File size: 7,552 Bytes
fc3d45b 02d63c6 b7362f5 95f0824 02d63c6 2c19b8d 02d63c6 b7362f5 9080283 02d63c6 b7362f5 02d63c6 6cfd3ee b7362f5 6cfd3ee b7362f5 02d63c6 b7362f5 12ced3a b7362f5 2c19b8d b7362f5 12ced3a b7362f5 2c19b8d b7362f5 02d63c6 b7362f5 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 |
import spaces
import gradio as gr
import torch
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel
from tdd_scheduler import TDDScheduler
from safetensors.torch import load_file
from PIL import Image
import transformers
transformers.utils.move_cache()
SAFETY_CHECKER = True
loaded_acc = None
device = "cuda"
ACC_lora={
"TDD":"sdxl_tdd_wo_adv_lora.safetensors",
"TDD_adv":"sdxl_tdd_lora_weights.safetensors",
}
if torch.cuda.is_available():
base1 = UNet2DConditionModel.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet", torch_dtype=torch.float16
).to(device)
base2 = UNet2DConditionModel.from_pretrained(
"frankjoshua/realvisxlV40_v40Bakedvae", subfolder="unet", torch_dtype=torch.float16
).to(device)
pipe_sdxl = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
unet=base1,
torch_dtype=torch.float16,
variant="fp16",
).to(device)
pipe_sdxl.load_lora_weights("RED-AIGC/TDD", weight_name=ACC_lora["TDD"], adapter_name="TDD")
pipe_sdxl.load_lora_weights("RED-AIGC/TDD", weight_name=ACC_lora["TDD_adv"], adapter_name="TDD_adv")
pipe_sdxl.scheduler = TDDScheduler.from_config(pipe_sdxl.scheduler.config)
pipe_sdxl_real = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
unet=base2,
torch_dtype=torch.float16,
variant="fp16",
).to(device)
pipe_sdxl_real.load_lora_weights("RED-AIGC/TDD", weight_name=ACC_lora["TDD"], adapter_name="TDD")
pipe_sdxl_real.load_lora_weights("RED-AIGC/TDD", weight_name=ACC_lora["TDD_adv"], adapter_name="TDD_adv")
pipe_sdxl_real.scheduler = TDDScheduler.from_config(pipe_sdxl.scheduler.config)
def update_base_model(ckpt):
if torch.cuda.is_available():
pipe_sdxl = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
).to(device)
return pipe_sdxl
if SAFETY_CHECKER:
from safety_checker import StableDiffusionSafetyChecker
from transformers import CLIPFeatureExtractor
safety_checker = StableDiffusionSafetyChecker.from_pretrained(
"CompVis/stable-diffusion-safety-checker"
).to(device)
feature_extractor = CLIPFeatureExtractor.from_pretrained(
"openai/clip-vit-base-patch32"
)
def check_nsfw_images(
images: list[Image.Image],
) -> tuple[list[Image.Image], list[bool]]:
safety_checker_input = feature_extractor(images, return_tensors="pt").to(device)
has_nsfw_concepts = safety_checker(
images=[images], clip_input=safety_checker_input.pixel_values.to(device)
)
return images, has_nsfw_concepts
@spaces.GPU(enable_queue=True, duration=3)
def generate_image(
prompt,
negative_prompt,
ckpt,
acc,
num_inference_steps,
guidance_scale,
eta,
seed,
progress=gr.Progress(track_tqdm=True),
):
global loaded_acc
#pipe = pipe_sdxl #if mode == "sdxl" else pipe_sd15
if ckpt == "Real":
pipe = pipe_sdxl_real
else:
pipe = pipe_sdxl
if loaded_acc != acc:
#pipe.load_lora_weights(ACC_lora[acc], adapter_name=acc)
pipe.set_adapters([acc], adapter_weights=[1.0])
print(pipe.get_active_adapters())
loaded_acc = acc
results = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
eta=eta,
generator=torch.Generator(device=pipe.device).manual_seed(seed),
)
if SAFETY_CHECKER:
images, has_nsfw_concepts = check_nsfw_images(results.images)
if any(has_nsfw_concepts):
gr.Warning("NSFW content detected.")
return Image.new("RGB", (512, 512))
return images[0]
return results.images[0]
css = """
h1 {
text-align: center;
display:block;
}
.gradio-container {
max-width: 70.5rem !important;
}
"""
with gr.Blocks(css=css) as demo:
gr.Markdown(
"""
# ✨Target-Driven Distillation✨
Target-Driven Distillation (TDD) is a state-of-the-art consistency distillation model that largely accelerates the inference processes of diffusion models. Using its delicate strategies of *target timestep selection* and *decoupled guidance*, models distilled by TDD can generated highly detailed images with only a few steps.
[](https://arxiv.org) [](https://huggingface.co/RedAIGC/TDD)
"""
)
with gr.Row():
with gr.Column(scale=1):
with gr.Group():
with gr.Row():
prompt = gr.Textbox(label="Prompt")
with gr.Row():
negative_prompt = gr.Textbox(label="Negative Prompt")
with gr.Row():
steps = gr.Slider(
label="Sampling Steps",
minimum=4,
maximum=8,
step=1,
value=4,
)
with gr.Row():
guidance_scale = gr.Slider(
label="CFG Scale",
minimum=1,
maximum=4,
step=0.1,
value=2.0,
)
with gr.Row():
eta = gr.Slider(
label="eta",
minimum=0,
maximum=0.3,
step=0.01,
value=0.2,
)
with gr.Row():
seed = gr.Number(label="Seed", value=-1)
with gr.Row():
ckpt = gr.Dropdown(
label="Base Model",
choices=["SDXL-1.0", "Real"],
value="SDXL-1.0",
)
acc = gr.Dropdown(
label="Accelerate Lora",
choices=["TDD", "TDD_adv"],
value="TDD_adv",
)
with gr.Column(scale=1):
with gr.Group():
img = gr.Image(label="TDD Image", value="cat.png")
submit_sdxl = gr.Button("Run on SDXL")
gr.Examples(
examples=[
["A photo of a cat made of water.", "", "SDXL-1.0", "TDD_adv", 4, 1.7, 0.2, 546237],
["Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", "NSFW, low quality, low resolution, normal quality", "Real", "TDD_adv", 7, 2.0, 0.3, 68436]
["panda mad scientist mixing sparkling chemicals", "", "Real", "TDD_adv", 5, 1.7, 0.2, 3853],
["a dog in snow, 8k", "", "SDXL-1.0", "TDD", 6, 1.3, 0.2, 12138]
],
inputs=[prompt, negative_prompt, ckpt, acc, steps, guidance_scale, eta, seed],
outputs=[img],
fn=generate_image,
cache_examples="lazy",
)
gr.on(
fn=generate_image,
triggers=[ckpt.change, prompt.submit, submit_sdxl.click],
inputs=[prompt, negative_prompt, ckpt, acc, steps, guidance_scale, eta, seed],
outputs=[img],
)
demo.queue(api_open=False).launch(show_api=False) |