import random import gradio as gr from datasets import load_dataset from PIL import Image # from model import get_sd_small, get_sd_tiny, get_sd_every from trans_google import google_translator import replicate from i18n import i18nTranslator word_list_dataset = load_dataset("Gustavosta/Stable-Diffusion-Prompts") word_list = word_list_dataset["train"]['Prompt'] # # from diffusers import EulerDiscreteScheduler, DDIMScheduler, KDPM2AncestralDiscreteScheduler, \ # UniPCMultistepScheduler, DPMSolverSinglestepScheduler, DEISMultistepScheduler, PNDMScheduler, \ # DPMSolverMultistepScheduler, HeunDiscreteScheduler, EulerAncestralDiscreteScheduler, DDPMScheduler, \ # LMSDiscreteScheduler, KDPM2DiscreteScheduler # import torch # import base64 # from io import BytesIO is_gpu_busy = False # translator = i18nTranslator() # translator.init(path='locales') samplers = [ "EulerDiscrete", "EulerAncestralDiscrete", "UniPCMultistep", "DPMSolverSinglestep", "DPMSolverMultistep", "KDPM2Discrete", "KDPM2AncestralDiscrete", "DEISMultistep", "HeunDiscrete", "PNDM", "DDPM", "DDIM", "LMSDiscrete", ] re_sampler = [ "DDIM", "K_EULER", "DPMSolverMultistep", "K_EULER_ANCESTRAL", "PNDM", "KLMS" ] rand = random.Random() translator = google_translator() # tiny_pipe = get_sd_tiny() # small_pipe = get_sd_small() # every_pipe = get_sd_every() # def get_pipe(width: int, height: int): # if width == 512 and height == 512: # return tiny_pipe # elif width == 256 and height == 256: # return small_pipe # else: # return every_pipe def infer(prompt: str, negative: str, width: int, height: int, sampler: str, steps: int, seed: int, scale): global is_gpu_busy if seed == 0: seed = rand.randint(0, 10000) else: seed = int(seed) # # pipeline = get_pipe(width, height) # images = [] # if torch.cuda.is_available(): # generator = torch.Generator(device="cuda").manual_seed(seed) # else: # generator = None # if sampler == "EulerDiscrete": # pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config) # elif sampler == "EulerAncestralDiscrete": # pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config) # elif sampler == "KDPM2Discrete": # pipeline.scheduler = KDPM2DiscreteScheduler.from_config(pipeline.scheduler.config) # elif sampler == "KDPM2AncestralDiscrete": # pipeline.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipeline.scheduler.config) # elif sampler == "UniPCMultistep": # pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config) # elif sampler == "DPMSolverSinglestep": # pipeline.scheduler = DPMSolverSinglestepScheduler.from_config(pipeline.scheduler.config) # elif sampler == "DPMSolverMultistep": # pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config) # elif sampler == "HeunDiscrete": # pipeline.scheduler = HeunDiscreteScheduler.from_config(pipeline.scheduler.config) # elif sampler == "DEISMultistep": # pipeline.scheduler = DEISMultistepScheduler.from_config(pipeline.scheduler.config) # elif sampler == "PNDM": # pipeline.scheduler = PNDMScheduler.from_config(pipeline.scheduler.config) # elif sampler == "DDPM": # pipeline.scheduler = DDPMScheduler.from_config(pipeline.scheduler.config) # elif sampler == "DDIM": # pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config) # elif sampler == "LMSDiscrete": # pipeline.scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config) try: translate_prompt = translator.translate(prompt, lang_tgt='en') translate_negative = translator.translate(negative, lang_tgt='en') except Exception as ex: print(ex) translate_prompt = prompt translate_negative = negative output = replicate.run( "stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf", input={ "prompt": translate_prompt, "negative_prompt": translate_negative, "guidance_scale": scale, "num_inference_steps": steps, "seed": seed, "scheduler": sampler, } ) # image = pipeline(prompt=translate_prompt, # negative_prompt=translate_negative, # guidance_scale=scale, # num_inference_steps=steps, # generator=generator, # height=height, # width=width).images[0] # buffered = BytesIO() # image.save(buffered, format="JPEG") # img_str = base64.b64encode(buffered.getvalue()) # img_base64 = bytes("data:image/jpeg;base64,", encoding='utf-8') + img_str images.append(output[0]) return images css = """ .gradio-container { font-family: 'IBM Plex Sans', sans-serif; } .gr-button { color: white; border-color: black; background: black; } input[type='range'] { accent-color: black; } .dark input[type='range'] { accent-color: #dfdfdf; } .container { max-width: 1130px; margin: auto; padding-top: 1.5rem; } #prompt-column { min-height: 500px } #gallery { min-height: 22rem; margin-bottom: 15px; margin-left: auto; margin-right: auto; border-bottom-right-radius: .5rem !important; border-bottom-left-radius: .5rem !important; } #gallery>div>.h-full { min-height: 20rem; } .details:hover { text-decoration: underline; } .gr-button { white-space: nowrap; } .gr-button:focus { border-color: rgb(147 197 253 / var(--tw-border-opacity)); outline: none; box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); --tw-border-opacity: 1; --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity)); --tw-ring-opacity: .5; } #advanced-btn { font-size: .7rem !important; line-height: 19px; margin-top: 12px; margin-bottom: 12px; padding: 2px 8px; border-radius: 14px !important; } #advanced-options { display: none; margin-bottom: 20px; } .footer { margin-bottom: 45px; margin-top: 35px; text-align: center; border-bottom: 1px solid #e5e5e5; } .footer>p { font-size: .8rem; display: inline-block; padding: 0 10px; transform: translateY(10px); background: white; } .dark .footer { border-color: #303030; } .dark .footer>p { background: #0b0f19; } .acknowledgments h4{ margin: 1.25em 0 .25em 0; font-weight: bold; font-size: 115%; } .animate-spin { animation: spin 1s linear infinite; } @keyframes spin { from { transform: rotate(0deg); } to { transform: rotate(360deg); } } #share-btn-container { display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem; margin-top: 10px; margin-left: auto; } #share-btn { all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0; } #share-btn * { all: unset; } #share-btn-container div:nth-child(-n+2){ width: auto !important; min-height: 0px !important; } #share-btn-container .wrap { display: none !important; } .gr-form{ flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0; } #prompt-container{ gap: 0; } #prompt-text-input, #negative-prompt-text-input{padding: .45rem 0.625rem} #component-16{border-top-width: 1px!important;margin-top: 1em} .image_duplication{position: absolute; width: 100px; left: 50px} .generate-container {display: flex; justify-content: flex-end;} #generate-btn {background: linear-gradient(to bottom right, #ffedd5, #fdba74)} """ block = gr.Blocks(css=css) # text, negative, width, height, sampler, steps, seed, guidance_scale # examples = [ # [ # 'A high tech solarpunk utopia in the Amazon rainforest', # 'low quality', # 512, # 512, # 'ddim', # 30, # 0, # 9 # ], # [ # 'A pikachu fine dining with a view to the Eiffel Tower', # 'low quality', # 512, # 512, # 'ddim', # 30, # 0, # 9 # ], # [ # 'A mecha robot in a favela in expressionist style', # 'low quality, 3d, photorealistic', # 512, # 512, # 'ddim', # 30, # 0, # 9 # ], # [ # 'an insect robot preparing a delicious meal', # 'low quality, illustration', # 512, # 512, # 'ddim', # 30, # 0, # 9 # ], # [ # "A small cabin on top of a snowy mountain in the style of Disney, artstation", # 'low quality, ugly', # 512, # 512, # 'ddim', # 30, # 0, # 9 # ], # ] examples = list(map(lambda x: [ x, 'low quality', 512, 512, 'ddim', 30, 0, 9 ], word_list))[:500] with block: gr.HTML( """
small stable diffusion Demo App.
Click Generate image Button to generate image.
Also Change params to have a try
more size may cost more time.
It's just a simplified demo, you can use more advanced features optimize image quality