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from diffusers import ( StableDiffusionControlNetPipeline,
ControlNetModel, UniPCMultistepScheduler )
from transformers import pipeline
from PIL import Image
import gradio as gr
import numpy as np
import torch
stable_model_list = [
"runwayml/stable-diffusion-v1-5",
"stabilityai/stable-diffusion-2",
"stabilityai/stable-diffusion-2-base",
"stabilityai/stable-diffusion-2-1",
"stabilityai/stable-diffusion-2-1-base"
]
stable_inpiant_model_list = [
"stabilityai/stable-diffusion-2-inpainting",
"runwayml/stable-diffusion-inpainting"
]
stable_prompt_list = [
"a photo of a man.",
"a photo of a girl."
]
stable_negative_prompt_list = [
"bad, ugly",
"deformed"
]
def controlnet_depth(image_path:str):
depth_estimator = pipeline('depth-estimation')
image = Image.open(image_path)
image = depth_estimator(image)['depth']
image = np.array(image)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
image = Image.fromarray(image)
controlnet = ControlNetModel.from_pretrained(
"fusing/stable-diffusion-v1-5-controlnet-depth", torch_dtype=torch.float16
)
return controlnet, image
def stable_diffusion_controlnet_depth(
image_path:str,
model_path:str,
prompt:str,
negative_prompt:str,
guidance_scale:int,
num_inference_step:int,
):
controlnet, image = controlnet_depth(image_path=image_path)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
pretrained_model_name_or_path=model_path,
controlnet=controlnet,
safety_checker=None,
torch_dtype=torch.float16
)
pipe.to("cuda")
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
output = pipe(
prompt = prompt,
image = image,
negative_prompt = negative_prompt,
num_inference_steps = num_inference_step,
guidance_scale = guidance_scale,
).images
return output[0]
def stable_diffusion_controlnet_depth_app():
with gr.Tab('Depth'):
controlnet_depth_image_file = gr.Image(
type='filepath',
label='Image'
)
controlnet_depth_model_id = gr.Dropdown(
choices=stable_model_list,
value=stable_model_list[0],
label='Stable Model Id'
)
controlnet_depth_prompt = gr.Textbox(
lines=1,
value=stable_prompt_list[0],
label='Prompt'
)
controlnet_depth_negative_prompt = gr.Textbox(
lines=1,
value=stable_negative_prompt_list[0],
label='Negative Prompt'
)
with gr.Accordion("Advanced Options", open=False):
controlnet_depth_guidance_scale = gr.Slider(
minimum=0.1,
maximum=15,
step=0.1,
value=7.5,
label='Guidance Scale'
)
controlnet_depth_num_inference_step = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=50,
label='Num Inference Step'
)
controlnet_depth_predict = gr.Button(value='Generator')
variables = {
'image_path': controlnet_depth_image_file,
'model_path': controlnet_depth_model_id,
'prompt': controlnet_depth_prompt,
'negative_prompt': controlnet_depth_negative_prompt,
'guidance_scale': controlnet_depth_guidance_scale,
'num_inference_step': controlnet_depth_num_inference_step,
'predict': controlnet_depth_predict
}
return variables |