch(debug=is_colab, enable_queue=True, share=is_colab) |
