radames's picture
radames HF staff
add anyline params
f2d92e1
import spaces
import gradio as gr
from gradio_imageslider import ImageSlider
import torch
torch.jit.script = lambda f: f
from hidiffusion import apply_hidiffusion
from diffusers import (
ControlNetModel,
StableDiffusionXLControlNetImg2ImgPipeline,
DDIMScheduler,
)
from controlnet_aux import AnylineDetector
from compel import Compel, ReturnedEmbeddingsType
from PIL import Image
import os
import time
import numpy as np
IS_SPACES_ZERO = os.environ.get("SPACES_ZERO_GPU", "0") == "1"
IS_SPACE = os.environ.get("SPACE_ID", None) is not None
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16
LOW_MEMORY = os.getenv("LOW_MEMORY", "0") == "1"
print(f"device: {device}")
print(f"dtype: {dtype}")
print(f"low memory: {LOW_MEMORY}")
model = "stabilityai/stable-diffusion-xl-base-1.0"
# model = "stabilityai/sdxl-turbo"
# vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=dtype)
scheduler = DDIMScheduler.from_pretrained(model, subfolder="scheduler")
# controlnet = ControlNetModel.from_pretrained(
# "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16
# )
controlnet = ControlNetModel.from_pretrained(
"TheMistoAI/MistoLine",
torch_dtype=torch.float16,
revision="refs/pr/3",
variant="fp16",
)
pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
model,
controlnet=controlnet,
torch_dtype=dtype,
variant="fp16",
use_safetensors=True,
scheduler=scheduler,
)
compel = Compel(
tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
requires_pooled=[False, True],
)
pipe = pipe.to(device)
if not IS_SPACES_ZERO:
apply_hidiffusion(pipe)
# pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()
pipe.enable_vae_tiling()
anyline = AnylineDetector.from_pretrained(
"TheMistoAI/MistoLine", filename="MTEED.pth", subfolder="Anyline"
).to(device)
def pad_image(image):
w, h = image.size
if w == h:
return image
elif w > h:
new_image = Image.new(image.mode, (w, w), (0, 0, 0))
pad_w = 0
pad_h = (w - h) // 2
new_image.paste(image, (0, pad_h))
return new_image
else:
new_image = Image.new(image.mode, (h, h), (0, 0, 0))
pad_w = (h - w) // 2
pad_h = 0
new_image.paste(image, (pad_w, 0))
return new_image
@spaces.GPU
def predict(
input_image,
prompt,
negative_prompt,
seed,
guidance_scale=8.5,
scale=2,
controlnet_conditioning_scale=0.5,
strength=1.0,
controlnet_start=0.0,
controlnet_end=1.0,
guassian_sigma=2.0,
intensity_threshold=3,
progress=gr.Progress(track_tqdm=True),
):
if IS_SPACES_ZERO:
apply_hidiffusion(pipe)
if input_image is None:
raise gr.Error("Please upload an image.")
padded_image = pad_image(input_image).resize((1024, 1024)).convert("RGB")
conditioning, pooled = compel([prompt, negative_prompt])
generator = torch.manual_seed(seed)
last_time = time.time()
anyline_image = anyline(
padded_image,
detect_resolution=1280,
guassian_sigma=max(0.01, guassian_sigma),
intensity_threshold=intensity_threshold,
)
images = pipe(
image=padded_image,
control_image=anyline_image,
strength=strength,
prompt_embeds=conditioning[0:1],
pooled_prompt_embeds=pooled[0:1],
negative_prompt_embeds=conditioning[1:2],
negative_pooled_prompt_embeds=pooled[1:2],
width=1024 * scale,
height=1024 * scale,
controlnet_conditioning_scale=float(controlnet_conditioning_scale),
controlnet_start=float(controlnet_start),
controlnet_end=float(controlnet_end),
generator=generator,
num_inference_steps=30,
guidance_scale=guidance_scale,
eta=1.0,
)
print(f"Time taken: {time.time() - last_time}")
return (padded_image, images.images[0]), padded_image, anyline_image
css = """
#intro{
# max-width: 32rem;
# text-align: center;
# margin: 0 auto;
}
"""
with gr.Blocks(css=css) as demo:
gr.Markdown(
"""
# Enhance This
### HiDiffusion SDXL
[HiDiffusion](https://github.com/megvii-research/HiDiffusion) enables higher-resolution image generation.
You can upload an initial image and prompt to generate an enhanced version.
SDXL Controlnet [TheMistoAI/MistoLine](https://huggingface.co/TheMistoAI/MistoLine)
[Duplicate Space](https://huggingface.co/spaces/radames/Enhance-This-HiDiffusion-SDXL?duplicate=true) to avoid the queue.
