import io import base64 import os import sys import numpy as np import torch from torch import autocast import diffusers from diffusers.configuration_utils import FrozenDict from diffusers import ( StableDiffusionPipeline, StableDiffusionInpaintPipeline, StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipelineLegacy, DDIMScheduler, LMSDiscreteScheduler, ) from PIL import Image from PIL import ImageOps import gradio as gr import base64 import skimage import skimage.measure import yaml import json from enum import Enum try: abspath = os.path.abspath(__file__) dirname = os.path.dirname(abspath) os.chdir(dirname) except: pass from utils import * assert diffusers.__version__ >= "0.6.0", "Please upgrade diffusers to 0.6.0" USE_NEW_DIFFUSERS = True RUN_IN_SPACE = "RUN_IN_HG_SPACE" in os.environ class ModelChoice(Enum): INPAINTING = "stablediffusion-inpainting" INPAINTING_IMG2IMG = "stablediffusion-inpainting+img2img-v1.5" MODEL_1_5 = "stablediffusion-v1.5" MODEL_1_4 = "stablediffusion-v1.4" try: from sd_grpcserver.pipeline.unified_pipeline import UnifiedPipeline except: UnifiedPipeline = StableDiffusionInpaintPipeline # sys.path.append("./glid_3_xl_stable") USE_GLID = False # try: # from glid3xlmodel import GlidModel # except: # USE_GLID = False try: cuda_available = torch.cuda.is_available() except: cuda_available = False finally: if sys.platform == "darwin": device = "mps" if torch.backends.mps.is_available() else "cpu" elif cuda_available: device = "cuda" else: device = "cpu" if device != "cuda": import contextlib autocast = contextlib.nullcontext with open("config.yaml", "r") as yaml_in: yaml_object = yaml.safe_load(yaml_in) config_json = json.dumps(yaml_object) def load_html(): body, canvaspy = "", "" with open("index.html", encoding="utf8") as f: body = f.read() with open("canvas.py", encoding="utf8") as f: canvaspy = f.read() body = body.replace("- paths:\n", "") body = body.replace(" - ./canvas.py\n", "") body = body.replace("from canvas import InfCanvas", canvaspy) return body def test(x): x = load_html() return f"""""" DEBUG_MODE = False try: SAMPLING_MODE = Image.Resampling.LANCZOS except Exception as e: SAMPLING_MODE = Image.LANCZOS try: contain_func = ImageOps.contain except Exception as e: def contain_func(image, size, method=SAMPLING_MODE): # from PIL: https://pillow.readthedocs.io/en/stable/reference/ImageOps.html#PIL.ImageOps.contain im_ratio = image.width / image.height dest_ratio = size[0] / size[1] if im_ratio != dest_ratio: if im_ratio > dest_ratio: new_height = int(image.height / image.width * size[0]) if new_height != size[1]: size = (size[0], new_height) else: new_width = int(image.width / image.height * size[1]) if new_width != size[0]: size = (new_width, size[1]) return image.resize(size, resample=method) import argparse parser = argparse.ArgumentParser(description="stablediffusion-infinity") parser.add_argument("--port", type=int, help="listen port", dest="server_port") parser.add_argument("--host", type=str, help="host", dest="server_name") parser.add_argument("--share", action="store_true", help="share this app?") parser.add_argument("--debug", action="store_true", help="debug mode") parser.add_argument("--fp32", action="store_true", help="using full precision") parser.add_argument("--encrypt", action="store_true", help="using https?") parser.add_argument("--ssl_keyfile", type=str, help="path to ssl_keyfile") parser.add_argument("--ssl_certfile", type=str, help="path to ssl_certfile") parser.add_argument("--ssl_keyfile_password", type=str, help="ssl_keyfile_password") parser.add_argument( "--auth", nargs=2, metavar=("username", "password"), help="use username password" ) parser.add_argument( "--remote_model", type=str, help="use