|
from typing import Dict, List, Any |
|
import torch |
|
from diffusers import DPMSolverMultistepScheduler, EulerDiscreteScheduler, EulerDiscreteScheduler, DDIMScheduler, StableDiffusionInpaintPipeline, AutoPipelineForInpainting, AutoPipelineForImage2Image, DiffusionPipeline, StableDiffusionXLImg2ImgPipeline, StableDiffusionControlNetInpaintPipeline, ControlNetModel, StableDiffusionPipeline |
|
from PIL import Image |
|
import base64 |
|
from io import BytesIO |
|
import numpy as np |
|
import cv2 |
|
|
|
|
|
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
|
|
|
if device.type != 'cuda': |
|
raise ValueError("need to run on GPU") |
|
|
|
class EndpointHandler(): |
|
def __init__(self, path=""): |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
self.controlnet = ControlNetModel.from_pretrained( |
|
"lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16 |
|
) |
|
|
|
|
|
self.pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained( |
|
"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, torch_dtype=torch.float16 |
|
) |
|
|
|
self.pipe.scheduler = EulerDiscreteScheduler.from_config(self.pipe.scheduler.config) |
|
self.pipe.enable_model_cpu_offload() |
|
self.pipe.enable_xformers_memory_efficient_attention() |
|
|
|
""" |
|
# load StableDiffusionInpaintPipeline pipeline |
|
self.pipe = AutoPipelineForInpainting.from_pretrained( |
|
"runwayml/stable-diffusion-inpainting", |
|
revision="fp16", |
|
torch_dtype=torch.float16, |
|
) |
|
# use DPMSolverMultistepScheduler |
|
self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config) |
|
|
|
self.pipe.enable_model_cpu_offload() |
|
|
|
self.pipe.enable_xformers_memory_efficient_attention() |
|
|
|
# move to device |
|
#self.pipe = self.pipe.to(device) |
|
|
|
self.pipe2 = AutoPipelineForInpainting.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True) |
|
#self.pipe2.enable_model_cpu_offload() |
|
self.pipe2.enable_xformers_memory_efficient_attention() |
|
|
|
self.pipe2.to("cuda") |
|
|
|
self.pipe3 = AutoPipelineForImage2Image.from_pipe(self.pipe2) |
|
#self.pipe3.enable_model_cpu_offload() |
|
self.pipe3.enable_xformers_memory_efficient_attention() |
|
""" |
|
|
|
|
|
def __call__(self, data: Any) -> List[List[Dict[str, float]]]: |
|
""" |
|
:param data: A dictionary contains `inputs` and optional `image` field. |
|
:return: A dictionary with `image` field contains image in base64. |
|
""" |
|
encoded_image = data.pop("image", None) |
|
encoded_mask_image = data.pop("mask_image", None) |
|
|
|
prompt = data.pop("prompt", "") |
|
|
|
negative_prompt = data.pop("negative_prompt", "") |
|
|
|
method = data.pop("method", "slow") |
|
strength = data.pop("strength", 0.2) |
|
guidance_scale = data.pop("guidance_scale", 8.0) |
|
num_inference_steps = data.pop("num_inference_steps", 20) |
|
""" |
|
if(method == "smooth"): |
|
if encoded_image is not None: |
|
image = self.decode_base64_image(encoded_image) |
|
out = self.smooth_pipe(prompt, image=image).images[0] |
|
|
|
return out |
|
""" |
|
|
|
|
|
if encoded_image is not None and encoded_mask_image is not None: |
|
image = self.decode_base64_image(encoded_image).convert("RGB") |
|
mask_image = self.decode_base64_image(encoded_mask_image).convert("RGB") |
|
else: |
|
image = None |
|
mask_image = None |
|
|
|
|
|
""" |
|
if(method == "fast"): |
|
image = self.fast_pipe( |
|
prompt=prompt, |
|
negative_prompt=negative_prompt, |
|
image=image, |
|
mask_image=mask_image, |
|
guidance_scale=guidance_scale, |
|
num_inference_steps=num_inference_steps, # steps between 15 and 30 work well for us |
|
strength=strength, # make sure to use `strength` below 1.0 |
|
generator=self.generator, |
|
).images[0] |
|
|
|
return image |
|
""" |
|
|
|
|
|
""" |
|
# run inference pipeline |
|
out = self.pipe(prompt=prompt, negative_prompt=negative_prompt, image=image, mask_image=mask_image, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale) |
|
|
|
print("1st pipeline part successful!") |
|
|
|
image = out.images[0].resize((1024, 1024)) |
|
|
|
print("image resizing successful!") |
|
|
|
image = self.pipe2( |
|
prompt=prompt, |
|
negative_prompt=negative_prompt, |
|
image=image, |
|
mask_image=mask_image, |
|
guidance_scale=guidance_scale, #8.0 |
|
num_inference_steps=int(num_inference_steps/10), #100 |
|
strength=strength, #0.2 |
|
output_type="latent", # let's keep in latent to save some VRAM |
|
).images[0] |
|
|
|
print("2nd pipeline part successful!") |
|
|
|
image2 = self.pipe3( |
|
prompt=prompt, |
|
image=image, |
|
guidance_scale=guidance_scale, #8.0 |
|
num_inference_steps=int(num_inference_steps/10), #100 |
|
strength=strength, #0.2 |
|
).images[0] |
|
|
|
print("3rd pipeline part successful!") |
|
|
|
|
|
# return first generate PIL image |
|
return image2 |
|
""" |
|
|
|
|
|
control_image = self.make_inpaint_condition(image, mask_image) |
|
|
|
|
|
image = self.pipe( |
|
prompt=prompt, |
|
negative_prompt=negative_prompt, |
|
num_inference_steps=num_inference_steps, |
|
eta=1.0, |
|
image=image, |
|
mask_image=mask_image, |
|
control_image=control_image, |
|
guidance_scale=guidance_scale, |
|
strength=strength |
|
).images[0] |
|
|
|
return image |
|
|
|
|
|
|
|
|
|
def decode_base64_image(self, image_string): |
|
base64_image = base64.b64decode(image_string) |
|
buffer = BytesIO(base64_image) |
|
image = Image.open(buffer) |
|
return image |
|
|
|
def make_inpaint_condition(self, image, image_mask): |
|
image = np.array(image.convert("RGB")).astype(np.float32) / 255.0 |
|
image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 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 > 0.5] = -1.0 |
|
image = np.expand_dims(image, 0).transpose(0, 3, 1, 2) |
|
image = torch.from_numpy(image) |
|
return image |
|
|
