Spaces:
Runtime error
Runtime error
File size: 10,070 Bytes
2a6b1af e14c450 2a6b1af e14c450 2a6b1af e14c450 2a6b1af b5ecd5f e14c450 b5ecd5f e14c450 000ee9a e14c450 61dc3f4 e14c450 61dc3f4 e14c450 000ee9a e14c450 2a6b1af e14c450 2a6b1af e14c450 2a6b1af e14c450 2a6b1af e14c450 2a6b1af e14c450 2a6b1af e14c450 2a6b1af |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 |
import random
import gradio as gr
import torch
from diffusers.utils import load_image
from PIL import Image
import numpy as np
import base64
from io import BytesIO
from mediapipe_face_common import generate_annotation
from diffusers import (
ControlNetModel,
StableDiffusionControlNetPipeline,
)
# Download the SD 1.5 model from HF
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
controlnet = ControlNetModel.from_pretrained(
"CrucibleAI/ControlNetMediaPipeFace", torch_dtype=torch.float16, variant="fp16")
model = StableDiffusionControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1-base", controlnet=controlnet, torch_dtype=torch.float16
)
model = model.to(device)
model.enable_model_cpu_offload()
canvas_html = "<face-canvas id='canvas-root' data-mode='crucibleAI' style='display:flex;max-width: 500px;margin: 0 auto;'></face-canvas>"
load_js = """
async () => {
const url = "https://huggingface.co/datasets/radames/gradio-components/raw/main/face-canvas.js"
fetch(url)
.then(res => res.text())
.then(text => {
const script = document.createElement('script');
script.type = "module"
script.src = URL.createObjectURL(new Blob([text], { type: 'application/javascript' }));
document.head.appendChild(script);
});
}
"""
get_js_image = """
async (input_image, prompt, a_prompt, n_prompt, max_faces, min_confidence, num_samples, ddim_steps, guess_mode, strength, scale, seed, eta, image_file_live_opt, live_conditioning) => {
const canvasEl = document.getElementById("canvas-root");
const imageData = canvasEl? canvasEl._data : null;
return [input_image, prompt, a_prompt, n_prompt, max_faces, min_confidence, num_samples, ddim_steps, guess_mode, strength, scale, seed, eta, image_file_live_opt, imageData];
}
"""
def pad_image(input_image):
pad_w, pad_h = np.max(((2, 2), np.ceil(
np.array(input_image.size) / 64).astype(int)), axis=0) * 64 - input_image.size
im_padded = Image.fromarray(
np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
w, h = im_padded.size
if w == h:
return im_padded
elif w > h:
new_image = Image.new(im_padded.mode, (w, w), (0, 0, 0))
new_image.paste(im_padded, (0, (w - h) // 2))
return new_image
else:
new_image = Image.new(im_padded.mode, (h, h), (0, 0, 0))
new_image.paste(im_padded, ((h - w) // 2, 0))
return new_image
def process(input_image: Image.Image, prompt, a_prompt, n_prompt, max_faces: int, min_confidence: float, num_samples, ddim_steps, guess_mode, strength: float, scale, seed: int, eta, image_file_live_opt="file", live_conditioning=None):
if input_image is None and 'image' not in live_conditioning:
raise gr.Error("Please provide an image")
try:
if image_file_live_opt == 'file':