a model (e.g. dreambooth fined) from huggingface hub", default="", ) parser.add_argument( "--local_model", type=str, help="use a model stored on your PC", default="" ) if __name__ == "__main__": args = parser.parse_args() else: args = parser.parse_args(["--debug"]) # args = parser.parse_args(["--debug"]) if args.auth is not None: args.auth = tuple(args.auth) model = {} def get_token(): token = "" if os.path.exists(".token"): with open(".token", "r") as f: token = f.read() token = os.environ.get("hftoken", token) return token def save_token(token): with open(".token", "w") as f: f.write(token) def prepare_scheduler(scheduler): if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: new_config = dict(scheduler.config) new_config["steps_offset"] = 1 scheduler._internal_dict = FrozenDict(new_config) return scheduler def my_resize(width, height): if width >= 512 and height >= 512: return width, height if width == height: return 512, 512 smaller = min(width, height) larger = max(width, height) if larger >= 608: return width, height factor = 1 if smaller < 290: factor = 2 elif smaller < 330: factor = 1.75 elif smaller < 384: factor = 1.375 elif smaller < 400: factor = 1.25 elif smaller < 450: factor = 1.125 return int(factor * width)//8*8, int(factor * height)//8*8 def load_learned_embed_in_clip( learned_embeds_path, text_encoder, tokenizer, token=None ): # https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu") # separate token and the embeds trained_token = list(loaded_learned_embeds.keys())[0] embeds = loaded_learned_embeds[trained_token] # cast to dtype of text_encoder dtype = text_encoder.get_input_embeddings().weight.dtype embeds.to(dtype) # add the token in tokenizer token = token if token is not None else trained_token num_added_tokens = tokenizer.add_tokens(token) if num_added_tokens == 0: raise ValueError( f"The tokenizer already contains the token {token}. Please pass a different `token` that is not already in the tokenizer." ) # resize the token embeddings text_encoder.resize_token_embeddings(len(tokenizer)) # get the id for the token and assign the embeds token_id = tokenizer.convert_tokens_to_ids(token) text_encoder.get_input_embeddings().weight.data[token_id] = embeds scheduler_dict = {"PLMS": None, "DDIM": None, "K-LMS": None} class StableDiffusionInpaint: def __init__( self, token: str = "", model_name: str = "", model_path: str = "", **kwargs, ): self.token = token original_checkpoint = False if model_path and os.path.exists(model_path): if model_path.endswith(".ckpt"): original_checkpoint = True elif model_path.endswith(".json"): model_name = os.path.dirname(model_path) else: model_name = model_path if original_checkpoint: print(f"Converting & Loading {model_path}") from convert_checkpoint import convert_checkpoint pipe = convert_checkpoint(model_path, inpainting=True) if device == "cuda" and not args.fp32: pipe.to(torch.float16) inpaint = StableDiffusionInpaintPipeline( vae=pipe.vae, text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer, unet=pipe.unet, scheduler=pipe.scheduler, safety_checker=pipe.safety_checker, feature_extractor=pipe.feature_extractor, ) else: print(f"Loading {model_name}") if device == "cuda" and not args.fp32: inpaint = StableDiffusionInpaintPipeline.from_pretrained( model_name, revision="fp16", torch_dtype=torch.float16, use_auth_token=token, ) else: inpaint = StableDiffusionInpaintPipeline.from_pretrained( model_name, use_auth_token=token, ) if os.path.exists("./embeddings"): print("Note that StableDiffusionInpaintPipeline + embeddings is untested") for item in os.listdir("./embeddings"): if item.endswith(".bin"): load_learned_embed_in_clip( os.path.join("./embeddings", item), inpaint.text_encoder, inpaint.tokenizer, ) inpaint.to(device) # if