Spaces:
Runtime error
Runtime error
File size: 5,896 Bytes
9554351 6523000 9554351 6523000 9554351 5470672 9554351 5470672 9554351 5470672 9554351 5470672 9554351 b8738f1 9554351 b8738f1 9554351 b8738f1 9554351 6523000 9554351 14547da 9554351 14547da 9554351 14547da 9554351 5470672 9554351 5470672 9554351 |
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 |
import torch
import spaces
import random
import os
import numpy as np
import gradio as gr
from PIL import Image
from diffusers import FluxControlNetPipeline, FluxControlNetModel
from diffusers.models import FluxMultiControlNetModel
from controlnet_aux import (
CannyDetector,
MidasDetector,
)
from huggingface_hub import login
USE_ZERO_GPU = os.environ.get("USE_ZERO_GPU", "0") == "1"
HF_TOKEN = os.environ.get("HF_TOKEN")
if HF_TOKEN:
login(token=HF_TOKEN)
MAX_SEED = np.iinfo(np.int32).max
MAX_SIZE = 1024
styles = [
"3D Animation",
"Maomu Ghibli",
]
device = "cuda" if torch.cuda.is_available() else "cpu"
base_model = 'black-forest-labs/FLUX.1-dev'
controlnet_model_union = 'Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro'
controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union, torch_dtype=torch.bfloat16)
controlnet = FluxMultiControlNetModel([controlnet_union]) # we always recommend loading via FluxMultiControlNetModel
pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
pipe.to(device)
pipe.unload_lora_weights()
pipe.load_lora_weights(
"vzhizhi6611/OminiControlArt",
weight_name=f"v0/3d_animation.safetensors",
adapter_name="3d_animation",
)
pipe.load_lora_weights(
"vzhizhi6611/OminiControlArt",
weight_name=f"v0/maomu_ghibli.safetensors",
adapter_name="maomu_ghibli",
)
canny_detector = CannyDetector()
midas_detector = MidasDetector.from_pretrained(
"valhalla/t2iadapter-aux-models", filename="dpt_large_384.pt", model_type="dpt_large"
)
midas_detector = midas_detector.to(device)
def infer(
input_image,
prompt,
style,
num_inference_steps=24,
guidance_scale=3.5,
seed=42,
randomize_seed=False,
canny_weight=0.2,
depth_weight=0.4,
canny_detect=0.375,
depth_detect=0.5,
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
# Set Adapter
activate_adapter_name = {
"3D Animation": "3d_animation",
"Maomu Ghibli": "maomu_ghibli",
}[style]
pipe.set_adapters(activate_adapter_name)
control_mode_depth = 2
control_mode_canny = 0
w, h = input_image.size
factor = max(w, h) / MAX_SIZE
width = int(w / factor)
height = int(h / factor)
input_image = input_image.resize((width, height), Image.LANCZOS)
canny_image = canny_detector(input_image, detect_resolution=int(MAX_SIZE * canny_detect), image_resolution=MAX_SIZE)
depth_image = midas_detector(input_image, detect_resolution=int(MAX_SIZE * depth_detect), image_resolution=MAX_SIZE)
control_image = []
control_mode = []
controlnet_conditioning_scale = []
if depth_weight > 0:
control_mode.append(control_mode_depth)
controlnet_conditioning_scale.append(depth_weight)
control_image.append(depth_image)
if canny_weight > 0:
control_mode.append(control_mode_canny)
controlnet_conditioning_scale.append(canny_weight)
control_image.append(canny_image)
result_image = pipe(
prompt,
control_image=control_image,
control_mode=control_mode,
width=width,
height=height,
controlnet_conditioning_scale=controlnet_conditioning_scale,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
generator=torch.Generator().manual_seed(seed),
).images[0]
return result_image, canny_image, depth_image, seed
if USE_ZERO_GPU:
infer = spaces.GPU(infer, duration=30)
def create_demo() -> gr.Blocks:
with gr.Blocks() as demo:
cropper = gr.State()
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt", lines=1, value="3d animation style selfie")
num_inference_steps = gr.Slider(minimum=1, maximum=100, value=24, step=1, label="Num Inference Steps")
guidance_scale = gr.Slider(minimum=0, maximum=20, value=3.5, step=0.5, label="Guidance Scale")
with gr.Accordion("Advanced Options", open=False):
canny_weight = gr.Slider(minimum=0, maximum=1, value=0.3, step=0.01, label="Canny Weight")
depth_weight = gr.Slider(minimum=0, maximum=1, value=0.6, step=0.01, label="Depth Weight")
canny_detect = gr.Slider(minimum=0.1, maximum=1, value=0.375, step=0.025, label="Canny Detect")
depth_detect = gr.Slider(minimum=0.1, maximum=1, value=0.375, step=0.025, label="Depth Detect")
with gr.Column():
seed = gr.Number(label="Seed", value=42)
randomize_seed = gr.Checkbox(label="Randomize Seed", value=False)
style = gr.Dropdown(label="Style", choices=styles, value=styles[0])
g_btn = gr.Button("Generate Image")
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Input Image", type="pil", interactive=True)
canny_image = gr.Image(label="Canny Image", type="pil", interactive=False)
with gr.Column():
result_image = gr.Image(label="Result Image", type="pil", interactive=False)
depth_image = gr.Image(label="Depth Image", type="pil", interactive=False)
seed_output = gr.Number(label="Seed Output", interactive=False)
g_btn.click(
fn=infer,
inputs=[
input_image,
prompt,
style,
num_inference_steps,
guidance_scale,
seed,
randomize_seed,
canny_weight,
depth_weight,
canny_detect,
depth_detect
],
outputs=[result_image, canny_image, depth_image, seed_output],
)
return demo |