# Edit Anything trained with Stable Diffusion + ControlNet + SAM + BLIP2
from torchvision.utils import save_image
from PIL import Image
from pytorch_lightning import seed_everything
import subprocess
from collections import OrderedDict
import re
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
import os
import requests
from io import BytesIO
from annotator.util import resize_image, HWC3, resize_points, get_bounding_box
import torch
from safetensors.torch import load_file
from collections import defaultdict
from diffusers import StableDiffusionControlNetPipeline
from diffusers import ControlNetModel, UniPCMultistepScheduler
from utils.stable_diffusion_controlnet import ControlNetModel2
from utils.stable_diffusion_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline, \
StableDiffusionControlNetInpaintMixingPipeline, prepare_mask_image
# need the latest transformers
# pip install git+https://github.com/huggingface/transformers.git
from transformers import AutoProcessor, Blip2ForConditionalGeneration
from diffusers import ControlNetModel, DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
import PIL.Image
# Segment-Anything init.
# pip install git+https://github.com/facebookresearch/segment-anything.git
try:
from segment_anything import (
sam_model_registry,
SamAutomaticMaskGenerator,
SamPredictor,
)
except ImportError:
print("segment_anything not installed")
result = subprocess.run(
[
"pip",
"install",
"git+https://github.com/facebookresearch/segment-anything.git",
],
check=True,
)
print(f"Install segment_anything {result}")
from segment_anything import (
sam_model_registry,
SamAutomaticMaskGenerator,
SamPredictor,
)
if not os.path.exists("./models/sam_vit_h_4b8939.pth"):
result = subprocess.run(
[
"wget",
"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
"-P",
"models",
],
check=True,
)
print(f"Download sam_vit_h_4b8939.pth {result}")
device = "cuda" if torch.cuda.is_available() else "cpu"
config_dict = OrderedDict(
[
("LAION Pretrained(v0-4)-SD15", "shgao/edit-anything-v0-4-sd15"),
("LAION Pretrained(v0-4)-SD21", "shgao/edit-anything-v0-4-sd21"),
("LAION Pretrained(v0-3)-SD21", "shgao/edit-anything-v0-3"),
("SAM Pretrained(v0-1)-SD21", "shgao/edit-anything-v0-1-1"),
]
)
def init_sam_model(sam_generator=None, mask_predictor=None):
if sam_generator is not None and mask_predictor is not None:
return sam_generator, mask_predictor
sam_checkpoint = "models/sam_vit_h_4b8939.pth"
model_type = "default"
sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
sam.to(device=device)
sam_generator = (
SamAutomaticMaskGenerator(
sam) if sam_generator is None else sam_generator
)
mask_predictor = SamPredictor(
sam) if mask_predictor is None else mask_predictor
return sam_generator, mask_predictor
def init_blip_processor():
blip_processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
return blip_processor
def init_blip_model():
blip_model = Blip2ForConditionalGeneration.from_pretrained(
"Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16, device_map="auto"
)
return blip_model
def get_pipeline_embeds(pipeline, prompt, negative_prompt, device):
# https://github.com/huggingface/diffusers/issues/2136
"""Get pipeline embeds for prompts bigger than the maxlength of the pipe
:param pipeline:
:param prompt:
:param negative_prompt:
:param device:
:return:
"""
max_length = pipeline.tokenizer.model_max_length
# simple way to determine length of tokens
count_prompt = len(re.split(r", ", prompt))
count_negative_prompt = len(re.split(r", ", negative_prompt))
# create the tensor based on which prompt is longer
if count_prompt >= count_negative_prompt:
input_ids = pipeline.tokenizer(