device == "mps": # _ = text2img("", num_inference_steps=1) scheduler_dict["PLMS"] = inpaint.scheduler scheduler_dict["DDIM"] = prepare_scheduler( DDIMScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, ) ) scheduler_dict["K-LMS"] = prepare_scheduler( LMSDiscreteScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear" ) ) self.safety_checker = inpaint.safety_checker save_token(token) try: total_memory = torch.cuda.get_device_properties(0).total_memory // ( 1024 ** 3 ) if total_memory <= 5: inpaint.enable_attention_slicing() except: pass self.inpaint = inpaint def run( self, image_pil, prompt="", negative_prompt="", guidance_scale=7.5, resize_check=True, enable_safety=True, fill_mode="patchmatch", strength=0.75, step=50, enable_img2img=False, use_seed=False, seed_val=-1, generate_num=1, scheduler="", scheduler_eta=0.0, **kwargs, ): inpaint = self.inpaint selected_scheduler = scheduler_dict.get(scheduler, scheduler_dict["PLMS"]) for item in [inpaint]: item.scheduler = selected_scheduler if enable_safety: item.safety_checker = self.safety_checker else: item.safety_checker = lambda images, **kwargs: (images, False) width, height = image_pil.size sel_buffer = np.array(image_pil) img = sel_buffer[:, :, 0:3] mask = sel_buffer[:, :, -1] nmask = 255 - mask process_width = width process_height = height if resize_check: process_width, process_height = my_resize(width, height) extra_kwargs = { "num_inference_steps": step, "guidance_scale": guidance_scale, "eta": scheduler_eta, } if USE_NEW_DIFFUSERS: extra_kwargs["negative_prompt"] = negative_prompt extra_kwargs["num_images_per_prompt"] = generate_num if use_seed: generator = torch.Generator(inpaint.device).manual_seed(seed_val) extra_kwargs["generator"] = generator if True: img, mask = functbl[fill_mode](img, mask) mask = 255 - mask mask = skimage.measure.block_reduce(mask, (8, 8), np.max) mask = mask.repeat(8, axis=0).repeat(8, axis=1) extra_kwargs["strength"] = strength inpaint_func = inpaint init_image = Image.fromarray(img) mask_image = Image.fromarray(mask) # mask_image=mask_image.filter(ImageFilter.GaussianBlur(radius = 8)) with autocast("cuda"): images = inpaint_func( prompt=prompt, image=init_image.resize( (process_width, process_height), resample=SAMPLING_MODE ), mask_image=mask_image.resize((process_width, process_height)), width=process_width, height=process_height, **extra_kwargs, )["images"] return images class StableDiffusion: def __init__( self, token: str = "", model_name: str = "runwayml/stable-diffusion-v1-5", model_path: str = None, inpainting_model: bool = False, **kwargs, ): self.token = token original_checkpoint = False if model_path and os.path.exists(model_path): if model_path.endswith(".ckpt"): original_checkpoint = True elif model_path.endswith(".json"): model_name = os.path.dirname(model_path) else: model_name = model_path if original_checkpoint: print(f"Converting & Loading {model_path}") from convert_checkpoint import convert_checkpoint text2img = convert_checkpoint(model_path) if device == "cuda" and not args.fp32: text2img.to(torch.float16) else: print(f"Loading {model_name}") if device == "cuda" and not args.fp32: text2img = StableDiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", revision="fp16", torch_dtype=torch.float16, use_auth_token=token, ) else: text2img = StableDiffusionPipeline.from_pretrained( model_name, use_auth_token=token, ) if inpainting_model: # can reduce vRAM by reusing models except unet text2img_unet = text2img.unet del text2img.vae del text2img.text_encoder del text2img.tokenizer del text2img.scheduler del text2img.safety_checker del text2img.feature_extractor import gc gc.collect() if