Spaces:
Running
Running
File size: 3,612 Bytes
3911a99 |
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 |
from diffusers import ( StableDiffusionControlNetPipeline,
ControlNetModel, UniPCMultistepScheduler)
from controlnet_aux import MLSDdetector
from PIL import Image
import gradio as gr
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_mlsd(image_path:str):
mlsd = MLSDdetector.from_pretrained('lllyasviel/ControlNet')
image = Image.open(image_path)
image = mlsd(image)
controlnet = ControlNetModel.from_pretrained(
"fusing/stable-diffusion-v1-5-controlnet-mlsd",
torch_dtype=torch.float16
)
return controlnet, image
def stable_diffusion_controlnet_mlsd(
image_path:str,
model_path:str,
prompt:str,
negative_prompt:str,
guidance_scale:int,
num_inference_step:int,
):
controlnet, image = controlnet_mlsd(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_mlsd_app():
with gr.Tab('MLSD line'):
controlnet_mlsd_image_file = gr.Image(
type='filepath',
label='Image'
)
controlnet_mlsd_model_id = gr.Dropdown(
choices=stable_model_list,
value=stable_model_list[0],
label='Stable Model Id'
)
controlnet_mlsd_prompt = gr.Textbox(
lines=1,
value=stable_prompt_list[0],
label='Prompt'
)
controlnet_mlsd_negative_prompt = gr.Textbox(
lines=1,
value=stable_negative_prompt_list[0],
label='Negative Prompt'
)
with gr.Accordion("Advanced Options", open=False):
controlnet_mlsd_guidance_scale = gr.Slider(
minimum=0.1,
maximum=15,
step=0.1,
value=7.5,
label='Guidance Scale'
)
controlnet_mlsd_num_inference_step = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=50,
label='Num Inference Step'
)
controlnet_mlsd_predict = gr.Button(value='Generator')
variables = {
'image_path': controlnet_mlsd_image_file,
'model_path': controlnet_mlsd_model_id,
'prompt': controlnet_mlsd_prompt,
'negative_prompt': controlnet_mlsd_negative_prompt,
'guidance_scale': controlnet_mlsd_guidance_scale,
'num_inference_step': controlnet_mlsd_num_inference_step,
'predict': controlnet_mlsd_predict
}
return variables
|