prompt, return_tensors="pt", truncation=False
).input_ids.to(device)
shape_max_length = input_ids.shape[-1]
negative_ids = pipeline.tokenizer(
negative_prompt,
truncation=False,
padding="max_length",
max_length=shape_max_length,
return_tensors="pt",
).input_ids.to(device)
else:
negative_ids = pipeline.tokenizer(
negative_prompt, return_tensors="pt", truncation=False
).input_ids.to(device)
shape_max_length = negative_ids.shape[-1]
input_ids = pipeline.tokenizer(
prompt,
return_tensors="pt",
truncation=False,
padding="max_length",
max_length=shape_max_length,
).input_ids.to(device)
concat_embeds = []
neg_embeds = []
for i in range(0, shape_max_length, max_length):
concat_embeds.append(pipeline.text_encoder(input_ids[:, i : i + max_length])[0])
neg_embeds.append(pipeline.text_encoder(negative_ids[:, i : i + max_length])[0])
return torch.cat(concat_embeds, dim=1), torch.cat(neg_embeds, dim=1)
def load_lora_weights(pipeline, checkpoint_path, multiplier, device, dtype):
LORA_PREFIX_UNET = "lora_unet"
LORA_PREFIX_TEXT_ENCODER = "lora_te"
# load LoRA weight from .safetensors
print('device: {}'.format(device))
if isinstance(checkpoint_path, str):
state_dict = load_file(checkpoint_path, device=device)
updates = defaultdict(dict)
for key, value in state_dict.items():
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
layer, elem = key.split(".", 1)
updates[layer][elem] = value
# directly update weight in diffusers model
for layer, elems in updates.items():
if "text" in layer:
layer_infos = layer.split(LORA_PREFIX_TEXT_ENCODER + "_")[-1].split("_")
curr_layer = pipeline.text_encoder
else:
layer_infos = layer.split(LORA_PREFIX_UNET + "_")[-1].split("_")
curr_layer = pipeline.unet
# find the target layer
temp_name = layer_infos.pop(0)
while len(layer_infos) > -1:
try:
curr_layer = curr_layer.__getattr__(temp_name)
if len(layer_infos) > 0:
temp_name = layer_infos.pop(0)
elif len(layer_infos) == 0:
break
except Exception:
if len(temp_name) > 0:
temp_name += "_" + layer_infos.pop(0)
else:
temp_name = layer_infos.pop(0)
# get elements for this layer
weight_up = elems["lora_up.weight"].to(dtype)
weight_down = elems["lora_down.weight"].to(dtype)
alpha = elems["alpha"]
if alpha:
alpha = alpha.item() / weight_up.shape[1]
else:
alpha = 1.0
# update weight
if len(weight_up.shape) == 4:
curr_layer.weight.data += (
multiplier
* alpha
* torch.mm(
weight_up.squeeze(3).squeeze(2),
weight_down.squeeze(3).squeeze(2),
)
.unsqueeze(2)
.unsqueeze(3)
)
else:
curr_layer.weight.data += (
multiplier * alpha * torch.mm(weight_up, weight_down)
)
else:
for ckptpath in checkpoint_path:
state_dict = load_file(ckptpath, device=device)
updates = defaultdict(dict)
for key, value in state_dict.items():
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
layer, elem = key.split(".", 1)
updates[layer][elem] = value
# directly update weight in diffusers model
for layer, elems in updates.items():
if "text" in layer:
layer_infos = layer.split(LORA_PREFIX_TEXT_ENCODER + "_")[-1].split(
"_"
)
curr_layer = pipeline.text_encoder
else:
layer_infos = layer.split(LORA_PREFIX_UNET + "_")[-1].split("_")
curr_layer = pipeline.unet
# find the target layer
temp_name = layer_infos.pop(0)
while len(layer_infos) > -1:
try:
curr_layer = curr_layer.__getattr__(temp_name)
if len(layer_infos) > 0:
temp_name = layer_infos.pop(0)
elif len(layer_infos) == 0:
break
except Exception:
if len(temp_name) > 0:
temp_name += "_" + layer_infos.pop(0)
else:
temp_name = layer_infos.pop(0)
# get elements for this layer
weight_up = elems["lora_up.weight"].to(dtype)
weight_down = elems["lora_down.weight"].to(dtype)
alpha = elems["alpha"]
if alpha:
alpha = alpha.item() / weight_up.shape[1]
else:
alpha = 1.0
# update weight
if len(weight_up.shape) == 4:
curr_layer.weight.data += (
multiplier
* alpha
* torch.mm(
weight_up.squeeze(3).squeeze(2),
weight_down.squeeze(3).squeeze(2),
)
.unsqueeze(2)
.unsqueeze(3)
)
else:
curr_layer.weight.data += (
multiplier * alpha * torch.mm(weight_up, weight_down)
)
return pipeline
def make_inpaint_condition(image, image_mask):
image = image / 255.0
assert (
image.shape[0:1] == image_mask.shape[0:1]
), "image and image_mask must have the same image size"
image[image_mask > 128] = -1.0 # set as masked pixel
image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return image
def obtain_generation_model(
base_model_path,
lora_model_path,
controlnet_path,
generation_only=False,
extra_inpaint=True,
lora_weight=1.0,
):
controlnet = []
controlnet.append(
ControlNetModel2.from_pretrained(
controlnet_path, torch_dtype=torch.float16)
) # sam control
if (not generation_only) and extra_inpaint: # inpainting control
print("Warning: ControlNet based inpainting model only support SD1.5 for now.")
controlnet.append(
ControlNetModel.from_pretrained(
"lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16
) # inpainting controlnet
)
if generation_only and extra_inpaint:
pipe = StableDiffusionControlNetPipeline.from_pretrained(
base_model_path,
controlnet=controlnet,
torch_dtype=torch.float16,
safety_checker=None,
)
else:
pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
base_model_path,
controlnet=controlnet,
torch_dtype=torch.float16,
safety_checker=None,
)
if lora_model_path is not None:
pipe = load_lora_weights(
pipe, [lora_model_path], lora_weight, "cpu", torch.float32
)
# speed up diffusion process with faster scheduler and memory optimization
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
# remove following line if xformers is not installed
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()
return pipe
def obtain_tile_model(base_model_path, lora_model_path, lora_weight=1.0):
controlnet = ControlNetModel2.from_pretrained(
"lllyasviel/control_v11f1e_sd15_tile", torch_dtype=torch.float16
) # tile controlnet
if (
base_model_path == "runwayml/stable-diffusion-v1-5"
or base_model_path == "stabilityai/stable-diffusion-2-inpainting"
):
print("base_model_path", base_model_path)
pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
controlnet=controlnet,
torch_dtype=torch.float16,
safety_checker=None,
)
else:
pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
base_model_path,
controlnet=controlnet,
torch_dtype=torch.float16,
safety_checker=None,
)
if lora_model_path is not None:
pipe = load_lora_weights(
pipe, [lora_model_path], lora_weight, "cpu", torch.float32
)
# speed up diffusion process with faster scheduler and memory optimization
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
# remove following line if xformers is not installed
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()
return pipe
def show_anns(anns):
if len(anns) == 0:
return
sorted_anns = sorted(anns, key=(lambda x: x["area"]), reverse=True)
full_img = None
# for ann in sorted_anns:
for i in range(len(sorted_anns)):
ann = anns[i]
m = ann["segmentation"]
if full_img is None:
full_img = np.zeros((m.shape[0], m.shape[1], 3))
map = np.zeros((m.shape[0], m.shape[1]), dtype=np.uint16)
map[m != 0] = i + 1
color_mask = np.random.random((1, 3)).tolist()[0]
full_img[m != 0] = color_mask
full_img = full_img * 255
# anno encoding from https://github.com/LUSSeg/ImageNet-S
res = np.zeros((map.shape[0], map.shape[1], 3))
res[:, :, 0] = map % 256
res[:, :, 1] = map // 256
res.astype(np.float32)
full_img = Image.fromarray(np.uint8(full_img))
return full_img, res
class EditAnythingLoraModel:
def __init__(
self,
base_model_path="../chilloutmix_NiPrunedFp32Fix",
lora_model_path="../40806/mix4",
use_blip=True,
blip_processor=None,
blip_model=None,
sam_generator=None,
controlmodel_name="LAION Pretrained(v0-4)-SD15",
# used when the base model is not an inpainting model.
extra_inpaint=True,
tile_model=None,
lora_weight=1.0,
alpha_mixing=None,
mask_predictor=None,
):
self.device = device
self.use_blip = use_blip
# Diffusion init using diffusers.
self.default_controlnet_path = config_dict[controlmodel_name]
self.base_model_path = base_model_path
self.lora_model_path = lora_model_path
self.defalut_enable_all_generate = False
self.extra_inpaint = extra_inpaint
self.last_ref_infer = False
self.pipe = obtain_generation_model(
base_model_path,
lora_model_path,
self.default_controlnet_path,
generation_only=False,
extra_inpaint=extra_inpaint,
lora_weight=lora_weight,
)
# self.pipe.load_textual_inversion("textual_inversion_cat/learned_embeds.bin")
# Segment-Anything init.
self.sam_generator, self.mask_predictor = init_sam_model(
sam_generator, mask_predictor
)
# BLIP2 init.
if use_blip:
if blip_processor is not None:
self.blip_processor = blip_processor
else:
self.blip_processor = init_blip_processor()
if blip_model is not None:
self.blip_model = blip_model
else:
self.blip_model = init_blip_model()
# tile model init.
if tile_model is not None:
self.tile_pipe = tile_model
else:
self.tile_pipe = obtain_tile_model(
base_model_path, lora_model_path, lora_weight=lora_weight
)
def get_blip2_text(self, image):
inputs = self.blip_processor(image, return_tensors="pt").to(
self.device, torch.float16
)
generated_ids = self.blip_model.generate(**inputs, max_new_tokens=50)
generated_text = self.blip_processor.batch_decode(
generated_ids, skip_special_tokens=True
)[0].strip()
return generated_text
def get_sam_control(self, image):
masks = self.sam_generator.generate(image)
full_img, res = show_anns(masks)
return full_img, res
def get_click_mask(self, image, clicked_points):
self.mask_predictor.set_image(image)
# Separate the points and labels
points, labels = zip(*[(point[:2], point[2])
for point in clicked_points])
# Convert the points and labels to numpy arrays
input_point = np.array(points)
input_label = np.array(labels)
masks, _, _ = self.mask_predictor.predict(
point_coords=input_point,
point_labels=input_label,
multimask_output=False,
)
return masks
@torch.inference_mode()
def process_image_click(
self,
original_image: gr.Image,
point_prompt: gr.Radio,
clicked_points: gr.State,
image_resolution,
evt: gr.SelectData,
):
# Get the clicked coordinates
clicked_coords = evt.index
x, y = clicked_coords
label = point_prompt
lab = 1 if label == "Foreground Point" else 0
clicked_points.append((x, y, lab))
input_image = np.array(original_image, dtype=np.uint8)
H, W, C = input_image.shape
input_image = HWC3(input_image)
img = resize_image(input_image, image_resolution)
# Update the clicked_points
resized_points = resize_points(
clicked_points, input_image.shape, image_resolution
)
mask_click_np = self.get_click_mask(img, resized_points)
# Convert mask_click_np to HWC format
mask_click_np = np.transpose(mask_click_np, (1, 2, 0)) * 255.0
mask_image = HWC3(mask_click_np.astype(np.uint8))
mask_image = cv2.resize(mask_image, (W, H), interpolation=cv2.INTER_LINEAR)
# mask_image = Image.fromarray(mask_image_tmp)
# Draw circles for all clicked points
edited_image = input_image
for x, y, lab in clicked_points:
# Set the circle color based on the label
color = (255, 0, 0) if lab == 1 else (0, 0, 255)
# Draw the circle
edited_image = cv2.circle(edited_image, (x, y), 20, color, -1)