device == "cuda" and not args.fp32: inpaint = StableDiffusionInpaintPipeline.from_pretrained( "runwayml/stable-diffusion-inpainting", revision="fp16", torch_dtype=torch.float16, use_auth_token=token, ).to(device) else: inpaint = StableDiffusionInpaintPipeline.from_pretrained( "runwayml/stable-diffusion-inpainting", use_auth_token=token, ).to(device) text2img_unet.to(device) text2img = StableDiffusionPipeline( vae=inpaint.vae, text_encoder=inpaint.text_encoder, tokenizer=inpaint.tokenizer, unet=text2img_unet, scheduler=inpaint.scheduler, safety_checker=inpaint.safety_checker, feature_extractor=inpaint.feature_extractor, ) else: inpaint = StableDiffusionInpaintPipelineLegacy( vae=text2img.vae, text_encoder=text2img.text_encoder, tokenizer=text2img.tokenizer, unet=text2img.unet, scheduler=text2img.scheduler, safety_checker=text2img.safety_checker, feature_extractor=text2img.feature_extractor, ).to(device) text_encoder = text2img.text_encoder tokenizer = text2img.tokenizer if os.path.exists("./embeddings"): for item in os.listdir("./embeddings"): if item.endswith(".bin"): load_learned_embed_in_clip( os.path.join("./embeddings", item), text2img.text_encoder, text2img.tokenizer, ) text2img.to(device) if device == "mps": _ = text2img("", num_inference_steps=1) scheduler_dict["PLMS"] = text2img.scheduler scheduler_dict["DDIM"] = prepare_scheduler( DDIMScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, ) ) scheduler_dict["K-LMS"] = prepare_scheduler( LMSDiscreteScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear" ) ) self.safety_checker = text2img.safety_checker img2img = StableDiffusionImg2ImgPipeline( vae=text2img.vae, text_encoder=text2img.text_encoder, tokenizer=text2img.tokenizer, unet=text2img.unet, scheduler=text2img.scheduler, safety_checker=text2img.safety_checker, feature_extractor=text2img.feature_extractor, ).to(device) save_token(token) try: total_memory = torch.cuda.get_device_properties(0).total_memory // ( 1024 ** 3 ) if total_memory <= 5: inpaint.enable_attention_slicing() except: pass self.text2img = text2img self.inpaint = inpaint self.img2img = img2img self.unified = UnifiedPipeline( vae=text2img.vae, text_encoder=text2img.text_encoder, tokenizer=text2img.tokenizer, unet=text2img.unet, scheduler=text2img.scheduler, safety_checker=text2img.safety_checker, feature_extractor=text2img.feature_extractor, ).to(device) self.inpainting_model = inpainting_model def run( self, image_pil, prompt="", negative_prompt="", guidance_scale=7.5, resize_check=True, enable_safety=True, fill_mode="patchmatch", strength=0.75, step=50, enable_img2img=False, use_seed=False, seed_val=-1, generate_num=1, scheduler="", scheduler_eta=0.0, **kwargs, ): text2img, inpaint, img2img, unified = ( self.text2img, self.inpaint, self.img2img, self.unified, ) selected_scheduler = scheduler_dict.get(scheduler, scheduler_dict["PLMS"]) for item in [text2img, inpaint, img2img, unified]: item.scheduler = selected_scheduler if enable_safety: item.safety_checker = self.safety_checker else: item.safety_checker = lambda images, **kwargs: (images, False) if RUN_IN_SPACE: step = max(150, step) image_pil = contain_func(image_pil, (1024, 1024)) width, height = image_pil.size sel_buffer = np.array(image_pil) img = sel_buffer[:, :, 0:3] mask = sel_buffer[:, :, -1] nmask = 255 - mask process_width = width process_height = height if resize_check: process_width, process_height = my_resize(width, height) extra_kwargs = { "num_inference_steps": step, "guidance_scale": guidance_scale, "eta": scheduler_eta, } if RUN_IN_SPACE: generate_num = max( int(4 * 512 * 512 // process_width // process_height), generate_num ) if USE_NEW_DIFFUSERS: extra_kwargs["negative_prompt"] = negative_prompt