Spaces:
Runtime error
Runtime error
File size: 5,588 Bytes
9f9752e f2467d5 0525e06 f2467d5 9f9752e 0525e06 9f9752e f2467d5 9f9752e c74fa4c 0b96e40 892985e a10a711 9f9752e 0b96e40 9f9752e c74fa4c 9f9752e 0b96e40 9f9752e |
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 |
from PIL import Image
import gradio as gr
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
import torch
torch.backends.cuda.matmul.allow_tf32 = True
import gc
model_id = "andite/pastel-mix"
model_url = "https://huggingface.co/andite/pastel-mix/blob/main/pastelmix-better-vae.ckpt"
controlnet = ControlNetModel.from_pretrained("ioclab/connow", torch_dtype=torch.float16, use_safetensors=True)
pipe = StableDiffusionControlNetPipeline.from_from_ckpt(
model_url,
controlnet=controlnet,
torch_dtype=torch.float16,
safety_checker=None,
)
pipe = pipe.to("cuda")
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()
pipe.enable_attention_slicing()
def infer(
prompt,
negative_prompt,
conditioning_image,
num_inference_steps=30,
size=768,
guidance_scale=7.0,
seed=1234,
ill=0.6,
):
conditioning_image_raw = Image.fromarray(conditioning_image)
conditioning_image = conditioning_image_raw.convert('L')
w = conditioning_image.width
h = conditioning_image.height
ratio = 768 / max(w, h)
w = int(w * ratio)
h = int(h * ratio)
g_cpu = torch.Generator()
if seed == -1:
generator = g_cpu.manual_seed(g_cpu.seed())
else:
generator = g_cpu.manual_seed(seed)
output_image = pipe(
prompt,
conditioning_image,
height=h,
width=w,
num_inference_steps=num_inference_steps,
generator=generator,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
controlnet_conditioning_scale=ill,
).images[0]
del conditioning_image, conditioning_image_raw
gc.collect()
return output_image
with gr.Blocks() as demo:
gr.Markdown(
"""
# ControlNet on Brightness
This is a demo on ControlNet based on brightness.
""")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(
label="Prompt",
)
negative_prompt = gr.Textbox(
label="Negative Prompt",
)
conditioning_image = gr.Image(
label="Conditioning Image",
)
with gr.Accordion('Advanced options', open=False):
with gr.Row():
num_inference_steps = gr.Slider(
10, 40, 20,
step=1,
label="Steps",
)
size = gr.Slider(
256, 768, 512,
step=128,
label="Size",
)
with gr.Row():
guidance_scale = gr.Slider(
label='Guidance Scale',
minimum=0.1,
maximum=30.0,
value=7.0,
step=0.1
)
seed = gr.Slider(
label='Seed',
value=-1,
minimum=-1,
maximum=2147483647,
step=1,
# randomize=True
)
with gr.Row():
ill = gr.Slider(
label='controlnet_ILL_scale',
minimum=0,
maximum=1,
value=0.6,
step=0.05
)
submit_btn = gr.Button(
value="Submit",
variant="primary"
)
with gr.Column(min_width=300):
output = gr.Image(
label="Result",
)
submit_btn.click(
fn=infer,
inputs=[
prompt, negative_prompt, conditioning_image, num_inference_steps, size, guidance_scale, seed,ill,
],
outputs=output
)
gr.Examples(
examples=[
["masterpiece, best quality, High contrast,A bamboo forest, a stream,The rising sun, colorful,", "((nsfw)),(blush),(bare),(worst quality:2, low quality:2),(zombie, sketch, interlocked fingers), greyscale", "./conditioning_images/ty1.jpg","./conditioning_images/tyt1.jpg"],
["masterpiece, best quality, High contrast,A bamboo forest, a stream,The rising sun, colorful,", "((nsfw)),(blush),(bare),(worst quality:2, low quality:2),(zombie, sketch, interlocked fingers), greyscale", "./conditioning_images/ty2.jpg","./conditioning_images/tyt2.jpg"],
],
inputs=[
prompt, negative_prompt, conditioning_image
],
outputs=output,
fn=infer,
cache_examples=True,
)
gr.Markdown(
"""
* [Dataset](https://huggingface.co/datasets/ioclab/grayscale_image_aesthetic_3M) Note that this was handled extra, and a preview version of the processing is here
[Anime Dataset](https://huggingface.co/datasets/ioclab/lighttestout) [Nature Dataset] (https://huggingface.co/datasets/ioclab/light)
* [Diffusers model](https://huggingface.co/ioclab/connow/tree/main), [Web UI model](https://huggingface.co/ioclab/control_v1u_sd15_illumination_webui)
* [Training Report](https://huggingface.co/ioclab/control_v1u_sd15_illumination_webui), [Doc(Chinese)](https://aigc.ioclab.com/sd-showcase/light_controlnet.html)
""")
demo.launch() |