# Set the opacity for the mask_image and edited_image
opacity_mask = 0.75
opacity_edited = 1.0
# Combine the edited_image and the mask_image using cv2.addWeighted()
overlay_image = cv2.addWeighted(
edited_image,
opacity_edited,
(mask_image *
np.array([0 / 255, 255 / 255, 0 / 255])).astype(np.uint8),
opacity_mask,
0,
)
return (
Image.fromarray(overlay_image),
clicked_points,
Image.fromarray(mask_image),
)
@torch.inference_mode()
def process(
self,
source_image,
enable_all_generate,
mask_image,
control_scale,
enable_auto_prompt,
a_prompt,
n_prompt,
num_samples,
image_resolution,
detect_resolution,
ddim_steps,
guess_mode,
scale,
seed,
eta,
enable_tile=True,
refine_alignment_ratio=None,
refine_image_resolution=None,
alpha_weight=0.5,
use_scale_map=False,
condition_model=None,
ref_image=None,
attention_auto_machine_weight=1.0,
gn_auto_machine_weight=1.0,
style_fidelity=0.5,
reference_attn=True,
reference_adain=True,
ref_prompt=None,
ref_sam_scale=None,
ref_inpaint_scale=None,
ref_auto_prompt=False,
ref_textinv=True,
ref_textinv_path=None,
):
if condition_model is None or condition_model == "EditAnything":
this_controlnet_path = self.default_controlnet_path
else:
this_controlnet_path = condition_model
input_image = (
source_image["image"]
if isinstance(source_image, dict)
else np.array(source_image, dtype=np.uint8)
)
if mask_image is None:
if enable_all_generate != self.defalut_enable_all_generate:
self.pipe = obtain_generation_model(
self.base_model_path,
self.lora_model_path,
this_controlnet_path,
enable_all_generate,
self.extra_inpaint,
)
self.defalut_enable_all_generate = enable_all_generate
if enable_all_generate:
print(
"source_image",
source_image["mask"].shape,
input_image.shape,
)
mask_image = (
np.ones((input_image.shape[0],
input_image.shape[1], 3)) * 255
)
else:
mask_image = source_image["mask"]
else:
mask_image = np.array(mask_image, dtype=np.uint8)
if self.default_controlnet_path != this_controlnet_path:
print(
"To Use:",
this_controlnet_path,
"Current:",
self.default_controlnet_path,
)
print("Change condition model to:", this_controlnet_path)
self.pipe = obtain_generation_model(
self.base_model_path,
self.lora_model_path,
this_controlnet_path,
enable_all_generate,
self.extra_inpaint,
)
self.default_controlnet_path = this_controlnet_path
torch.cuda.empty_cache()
if self.last_ref_infer:
print("Redefine the model to overwrite the ref mode")
self.pipe = obtain_generation_model(
self.base_model_path,
self.lora_model_path,
this_controlnet_path,
enable_all_generate,
self.extra_inpaint,
)
self.last_ref_infer = False
if ref_image is not None:
ref_mask = ref_image["mask"]
ref_image = ref_image["image"]
if ref_auto_prompt or ref_textinv:
bbox = get_bounding_box(
np.array(ref_mask) / 255
) # reverse the mask to make 1 the choosen region
cropped_ref_mask = ref_mask.crop(
(bbox[0], bbox[1], bbox[2], bbox[3]))
cropped_ref_image = ref_image.crop(
(bbox[0], bbox[1], bbox[2], bbox[3]))
# cropped_ref_image.save("debug.jpg")
cropped_ref_image = np.array(cropped_ref_image) * (
np.array(cropped_ref_mask)[:, :, :3] / 255.0
)
cropped_ref_image = Image.fromarray(
cropped_ref_image.astype("uint8"))
if ref_auto_prompt:
generated_prompt = self.get_blip2_text(cropped_ref_image)
ref_prompt += generated_prompt
a_prompt += generated_prompt
print("Generated ref text:", ref_prompt)
print("Generated input text:", a_prompt)
self.last_ref_infer = True
# ref_image = cropped_ref_image
# ref_mask = cropped_ref_mask
if ref_textinv:
try:
self.pipe.load_textual_inversion(ref_textinv_path)
print("Load textinv embedding from:", ref_textinv_path)
except:
print("No textinvert embeddings found.")
