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from typing import Dict, List, Any |
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import torch |
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from diffusers import DPMSolverMultistepScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler, DDIMScheduler, StableDiffusionInpaintPipeline, AutoPipelineForInpainting, AutoPipelineForImage2Image, DiffusionPipeline, StableDiffusionXLImg2ImgPipeline, StableDiffusionControlNetInpaintPipeline, ControlNetModel, StableDiffusionPipeline |
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from PIL import Image |
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import base64 |
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from io import BytesIO |
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import numpy as np |
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import cv2 |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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if device.type != 'cuda': |
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raise ValueError("need to run on GPU") |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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self.controlnet = ControlNetModel.from_pretrained( |
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"lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16 |
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) |
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self.pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained( |
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"diffusers/stable-diffusion-xl-1.0-inpainting-0.1", controlnet=self.controlnets, torch_dtype=torch.float16 |
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) |
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self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config) |
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self.pipe.enable_model_cpu_offload() |
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self.pipe.enable_xformers_memory_efficient_attention() |
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""" |
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# load StableDiffusionInpaintPipeline pipeline |
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self.pipe = AutoPipelineForInpainting.from_pretrained( |
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"runwayml/stable-diffusion-inpainting", |
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revision="fp16", |
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torch_dtype=torch.float16, |
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) |
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# use DPMSolverMultistepScheduler |
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self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config) |
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self.pipe.enable_model_cpu_offload() |
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self.pipe.enable_xformers_memory_efficient_attention() |
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# move to device |
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#self.pipe = self.pipe.to(device) |
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self.pipe2 = AutoPipelineForInpainting.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True) |
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#self.pipe2.enable_model_cpu_offload() |
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self.pipe2.enable_xformers_memory_efficient_attention() |
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self.pipe2.to("cuda") |
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self.pipe3 = AutoPipelineForImage2Image.from_pipe(self.pipe2) |
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#self.pipe3.enable_model_cpu_offload() |
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self.pipe3.enable_xformers_memory_efficient_attention() |
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""" |
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def __call__(self, data: Any) -> List[List[Dict[str, float]]]: |
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""" |
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:param data: A dictionary contains `inputs` and optional `image` field. |
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:return: A dictionary with `image` field contains image in base64. |
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""" |
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encoded_image = data.pop("image", None) |
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encoded_mask_image = data.pop("mask_image", None) |
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prompt = data.pop("prompt", "") |
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negative_prompt = data.pop("negative_prompt", "") |
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method = data.pop("method", "slow") |
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strength = data.pop("strength", 0.2) |
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guidance_scale = data.pop("guidance_scale", 8.0) |
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num_inference_steps = data.pop("num_inference_steps", 20) |
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""" |
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if(method == "smooth"): |
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if encoded_image is not None: |
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image = self.decode_base64_image(encoded_image) |
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out = self.smooth_pipe(prompt, image=image).images[0] |
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return out |
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""" |
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if encoded_image is not None and encoded_mask_image is not None: |
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image = self.decode_base64_image(encoded_image).convert("RGB") |
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mask_image = self.decode_base64_image(encoded_mask_image).convert("RGB") |
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else: |
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image = None |
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mask_image = None |
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""" |
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if(method == "fast"): |
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image = self.fast_pipe( |
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prompt=prompt, |
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negative_prompt=negative_prompt, |
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image=image, |
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mask_image=mask_image, |
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guidance_scale=guidance_scale, |
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num_inference_steps=num_inference_steps, # steps between 15 and 30 work well for us |
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strength=strength, # make sure to use `strength` below 1.0 |
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generator=self.generator, |
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).images[0] |
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return image |
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""" |
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""" |
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# run inference pipeline |
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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) |
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print("1st pipeline part successful!") |
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image = out.images[0].resize((1024, 1024)) |
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print("image resizing successful!") |
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image = self.pipe2( |
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prompt=prompt, |
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negative_prompt=negative_prompt, |
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image=image, |
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mask_image=mask_image, |
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guidance_scale=guidance_scale, #8.0 |
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num_inference_steps=int(num_inference_steps/10), #100 |
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strength=strength, #0.2 |
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output_type="latent", # let's keep in latent to save some VRAM |
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).images[0] |
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print("2nd pipeline part successful!") |
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image2 = self.pipe3( |
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prompt=prompt, |
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image=image, |
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guidance_scale=guidance_scale, #8.0 |
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num_inference_steps=int(num_inference_steps/10), #100 |
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strength=strength, #0.2 |
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).images[0] |
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print("3rd pipeline part successful!") |
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# return first generate PIL image |
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return image |
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""" |
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control_image = self.make_inpaint_condition(image, mask_image) |
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image = self.pipe( |
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prompt=prompt, |
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image=image, |
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negative_prompt=negative_prompt, |
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num_inference_steps=num_inference_steps, |
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eta=1.0, |
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mask_image=mask_image, |
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control_image=control_image, |
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guidance_scale=guidance_scale, |
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strength=strength, |
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).images[0] |
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return image |
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def decode_base64_image(self, image_string): |
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base64_image = base64.b64decode(image_string) |
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buffer = BytesIO(base64_image) |
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image = Image.open(buffer) |
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return image |
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def make_inpaint_condition(self, image, image_mask): |
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image = np.array(image.convert("RGB")).astype(np.float32) / 255.0 |
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image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0 |
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assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size" |
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image[image_mask > 0.5] = -1.0 |
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image = np.expand_dims(image, 0).transpose(0, 3, 1, 2) |
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image = torch.from_numpy(image) |
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return image |
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