<small>
<b>Notes</b> The author advises against the term "super resolution" because it's more like image-to-image generation than enhancement, but it's still a lot of fun!
</small>
""",
elem_id="intro",
)
with gr.Row():
with gr.Column(scale=1):
image_input = gr.Image(type="pil", label="Input Image")
prompt = gr.Textbox(
label="Prompt",
info="The prompt is very important to get the desired results. Please try to describe the image as best as you can. Accepts Compel Syntax",
)
negative_prompt = gr.Textbox(
label="Negative Prompt",
value="blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
)
seed = gr.Slider(
minimum=0,
maximum=2**64 - 1,
value=1415926535897932,
step=1,
label="Seed",
randomize=True,
)
with gr.Accordion(label="Advanced", open=False):
guidance_scale = gr.Slider(
minimum=0,
maximum=50,
value=8.5,
step=0.001,
label="Guidance Scale",
)
scale = gr.Slider(
minimum=1,
maximum=5,
value=2,
step=1,
label="Magnification Scale",
interactive=not IS_SPACE,
)
controlnet_conditioning_scale = gr.Slider(
minimum=0,
maximum=1,
step=0.001,
value=0.5,
label="ControlNet Conditioning Scale",
)
strength = gr.Slider(
minimum=0,
maximum=1,
step=0.001,
value=1,
label="Strength",
)
controlnet_start = gr.Slider(
minimum=0,
maximum=1,
step=0.001,
value=0.0,
label="ControlNet Start",
)
controlnet_end = gr.Slider(
minimum=0.0,
maximum=1.0,
step=0.001,
value=1.0,
label="ControlNet End",
)
guassian_sigma = gr.Slider(
minimum=0.01,
maximum=10.0,
step=0.1,
value=2.0,
label="(Anyline) Guassian Sigma",
)
intensity_threshold = gr.Slider(
minimum=0,
maximum=255,
step=1,
value=3,
label="(Anyline) Intensity Threshold",
)
btn = gr.Button()
with gr.Column(scale=2):
with gr.Group():
image_slider = ImageSlider(position=0.5)
with gr.Row():
padded_image = gr.Image(type="pil", label="Padded Image")
anyline_image = gr.Image(type="pil", label="Anyline Image")
inputs = [
image_input,
prompt,
negative_prompt,
seed,
guidance_scale,
scale,
controlnet_conditioning_scale,
strength,
controlnet_start,
controlnet_end,
guassian_sigma,
intensity_threshold,
]
outputs = [image_slider, padded_image, anyline_image]
btn.click(lambda x: None, inputs=None, outputs=image_slider).then(
fn=predict, inputs=inputs, outputs=outputs
)
gr.Examples(
fn=predict,
inputs=inputs,
outputs=outputs,
examples=[
[
"./examples/lara.jpeg",
"photography of lara croft 8k high definition award winning",
"blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
5436236241,
8.5,
2,
0.8,
1.0,
0.0,
0.9,
2,
3,
],
[
"./examples/cybetruck.jpeg",
"photo of tesla cybertruck futuristic car 8k high definition on a sand dune in mars, future",
"blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
383472451451,
8.5,
2,
0.8,
0.8,
0.0,
0.9,
2,
3,
],
[
"./examples/jesus.png",
"a photorealistic painting of Jesus Christ, 4k high definition",
"blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
13317204146129588000,
8.5,
2,
0.8,
0.8,
0.0,
0.9,
2,
3,
],
[
"./examples/anna-sullivan-DioLM8ViiO8-unsplash.jpg",
"A crowded stadium with enthusiastic fans watching a daytime sporting event, the stands filled with colorful attire and the sun casting a warm glow",
"blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
5623124123512,
8.5,
2,
0.8,
0.8,
0.0,
0.9,
2,
3,
],
[
"./examples/img_aef651cb-2919-499d-aa49-6d4e2e21a56e_1024.jpg",
"a large red flower on a black background 4k high definition",
"blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
23123412341234,
8.5,
2,
0.8,
0.8,
0.0,
0.9,
2,
3,
],
[
"./examples/huggingface.jpg",
"photo realistic huggingface human emoji costume, round, yellow, (human skin)+++ (human texture)+++",
"blurry, ugly, duplicate, poorly drawn, deformed, mosaic, emoji cartoon, drawing, pixelated",
12312353423,
15.206,
2,
0.364,
0.8,
0.0,
0.9,
2,
3,
],
],
cache_examples="lazy",
)
demo.queue(api_open=False)
demo.launch(show_api=False)