ref_data_path = "./utils/tmp/textinv/img"
if not os.path.exists(ref_data_path):
os.makedirs(ref_data_path)
cropped_ref_image.save(os.path.join(ref_data_path, 'ref.png'))
print("Ref image region is save to:", ref_data_path)
print("Plese finetune with run_texutal_inversion.sh in utils folder to get the textinvert embeddings.")
else:
ref_mask = None
with torch.no_grad():
if self.use_blip and enable_auto_prompt:
print("Generating text:")
blip2_prompt = self.get_blip2_text(input_image)
print("Generated text:", blip2_prompt)
if len(a_prompt) > 0:
a_prompt = blip2_prompt + "," + a_prompt
else:
a_prompt = blip2_prompt
input_image = HWC3(input_image)
img = resize_image(input_image, image_resolution)
H, W, C = img.shape
print("Generating SAM seg:")
# the default SAM model is trained with 1024 size.
full_segmask, detected_map = self.get_sam_control(
resize_image(input_image, detect_resolution)
)
detected_map = HWC3(detected_map.astype(np.uint8))
detected_map = cv2.resize(
detected_map, (W, H), interpolation=cv2.INTER_LINEAR
)
control = torch.from_numpy(detected_map.copy()).float().cuda()
control = torch.stack([control for _ in range(num_samples)], dim=0)
control = einops.rearrange(control, "b h w c -> b c h w").clone()
mask_imag_ori = HWC3(mask_image.astype(np.uint8))
mask_image_tmp = cv2.resize(
mask_imag_ori, (W, H), interpolation=cv2.INTER_LINEAR
)
mask_image = Image.fromarray(mask_image_tmp)
if seed == -1:
seed = random.randint(0, 65535)
seed_everything(seed)
generator = torch.manual_seed(seed)
postive_prompt = a_prompt
negative_prompt = n_prompt
prompt_embeds, negative_prompt_embeds = get_pipeline_embeds(
self.pipe, postive_prompt, negative_prompt, "cuda"
)
prompt_embeds = torch.cat([prompt_embeds] * num_samples, dim=0)
negative_prompt_embeds = torch.cat(
[negative_prompt_embeds] * num_samples, dim=0
)
if enable_all_generate and self.extra_inpaint:
self.pipe.safety_checker = lambda images, clip_input: (
images, False)
if ref_image is not None:
print("Not support yet.")
