Spaces:
Configuration error
Configuration error
File size: 4,220 Bytes
5028f2c 9643b26 5028f2c b388c40 5028f2c 1801f11 5028f2c 22b3efc 2714820 5028f2c b388c40 5028f2c 1801f11 5028f2c |
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 |
import spaces
from diffusers import ControlNetModel
from diffusers import StableDiffusionXLControlNetPipeline
from diffusers import EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
import gradio as gr
from torchvision import transforms
from controlnet_aux import OpenposeDetector
openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')
controlnet = ControlNetModel.from_pretrained(
"briaai/BRIA-2.3-ControlNet-Pose",
torch_dtype=torch.float16
).to('cuda')
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"briaai/BRIA-2.3",
controlnet=controlnet,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
offload_state_dict=True,
).to('cuda').to(torch.float16)
pipe.scheduler = EulerAncestralDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
num_train_timesteps=1000,
steps_offset=1
)
# pipe.enable_freeu(b1=1.1, b2=1.1, s1=0.5, s2=0.7)
# pipe.enable_xformers_memory_efficient_attention()
pipe.force_zeros_for_empty_prompt = False
def resize_image(image):
image = image.convert('RGB')
current_size = image.size
if current_size[0] > current_size[1]:
center_cropped_image = transforms.functional.center_crop(image, (current_size[1], current_size[1]))
else:
center_cropped_image = transforms.functional.center_crop(image, (current_size[0], current_size[0]))
resized_image = transforms.functional.resize(center_cropped_image, (1024, 1024))
return resized_image
@spaces.GPU
def generate_(prompt, negative_prompt, pose_image, num_steps, controlnet_conditioning_scale, seed):
generator = torch.Generator("cuda").manual_seed(seed)
images = pipe(
prompt, negative_prompt=negative_prompt, image=pose_image, num_inference_steps=num_steps, controlnet_conditioning_scale=float(controlnet_conditioning_scale),
generator=generator, height=1024, width=1024,
).images
return images
@spaces.GPU
def process(input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed):
# resize input_image to 1024x1024
input_image = resize_image(input_image)
pose_image = openpose(input_image, include_body=True, include_hand=True, include_face=True)
images = generate_(prompt, negative_prompt, pose_image, num_steps, controlnet_conditioning_scale, seed)
return [pose_image,images[0]]
block = gr.Blocks().queue()
with block:
gr.Markdown("## BRIA 2.3 ControlNet Pose")
gr.HTML('''
<p style="margin-bottom: 10px; font-size: 94%">
This is a demo for ControlNet Pose that using
<a href="https://huggingface.co/briaai/BRIA-2.3" target="_blank">BRIA 2.3 text-to-image model</a> as backbone.
Trained on licensed data, BRIA 2.3 provide full legal liability coverage for copyright and privacy infringement.
</p>
''')
with gr.Row():
with gr.Column():
input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
prompt = gr.Textbox(label="Prompt")
negative_prompt = gr.Textbox(label="Negative prompt", value="Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers")
num_steps = gr.Slider(label="Number of steps", minimum=25, maximum=100, value=50, step=1)
controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05)
seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True,)
run_button = gr.Button(value="Run")
with gr.Column():
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[2], height='auto')
ips = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed]
run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
block.launch(debug = True) |