# Resize before annotation so that we can keep our line-widths consistent with the training data.
input_image = pad_image(input_image.convert('RGB')).resize((512, 512))
empty = generate_annotation(np.array(input_image), max_faces, min_confidence)
visualization = Image.fromarray(empty)
elif image_file_live_opt == 'webcam':
base64_img = live_conditioning['image']
image_data = base64.b64decode(base64_img.split(',')[1])
visualization = Image.open(BytesIO(image_data)).convert('RGB').resize((512, 512))
if seed == -1:
seed = random.randint(0, 2147483647)
generator = torch.Generator(device).manual_seed(seed)
output = model(prompt=prompt + ' ' + a_prompt,
negative_prompt=n_prompt,
image=visualization,
generator=generator,
num_images_per_prompt=num_samples,
num_inference_steps=ddim_steps,
controlnet_conditioning_scale=float(strength),
guidance_scale=scale,
eta=eta,
)
results = [visualization] + output.images
return results
except Exception as e:
raise gr.Error(str(e))
# switch between file upload and webcam
def toggle(choice):
if choice == "file":
return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
elif choice == "webcam":
return gr.update(visible=False, value=None), gr.update(visible=True, value=canvas_html)
block = gr.Blocks().queue()
with block:
# hidden JSON component to store live conditioning
live_conditioning = gr.JSON(value={}, visible=False)
with gr.Row():
gr.Markdown("## Control Stable Diffusion with a Facial Pose")
with gr.Row():
with gr.Column():
image_file_live_opt = gr.Radio(["file", "webcam"], value="file",
label="How would you like to upload your image?")
input_image = gr.Image(source="upload", visible=True, type="pil")
canvas = gr.HTML(None, elem_id="canvas_html", visible=False)
image_file_live_opt.change(fn=toggle,
inputs=[image_file_live_opt],
outputs=[input_image, canvas],
queue=False)
prompt = gr.Textbox(label="Prompt")
run_button = gr.Button(label="Run")
with gr.Accordion("Advanced options", open=False):
num_samples = gr.Slider(
label="Images", minimum=1, maximum=4, value=1, step=1)
max_faces = gr.Slider(
label="Max Faces", minimum=1, maximum=10, value=5, step=1)
min_confidence = gr.Slider(
label="Min Confidence", minimum=0.01, maximum=1.0, value=0.5, step=0.01)
strength = gr.Slider(
label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
guess_mode = gr.Checkbox(label='Guess Mode', value=False)
ddim_steps = gr.Slider(
label="Steps", minimum=1, maximum=100, value=20, step=1)
scale = gr.Slider(label="Guidance Scale",
minimum=0.1, maximum=30.0, value=9.0, step=0.1)
seed = gr.Slider(label="Seed", minimum=-1,
maximum=2147483647, step=1, randomize=True)
eta = gr.Number(label="eta (DDIM)", value=0.0)
a_prompt = gr.Textbox(
label="Added Prompt", value='best quality, extremely detailed')
n_prompt = gr.Textbox(label="Negative Prompt",
value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality')
with gr.Column():
result_gallery = gr.Gallery(
label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
ips = [input_image, prompt, a_prompt, n_prompt, max_faces, min_confidence,
num_samples, ddim_steps, guess_mode, strength, scale, seed, eta]
run_button.click(fn=process, inputs=ips + [image_file_live_opt, live_conditioning],
outputs=[result_gallery],
_js=get_js_image)
# load js for live conditioning
block.load(None, None, None, _js=load_js)
gr.Examples(fn=process,
examples=[
["./examples/two2.jpeg",
"Highly detailed photograph of two clowns",
"best quality, extremely detailed",
"cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
10, 0.4, 3, 20, False, 1.0, 9.0, -1, 0.0],
["./examples/two.jpeg",
"a photo of two silly men",
"best quality, extremely detailed",
"cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
10, 0.4, 3, 20, False, 1.0, 9.0, -1, 0.0],
["./examples/pedro-512.jpg",
"Highly detailed photograph of young woman smiling, with palm trees in the background",
"best quality, extremely detailed",
"cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
10, 0.4, 3, 20, False, 1.0, 9.0, -1, 0.0],
["./examples/image1.jpg",
"Highly detailed photograph of a scary clown",
"best quality, extremely detailed",
"cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
10, 0.4, 3, 20, False, 1.0, 9.0, -1, 0.0],
["./examples/image0.jpg",
"Highly detailed photograph of Madonna",
"best quality, extremely detailed",
"cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
10, 0.4, 3, 20, False, 1.0, 9.0, -1, 0.0],
],
inputs=ips,
outputs=[result_gallery],
cache_examples=True)
block.launch(server_name='0.0.0.0')
|