File size: 6,319 Bytes
37112ef |
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 |
import spaces
import gradio as gr
from huggingface_hub import ModelCard
from modules.helpers.common_helpers import ControlNetReq, BaseReq, BaseImg2ImgReq, BaseInpaintReq
from modules.helpers.sdxl_helpers import gen_img
from config import sdxl_loras
loras = sdxl_loras
# Event functions
def update_fast_generation(fast_generation):
if fast_generation:
return (
gr.update(
value=0.0
),
gr.update(
value=8
)
)
else:
return (
gr.update(
value=7.0
),
gr.update(
value=20
)
)
def add_to_enabled_loras(selected_lora, enabled_loras):
lora_data = loras
try:
selected_lora = int(selected_lora)
if 0 <= selected_lora: # is the index of the lora in the gallery
lora_info = lora_data[selected_lora]
enabled_loras.append({
"repo_id": lora_info["repo"],
"trigger_word": lora_info["trigger_word"]
})
except ValueError:
link = selected_lora.split("/")
if len(link) == 2:
model_card = ModelCard.load(selected_lora)
trigger_word = model_card.data.get("instance_prompt", "")
enabled_loras.append({
"repo_id": selected_lora,
"trigger_word": trigger_word
})
return (
gr.update( # selected_lora
value=""
),
gr.update( # custom_lora_info
value="",
visible=False
),
gr.update( # enabled_loras
value=enabled_loras
)
)
@spaces.GPU(duration=75)
def generate_image(
model, prompt, negative_prompt, fast_generation, enabled_loras, enabled_embeddings, # type: ignore
lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5, # type: ignore
img2img_image, inpaint_image, canny_image, pose_image, depth_image, scribble_image, # type: ignore
img2img_strength, inpaint_strength, canny_strength, pose_strength, depth_strength, scribble_strength, # type: ignore
resize_mode,
scheduler, image_height, image_width, image_num_images_per_prompt, # type: ignore
image_num_inference_steps, image_clip_skip, image_guidance_scale, image_seed, # type: ignore
refiner, vae
):
try:
base_args = {
"model": model,
"prompt": prompt,
"negative_prompt": negative_prompt,
"fast_generation": fast_generation,
"loras": None,
"embeddings": None,
"resize_mode": resize_mode,
"scheduler": scheduler,
"height": image_height,
"width": image_width,
"num_images_per_prompt": image_num_images_per_prompt,
"num_inference_steps": image_num_inference_steps,
"clip_skip": image_clip_skip,
"guidance_scale": image_guidance_scale,
"seed": image_seed,
"refiner": refiner,
"vae": vae,
"controlnet_config": None,
}
base_args = BaseReq(**base_args)
if len(enabled_loras) > 0:
base_args.loras = []
for enabled_lora, slider in zip(enabled_loras, [lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5]):
if enabled_lora["repo_id"]:
base_args.loras.append({
"repo_id": enabled_lora["repo_id"],
"weight": slider
})
if len(enabled_embeddings) > 0:
base_args.embeddings = enabled_embeddings
image = None
mask_image = None
strength = None
if img2img_image:
image = img2img_image
strength = float(img2img_strength)
base_args = BaseImg2ImgReq(
**base_args.__dict__,
image=image,
strength=strength
)
elif inpaint_image:
image = inpaint_image['background'] if not all(pixel == (0, 0, 0) for pixel in list(inpaint_image['background'].getdata())) else None
mask_image = inpaint_image['layers'][0] if image else None
strength = float(inpaint_strength)
if image and mask_image:
base_args = BaseInpaintReq(
**base_args.__dict__,
image=image,
mask_image=mask_image,
strength=strength
)
elif any([canny_image, pose_image, depth_image]):
base_args.controlnet_config = ControlNetReq(
controlnets=[],
control_images=[],
controlnet_conditioning_scale=[]
)
if canny_image:
base_args.controlnet_config.controlnets.append("canny")
base_args.controlnet_config.control_images.append(canny_image)
base_args.controlnet_config.controlnet_conditioning_scale.append(float(canny_strength))
if pose_image:
base_args.controlnet_config.controlnets.append("pose")
base_args.controlnet_config.control_images.append(pose_image)
base_args.controlnet_config.controlnet_conditioning_scale.append(float(pose_strength))
if depth_image:
base_args.controlnet_config.controlnets.append("depth")
base_args.controlnet_config.control_images.append(depth_image)
base_args.controlnet_config.controlnet_conditioning_scale.append(float(depth_strength))
if scribble_image:
base_args.controlnet_config.controlnets.append("scribble")
base_args.controlnet_config.control_images.append(scribble_image)
base_args.controlnet_config.controlnet_conditioning_scale.append(float(scribble_strength))
else:
base_args = BaseReq(**base_args.__dict__)
return gr.update(
value=gen_img(base_args),
interactive=True
)
except Exception as e:
raise gr.Error(f"Error: {e}") from e
|