extra_kwargs["num_images_per_prompt"] = generate_num if use_seed: generator = torch.Generator(text2img.device).manual_seed(seed_val) extra_kwargs["generator"] = generator if nmask.sum() < 1 and enable_img2img: init_image = Image.fromarray(img) with autocast("cuda"): images = img2img( prompt=prompt, init_image=init_image.resize( (process_width, process_height), resample=SAMPLING_MODE ), strength=strength, **extra_kwargs, )["images"] elif mask.sum() > 0: if fill_mode == "g_diffuser" and not self.inpainting_model: mask = 255 - mask mask = mask[:, :, np.newaxis].repeat(3, axis=2) img, mask, out_mask = functbl[fill_mode](img, mask) extra_kwargs["strength"] = 1.0 extra_kwargs["out_mask"] = Image.fromarray(out_mask) inpaint_func = unified else: img, mask = functbl[fill_mode](img, mask) mask = 255 - mask mask = skimage.measure.block_reduce(mask, (8, 8), np.max) mask = mask.repeat(8, axis=0).repeat(8, axis=1) extra_kwargs["strength"] = strength inpaint_func = inpaint init_image = Image.fromarray(img) mask_image = Image.fromarray(mask) # mask_image=mask_image.filter(ImageFilter.GaussianBlur(radius = 8)) with autocast("cuda"): input_image = init_image.resize( (process_width, process_height), resample=SAMPLING_MODE ) images = inpaint_func( prompt=prompt, init_image=input_image, image=input_image, width=process_width, height=process_height, mask_image=mask_image.resize((process_width, process_height)), **extra_kwargs, )["images"] else: with autocast("cuda"): images = text2img( prompt=prompt, height=process_width, width=process_height, **extra_kwargs, )["images"] return images def get_model(token="", model_choice="", model_path=""): if "model" not in model: model_name = "" if model_choice == ModelChoice.INPAINTING.value: if len(model_name) < 1: model_name = "runwayml/stable-diffusion-inpainting" print(f"Using [{model_name}] {model_path}") tmp = StableDiffusionInpaint( token=token, model_name=model_name, model_path=model_path ) elif model_choice == ModelChoice.INPAINTING_IMG2IMG.value: print( f"Note that {ModelChoice.INPAINTING_IMG2IMG.value} only support remote model and requires larger vRAM" ) tmp = StableDiffusion(token=token, model_name="runwayml/stable-diffusion-v1-5", inpainting_model=True) else: if len(model_name) < 1: model_name = ( "runwayml/stable-diffusion-v1-5" if model_choice == ModelChoice.MODEL_1_5.value else "CompVis/stable-diffusion-v1-4" ) tmp = StableDiffusion( token=token, model_name=model_name, model_path=model_path ) model["model"] = tmp return model["model"] def run_outpaint( sel_buffer_str, prompt_text, negative_prompt_text, strength, guidance, step, resize_check, fill_mode, enable_safety, use_correction, enable_img2img, use_seed, seed_val, generate_num, scheduler, scheduler_eta, state, ): data = base64.b64decode(str(sel_buffer_str)) pil = Image.open(io.BytesIO(data)) width, height = pil.size sel_buffer = np.array(pil) cur_model = get_model() images = cur_model.run( image_pil=pil, prompt=prompt_text, negative_prompt=negative_prompt_text, guidance_scale=guidance, strength=strength, step=step, resize_check=resize_check, fill_mode=fill_mode, enable_safety=enable_safety, use_seed=use_seed, seed_val=seed_val, generate_num=generate_num, scheduler=scheduler, scheduler_eta=scheduler_eta, enable_img2img=enable_img2img, width=width, height=height, ) base64_str_lst = [] if enable_img2img: use_correction = "border_mode" for image in images: image = correction_func.run(pil.resize(image.size), image, mode=use_correction) resized_img = image.resize((width, height), resample=SAMPLING_MODE,) out = sel_buffer.copy() out[:, :, 0:3] = np.array(resized_img) out[:, :, -1] = 255 out_pil = Image.fromarray(out) out_buffer = io.BytesIO() out_pil.save(out_buffer, format="PNG") out_buffer.seek(0) base64_bytes = base64.b64encode(out_buffer.read()) base64_str = base64_bytes.decode("ascii") base64_str_lst.append(base64_str) return ( gr.update(label=str(state + 1), value=",".join(base64_str_lst),), gr.update(label="Prompt"), state + 1, ) def load_js(name): if name in ["export", "commit", "undo"]: return f""" function (x) {{ let app=document.querySelector("gradio-app"); app=app.shadowRoot??app; let frame=app.querySelector("#sdinfframe").contentWindow.document; let button=frame.querySelector("#{name}"); button.click(); return x; }} """ ret = "" with open(f"./js/{name}.js", "r") as f: ret = f.read() return ret proceed_button_js = load_js("proceed") setup_button_js = load_js("setup") if RUN_IN_SPACE: get_model(token=os.environ.get("hftoken", ""), model_choice=ModelChoice.INPAINTING_IMG2IMG.value) blocks = gr.Blocks( title="StableDiffusion-Infinity", css=""" .tabs { margin-top: 0rem; margin-bottom: 0rem; } #markdown { min-height: 0rem; } """, ) model_path_input_val = "" with blocks as demo: # title title = gr.Markdown( """ **stablediffusion-infinity**: Outpainting with Stable Diffusion on an infinite canvas: [https://github.com/lkwq007/stablediffusion-infinity](https://github.com/lkwq007/stablediffusion-infinity) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lkwq007/stablediffusion-infinity/blob/master/stablediffusion_infinity_colab.ipynb) [![Setup Locally](https://img.shields.io/badge/%F0%9F%96%A5%EF%B8%8F%20Setup-Locally-blue)](https://github.com/lkwq007/stablediffusion-infinity/blob/master/docs/setup_guide.md) """, elem_id="markdown", ) # frame frame = gr.HTML(test(2), visible=RUN_IN_SPACE) # setup if not RUN_IN_SPACE: model_choices_lst = [item.value for item in ModelChoice] if args.local_model: model_path_input_val = args.local_model # model_choices_lst.insert(0, "local_model") elif args.remote_model: model_path_input_val = args.remote_model # model_choices_lst.insert(0, "remote_model") with gr.Row(elem_id="setup_row"): with gr.Column(scale=4, min_width=350): token = gr.Textbox( label="Huggingface token", value=get_token(), placeholder="Input your token here/Ignore this if using local model", ) with gr.Column(scale=3, min_width=320): model_selection = gr.Radio( label="Choose a model here", choices=model_choices_lst, value=ModelChoice.INPAINTING.value, ) with gr.Column(scale=1, min_width=100): canvas_width = gr.Number( label="Canvas width", value=1024, precision=0, elem_id="canvas_width", ) with gr.Column(scale=1, min_width=100): canvas_height = gr.Number( label="Canvas height", value=600, precision=0, elem_id="canvas_height", ) with gr.Column(scale=1, min_width=100): selection_size = gr.Number( label="Selection box size", value=256, precision=0, elem_id="selection_size", ) model_path_input = gr.Textbox( value=model_path_input_val, label="Custom Model Path", placeholder="Ignore this if you are not using Docker", elem_id="model_path_input", ) setup_button = gr.Button("Click to Setup (may take a while)", variant="primary") with gr.Row(): with gr.Column(scale=3, min_width=270): init_mode = gr.Radio( label="Init Mode", choices=[ "patchmatch", "edge_pad", "cv2_ns", "cv2_telea", "perlin", "gaussian", ], value="patchmatch", type="value", ) postprocess_check = gr.Radio( label="Photometric Correction Mode", choices=["disabled", "mask_mode", "border_mode",], value="disabled", type="value", ) # canvas control with gr.Column(scale=3, min_width=270): sd_prompt = gr.Textbox( label="Prompt", placeholder="input your prompt here!", lines=2 ) sd_negative_prompt = gr.Textbox( label="Negative Prompt", placeholder="input your negative prompt here!", lines=2, ) with gr.Column(scale=2, min_width=150): with