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import os
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
import torch
import numpy as np
from pipeline_stable_diffusion_controlnet_inpaint import *
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import *
import random
# model2
controlnet1 = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_scribble", torch_dtype=torch.float16)
pipe1 = StableDiffusionControlNetInpaintPipeline.from_pretrained("hirol/Any-inpainting", controlnet=controlnet1, torch_dtype=torch.float16)
pipe1.scheduler = UniPCMultistepScheduler.from_config(pipe1.scheduler.config)
pipe1.to('cuda')
# model1
controlnet = ControlNetModel.from_pretrained("hirol/control_any5_openpose", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained("hirol/Any-inpainting", controlnet=controlnet, torch_dtype=torch.float16)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.to('cuda')
def make_inpaint_condition(image, image_mask):
image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
image_mask = np.array(image_mask.convert("L"))
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 generate_image(prompt:str, negative_prompt:str, openpose_image, original_image, mask_image):
a = random.randint(10000,90000)
generator = torch.manual_seed(a)
# control_image = make_inpaint_condition(original_image, mask_image)
# images = [openpose_image, control_image]
image = pipe(
prompt=prompt,
# images,
image=original_image,
control_image=openpose_image,
mask_image=mask_image,
num_inference_steps=20,
generator=generator,
negative_prompt=negative_prompt,
# controlnet_conditioning_scale=[1.0, 0.8],
).images[0]
return image
def generate_image_sketch(prompt: str, negative_prompt: str, openpose_image, original_image, mask_image):
b = random.randint(10000, 90000)
generator = torch.manual_seed(b)
image = pipe1(
prompt=prompt,
# images,
image=original_image,
control_image=openpose_image,
mask_image=mask_image,
num_inference_steps=20,
generator=generator,
negative_prompt=negative_prompt,
# controlnet_conditioning_scale=[1.0, 0.8],
).images[0]
return [image] |