return
x_samples = self.pipe(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
num_images_per_prompt=num_samples,
num_inference_steps=ddim_steps,
generator=generator,
height=H,
width=W,
image=[control.type(torch.float16)],
controlnet_conditioning_scale=[float(control_scale)],
guidance_scale=scale,
guess_mode=guess_mode,
).images
else:
multi_condition_image = []
multi_condition_scale = []
multi_condition_image.append(control.type(torch.float16))
multi_condition_scale.append(float(control_scale))
ref_multi_condition_scale = []
if ref_image is not None:
ref_multi_condition_scale.append(float(ref_sam_scale))
if self.extra_inpaint:
inpaint_image = make_inpaint_condition(img, mask_image_tmp)
multi_condition_image.append(
inpaint_image.type(torch.float16))
multi_condition_scale.append(1.0)
if ref_image is not None:
ref_multi_condition_scale.append(
float(ref_inpaint_scale))
if use_scale_map:
scale_map_tmp = source_image["mask"]
tmp = HWC3(scale_map_tmp.astype(np.uint8))
scale_map_tmp = cv2.resize(
tmp, (W, H), interpolation=cv2.INTER_LINEAR)
scale_map_tmp = Image.fromarray(scale_map_tmp)
controlnet_conditioning_scale_map = 1.0 - \
prepare_mask_image(scale_map_tmp).float()
print('scale map:', controlnet_conditioning_scale_map.size())
else:
controlnet_conditioning_scale_map = None
if isinstance(self.pipe, StableDiffusionControlNetInpaintMixingPipeline):
x_samples = self.pipe(
image=img,
mask_image=mask_image,
prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds,
num_images_per_prompt=num_samples,
num_inference_steps=ddim_steps,
generator=generator,
controlnet_conditioning_image=multi_condition_image,
height=H,
width=W,
controlnet_conditioning_scale=multi_condition_scale,
guidance_scale=scale,
alpha_weight=alpha_weight,
controlnet_conditioning_scale_map=controlnet_conditioning_scale_map
).images
else:
x_samples = self.pipe(
image=img,
mask_image=mask_image,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
num_images_per_prompt=num_samples,
num_inference_steps=ddim_steps,
generator=generator,
controlnet_conditioning_image=multi_condition_image,
height=H,
width=W,
controlnet_conditioning_scale=multi_condition_scale,
guidance_scale=scale,
ref_image=ref_image,
ref_mask=ref_mask,
ref_prompt=ref_prompt,
attention_auto_machine_weight=attention_auto_machine_weight,
gn_auto_machine_weight=gn_auto_machine_weight,
style_fidelity=style_fidelity,
reference_attn=reference_attn,
reference_adain=reference_adain,
ref_controlnet_conditioning_scale=ref_multi_condition_scale,
guess_mode=guess_mode,
).images
results = [x_samples[i] for i in range(num_samples)]
results_tile = []
if enable_tile:
prompt_embeds, negative_prompt_embeds = get_pipeline_embeds(
self.tile_pipe, postive_prompt, negative_prompt, "cuda"
)
for i in range(num_samples):
img_tile = PIL.Image.fromarray(
resize_image(
np.array(x_samples[i]), refine_image_resolution)
)
if i == 0:
mask_image_tile = cv2.resize(
mask_imag_ori,
(img_tile.size[0], img_tile.size[1]),
interpolation=cv2.INTER_LINEAR,
)
mask_image_tile = Image.fromarray(mask_image_tile)
if isinstance(self.pipe, StableDiffusionControlNetInpaintMixingPipeline):
x_samples_tile = self.tile_pipe(
image=img_tile,
mask_image=mask_image_tile,
prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds,
num_images_per_prompt=1,
num_inference_steps=ddim_steps,
generator=generator,
controlnet_conditioning_image=img_tile,
height=img_tile.size[1],
width=img_tile.size[0],
controlnet_conditioning_scale=1.0,
alignment_ratio=refine_alignment_ratio,
guidance_scale=scale,
alpha_weight=alpha_weight,
controlnet_conditioning_scale_map=controlnet_conditioning_scale_map
).images
else:
x_samples_tile = self.tile_pipe(
image=img_tile,
mask_image=mask_image_tile,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
num_images_per_prompt=1,
num_inference_steps=ddim_steps,
generator=generator,
controlnet_conditioning_image=img_tile,
height=img_tile.size[1],
width=img_tile.size[0],
controlnet_conditioning_scale=1.0,
alignment_ratio=refine_alignment_ratio,
guidance_scale=scale,
guess_mode=guess_mode,
).images
results_tile += x_samples_tile
return results_tile, results, [full_segmask, mask_image], postive_prompt
def download_image(url):
response = requests.get(url)
return Image.open(BytesIO(response.content)).convert("RGB")