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app.py
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import io
import base64
import os
import sys
import numpy as np
import torch
from torch import autocast
import diffusers
assert tuple(map(int,diffusers.__version__.split("."))) >= (0,9,0), "Please upgrade diffusers to 0.9.0"
from diffusers.configuration_utils import FrozenDict
from diffusers import (
StableDiffusionPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionImg2ImgPipeline,
StableDiffusionInpaintPipelineLegacy,
DDIMScheduler,
LMSDiscreteScheduler,
DiffusionPipeline,
StableDiffusionUpscalePipeline,
DPMSolverMultistepScheduler,
)
from diffusers.models import AutoencoderKL
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
from utils import *
try:
abspath = os.path.abspath(__file__)
dirname = os.path.dirname(abspath)
os.chdir(dirname)
except:
pass
try:
from interrogate import Interrogator
except:
Interrogator = DummyInterrogator
USE_NEW_DIFFUSERS = True
RUN_IN_SPACE = "RUN_IN_HG_SPACE" in os.environ
class ModelChoice(Enum):
INPAINTING = "stablediffusion-inpainting"
INPAINTING2 = "stablediffusion-2-inpainting"
INPAINTING_IMG2IMG = "stablediffusion-inpainting+img2img-1.5"
MODEL_2_1 = "stablediffusion-2.1"
MODEL_2_0_V = "stablediffusion-2.0v"
MODEL_2_0 = "stablediffusion-2.0"
MODEL_1_5 = "stablediffusion-1.5"
MODEL_1_4 = "stablediffusion-1.4"
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"""<iframe id="sdinfframe" style="width: 100%; height: 600px" name="result" allow="midi; geolocation; microphone; camera;
display-capture; encrypted-media; vertical-scroll 'none'" sandbox="allow-modals allow-forms
allow-scripts allow-same-origin allow-popups
allow-top-navigation-by-user-activation allow-downloads" allowfullscreen=""
allowpaymentrequest="" frameborder="0" srcdoc='{x}'></iframe>"""
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("--lowvram", action="store_true", help="using lowvram mode")
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, "DPM": 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
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
if device == "cuda" and not args.fp32:
vae.to(torch.float16)
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=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,
vae=vae,
)
else:
inpaint = StableDiffusionInpaintPipeline.from_pretrained(
model_name, use_auth_token=token, vae=vae
)
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"
)
)
scheduler_dict["DPM"] = prepare_scheduler(
DPMSolverMultistepScheduler.from_config(inpaint.scheduler.config)
)
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 or args.lowvram:
inpaint.enable_attention_slicing()
inpaint.enable_sequential_cpu_offload()
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 or self.safety_checker is None:
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)
process_width = process_width * 8 // 8
process_height = process_height * 8 // 8
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:
if fill_mode == "g_diffuser":
mask = 255 - mask
mask = mask[:, :, np.newaxis].repeat(3, axis=2)
img, mask = functbl[fill_mode](img, mask)
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))
if True:
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
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
if device == "cuda" and not args.fp32:
vae.to(torch.float16)
if original_checkpoint:
print(f"Converting & Loading {model_path}")
from convert_checkpoint import convert_checkpoint
pipe = convert_checkpoint(model_path)
if device == "cuda" and not args.fp32:
pipe.to(torch.float16)
text2img = StableDiffusionPipeline(
vae=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:
text2img = StableDiffusionPipeline.from_pretrained(
model_name,
revision="fp16",
torch_dtype=torch.float16,
use_auth_token=token,
vae=vae,
)
else:
text2img = StableDiffusionPipeline.from_pretrained(
model_name, use_auth_token=token, vae=vae
)
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,
vae=vae,
).to(device)
else:
inpaint = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting",
use_auth_token=token,
vae=vae,
).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"
)
)
scheduler_dict["DPM"] = prepare_scheduler(
DPMSolverMultistepScheduler.from_config(text2img.scheduler.config)
)
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 or args.lowvram:
inpaint.enable_attention_slicing()
inpaint.enable_sequential_cpu_offload()
if inpainting_model:
text2img.enable_attention_slicing()
text2img.enable_sequential_cpu_offload()
except:
pass
self.text2img = text2img
self.inpaint = inpaint
self.img2img = img2img
if True:
self.unified = inpaint
else:
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 or self.safety_checker is None:
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)
if True:
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 = functbl[fill_mode](img, mask)
extra_kwargs["strength"] = 1.0
extra_kwargs["out_mask"] = Image.fromarray(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)
inpaint_func = inpaint
init_image = Image.fromarray(img)
mask_image = Image.fromarray(mask)
# mask_image=mask_image.filter(ImageFilter.GaussianBlur(radius = 8))
input_image = init_image.resize(
(process_width, process_height), resample=SAMPLING_MODE
)
if self.inpainting_model:
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:
extra_kwargs["strength"] = strength
if True:
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:
if True:
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 args.local_model:
print(f"Using local_model: {args.local_model}")
model_path = args.local_model
elif args.remote_model:
print(f"Using remote_model: {args.remote_model}")
model_name = args.remote_model
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.INPAINTING2.value:
if len(model_name) < 1:
model_name = "stabilityai/stable-diffusion-2-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, 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"
)
if model_choice == ModelChoice.MODEL_2_0.value:
model_name = "stabilityai/stable-diffusion-2-base"
elif model_choice == ModelChoice.MODEL_2_0_V.value:
model_name = "stabilityai/stable-diffusion-2"
elif model_choice == ModelChoice.MODEL_2_1.value:
model_name = "stabilityai/stable-diffusion-2-1-base"
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,
interrogate_mode,
state,
):
data = base64.b64decode(str(sel_buffer_str))
pil = Image.open(io.BytesIO(data))
if interrogate_mode:
if "interrogator" not in model:
model["interrogator"] = Interrogator()
interrogator = model["interrogator"]
img = np.array(pil)[:, :, 0:3]
mask = np.array(pil)[:, :, -1]
x, y = np.nonzero(mask)
if len(x) > 0:
x0, x1 = x.min(), x.max() + 1
y0, y1 = y.min(), y.max() + 1
img = img[x0:x1, y0:y1, :]
pil = Image.fromarray(img)
interrogate_ret = interrogator.interrogate(pil)
return (
gr.update(value=",".join([sel_buffer_str]),),
gr.update(label="Prompt", value=interrogate_ret),
state,
)
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)
""",
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 type here",
choices=model_choices_lst,
value=ModelChoice.INPAINTING2.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 (You have to select a correct model type for your local model)",
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",
"g_diffuser",
],
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=1.0,
step=0.01,
)
with gr.Row():