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import numpy as np
import torch
import cv2 as cv
import random
import os
import spaces
import gradio as gr
from PIL import Image
from transformers import pipeline
from controlnet_aux import MLSDdetector, HEDdetector, NormalBaeDetector, LineartDetector
from peft import PeftModel, LoraConfig
from diffusers import (
DiffusionPipeline,
StableDiffusionPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionControlNetImg2ImgPipeline,
DPMSolverMultistepScheduler,
PNDMScheduler,
ControlNetModel
)
from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import rescale_noise_cfg, retrieve_timesteps
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils.torch_utils import randn_tensor
from diffusers.utils import load_image, make_image_grid
device = "cuda" if torch.cuda.is_available() else "cpu"
if torch.cuda.is_available():
torch_dtype = torch.float16
else:
torch_dtype = torch.float32
default_model = 'CompVis/stable-diffusion-v1-4'
LoRA_path = 'new_model'
CONTROLNET_MODE = {
"Canny Edge Detection" : "lllyasviel/control_v11p_sd15_canny",
"Pixel to Pixel": "lllyasviel/control_v11e_sd15_ip2p",
"HED edge detection (soft edge)" : "lllyasviel/control_sd15_hed",
"Midas depth estimation" : "lllyasviel/control_v11f1p_sd15_depth",
"Surface Normal Estimation" : "lllyasviel/control_v11p_sd15_normalbae",
"Scribble-Based Generation" : "lllyasviel/control_v11p_sd15_scribble",
"Line Art Generation": "lllyasviel/control_v11p_sd15_lineart",
}
def get_pipe(
model_id,
use_controlnet,
controlnet_mode,
use_ip_adapter
):
if use_controlnet and use_ip_adapter:
print('Pipe with ControlNet and IPAdapter')
controlnet = ControlNetModel.from_pretrained(
CONTROLNET_MODE[controlnet_mode],
cache_dir="./models_cache",
torch_dtype=torch.float16
)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
model_id if model_id!='Maria_Lashina_LoRA' else default_model,
torch_dtype=torch_dtype,
controlnet=controlnet,
safety_checker=None,
).to(device)
pipe.load_ip_adapter(
"h94/IP-Adapter",
subfolder="models",
weight_name="ip-adapter-plus_sd14.bin",
)
elif use_controlnet and not use_ip_adapter:
print('Pipe with ControlNet')
controlnet = ControlNetModel.from_pretrained(
CONTROLNET_MODE[controlnet_mode],
cache_dir="./models_cache",
torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
model_id if model_id!='Maria_Lashina_LoRA' else default_model,
torch_dtype=torch_dtype,
controlnet=controlnet,
safety_checker=None,
).to(device)
elif use_ip_adapter and not use_controlnetconto:
print('Pipe with IpAdapter')
pipe = StableDiffusionPipeline.from_pretrained(
model_id if model_id!='Maria_Lashina_LoRA' else default_model,
torch_dtype=torch_dtype,
safety_checker=None,
).to(device)
pipe.load_ip_adapter(
"h94/IP-Adapter",
subfolder="models",
weight_name="ip-adapter-plus_sd14.bin")
elif not use_controlnet and not use_ip_adapter:
print('Pipe with only SD')
pipe = StableDiffusionPipeline.from_pretrained(
model_id if model_id!='Maria_Lashina_LoRA' else default_model,
torch_dtype=torch_dtype,
safety_checker=None,
).to(device)
if model_id == 'Maria_Lashina_LoRA':
adapter_name = 'a cartoonish mouse'
unet_sub_dir = os.path.join(LoRA_path, "unet")
text_encoder_sub_dir = os.path.join(LoRA_path, "text_encoder")
pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name)
pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, text_encoder_sub_dir, adapter_name=adapter_name)
if torch_dtype == torch.float16:
pipe.unet.half()
pipe.text_encoder.half()
return pipe
def prepare_controlnet_image(controlnet_image, mode):
if mode == "Canny Edge Detection":
image = cv.Canny(controlnet_image, 80, 160)
image = np.repeat(image[:, :, None], 3, axis=2)
image = Image.fromarray(image)
elif mode == "HED edge detection (soft edge)":
processor = HEDdetector.from_pretrained('lllyasviel/Annotators')
image = processor(controlnet_image)
elif mode == "Midas depth estimation":
depth_estimator = pipeline('depth-estimation')
image = depth_estimator(controlnet_image)['depth']
image = np.array(image)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
image = Image.fromarray(image)
elif mode == "Surface Normal Estimation":
processor = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
image = processor(controlnet_image)
elif mode == "Scribble-Based Generation":
processor = HEDdetector.from_pretrained('lllyasviel/Annotators')
image = processor(controlnet_image, scribble=True)
elif mode == "Line Art Generation":
processor = LineartDetector.from_pretrained("lllyasviel/Annotators")
image = processor(controlnet_image)
else:
image = controlnet_image
return image
# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
model_id,
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
use_controlnet,
controlnet_strength,
controlnet_mode,
controlnet_image,
use_ip_adapter,
ip_adapter_scale,
ip_adapter_image,
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
if not use_controlnet and not use_ip_adapter:
pipe = get_pipe(model_id, use_controlnet, controlnet_mode, use_ip_adapter)
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator
).images[0]
elif use_controlnet and not use_ip_adapter:
cn_image = prepare_controlnet_image(controlnet_image, controlnet_mode)
pipe = get_pipe(model_id, use_controlnet, controlnet_mode, use_ip_adapter)
image = pipe(
prompt,
cn_image,
negative_prompt=negative_prompt,
num_inference_steps = num_inference_steps,
controlnet_conditioning_scale=controlnet_strength,
generator=generator
).images[0]
elif not use_controlnet and use_ip_adapter:
pipe = get_pipe(model_id, use_controlnet, controlnet_mode, use_ip_adapter)
pipe.set_ip_adapter_scale(ip_adapter_scale)
image = pipe(
prompt,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
ip_adapter_image=ip_adapter_image,
generator=generator
).images[0]
elif use_controlnet and use_ip_adapter:
cn_image = prepare_controlnet_image(controlnet_image, controlnet_mode)
pipe = get_pipe(model_id, use_controlnet, controlnet_mode, use_ip_adapter)
pipe.set_ip_adapter_scale(ip_adapter_scale)
image = pipe(
prompt,
cn_image,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
height=height,
width=width,
controlnet_conditioning_scale=controlnet_strength,
ip_adapter_image=image_upload_ip,
generator=generator,
).images[0]
return image, seed |