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from typing import Dict, List, Any |
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import torch |
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from diffusers import StableDiffusionXLImg2ImgPipeline, DiffusionPipeline, AutoencoderKL, DPMSolverMultistepScheduler, DDIMScheduler, StableDiffusionInpaintPipeline, AutoPipelineForInpainting, AutoPipelineForImage2Image, StableDiffusionControlNetInpaintPipeline, ControlNetModel |
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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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from diffusers.image_processor import VaeImageProcessor |
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import numpy as np |
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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.smooth_pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained( |
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"stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16 |
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) |
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self.smooth_pipe.to("cuda") |
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self.vae = AutoencoderKL.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", |
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subfolder="vae", use_safetensors=True, |
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).to("cuda") |
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self.smooth_pipe.enable_model_cpu_offload() |
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self.smooth_pipe.enable_xformers_memory_efficient_attention() |
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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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"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, torch_dtype=torch.float16 |
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) |
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self.pipe.scheduler = DDIMScheduler.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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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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method = data.pop("method", "rasterize") |
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if(method == "rasterize"): |
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encoded_image = data.pop("image", None) |
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prompt = data.pop("prompt", "") |
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num_inference_steps = data.pop("num_inference_steps", 50) |
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if encoded_image is not None: |
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image = self.decode_base64_image(encoded_image).convert('RGB') |
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image_processor = VaeImageProcessor(); |
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latents = image_processor.preprocess(image) |
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latents = latents.to(device="cuda") |
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with torch.no_grad(): |
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latents_dist = self.vae.encode(latents).latent_dist.sample() * self.vae.config.scaling_factor |
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self.smooth_pipe.enable_xformers_memory_efficient_attention() |
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out = self.smooth_pipe(prompt, image=latents_dist, num_inference_steps=num_inference_steps).images |
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return out |
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else: |
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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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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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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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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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image=image, |
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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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