gr.Group(): with gr.Row(): sd_generate_num = gr.Number( label="Sample number", value=1, precision=0 ) sd_strength = gr.Slider( label="Strength", minimum=0.0, maximum=1.0, value=0.75, step=0.01, ) with gr.Row(): sd_scheduler = gr.Dropdown( list(scheduler_dict.keys()), label="Scheduler", value="PLMS" ) sd_scheduler_eta = gr.Number(label="Eta", value=0.0) with gr.Column(scale=1, min_width=80): sd_step = gr.Number(label="Step", value=50, precision=0) sd_guidance = gr.Number(label="Guidance", value=7.5) proceed_button = gr.Button("Proceed", elem_id="proceed", visible=DEBUG_MODE) xss_js = load_js("xss").replace("\n", " ") xss_html = gr.HTML( value=f""" """, visible=False, ) xss_keyboard_js = load_js("keyboard").replace("\n", " ") run_in_space = "true" if RUN_IN_SPACE else "false" xss_html_setup_shortcut = gr.HTML( value=f""" """, visible=False, ) # sd pipeline parameters sd_img2img = gr.Checkbox(label="Enable Img2Img", value=False, visible=False) sd_resize = gr.Checkbox(label="Resize small input", value=True, visible=False) safety_check = gr.Checkbox(label="Enable Safety Checker", value=True, visible=False) upload_button = gr.Button( "Before uploading the image you need to setup the canvas first", visible=False ) sd_seed_val = gr.Number(label="Seed", value=0, precision=0, visible=False) sd_use_seed = gr.Checkbox(label="Use seed", value=False, visible=False) model_output = gr.Textbox(visible=DEBUG_MODE, elem_id="output", label="0") model_input = gr.Textbox(visible=DEBUG_MODE, elem_id="input", label="Input") upload_output = gr.Textbox(visible=DEBUG_MODE, elem_id="upload", label="0") model_output_state = gr.State(value=0) upload_output_state = gr.State(value=0) cancel_button = gr.Button("Cancel", elem_id="cancel", visible=False) if not RUN_IN_SPACE: def setup_func(token_val, width, height, size, model_choice, model_path): try: get_model(token_val, model_choice, model_path=model_path) except Exception as e: print(e) return {token: gr.update(value=str(e))} return { token: gr.update(visible=False), canvas_width: gr.update(visible=False), canvas_height: gr.update(visible=False), selection_size: gr.update(visible=False), setup_button: gr.update(visible=False), frame: gr.update(visible=True), upload_button: gr.update(value="Upload Image"), model_selection: gr.update(visible=False), model_path_input: gr.update(visible=False), } setup_button.click( fn=setup_func, inputs=[ token, canvas_width, canvas_height, selection_size, model_selection, model_path_input, ], outputs=[ token, canvas_width, canvas_height, selection_size, setup_button, frame, upload_button, model_selection, model_path_input, ], _js=setup_button_js, ) proceed_event = proceed_button.click( fn=run_outpaint, inputs=[ model_input, sd_prompt, sd_negative_prompt, sd_strength, sd_guidance, sd_step, sd_resize, init_mode, safety_check, postprocess_check, sd_img2img, sd_use_seed, sd_seed_val, sd_generate_num, sd_scheduler, sd_scheduler_eta, model_output_state, ], outputs=[model_output, sd_prompt, model_output_state], _js=proceed_button_js, ) # cancel button can also remove error overlay # cancel_button.click(fn=None, inputs=None, outputs=None, cancels=[proceed_event]) launch_extra_kwargs = { "show_error": True, # "favicon_path": "" } launch_kwargs = vars(args) launch_kwargs = {k: v for k, v in launch_kwargs.items() if v is not None} launch_kwargs.pop("remote_model", None) launch_kwargs.pop("local_model", None) launch_kwargs.pop("fp32", None) launch_kwargs.update(launch_extra_kwargs) try: import google.colab launch_kwargs["debug"] = True except: pass if RUN_IN_SPACE: demo.launch() elif args.debug: launch_kwargs["server_name"] = "0.0.0.0" demo.queue().launch(**launch_kwargs) else: demo.queue().launch(**launch_kwargs)