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from typing import Optional |
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import torch |
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from diffusers import ControlNetModel, StableDiffusionControlNetImg2ImgPipeline |
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from PIL import Image |
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import internals.util.image as ImageUtil |
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from internals.pipelines.commons import AbstractPipeline |
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from internals.pipelines.controlnets import ControlNet |
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from internals.pipelines.high_res import HighRes |
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from internals.pipelines.sdxl_llite_pipeline import SDXLLLiteImg2ImgPipeline |
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from internals.util import get_generators |
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from internals.util.config import ( |
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get_base_dimension, |
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get_hf_cache_dir, |
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get_is_sdxl, |
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get_num_return_sequences, |
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) |
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class RealtimeDraw(AbstractPipeline): |
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def load(self, pipeline: AbstractPipeline): |
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if hasattr(self, "pipe"): |
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return |
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if get_is_sdxl(): |
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lite_pipe = SDXLLLiteImg2ImgPipeline() |
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lite_pipe.load( |
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pipeline, |
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[ |
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"https://s3.ap-south-1.amazonaws.com/autodraft.model.assets/models/replicate-xl-llite.safetensors" |
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], |
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) |
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self.pipe = lite_pipe |
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else: |
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self.__controlnet_scribble = ControlNetModel.from_pretrained( |
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"lllyasviel/control_v11p_sd15_scribble", |
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torch_dtype=torch.float16, |
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cache_dir=get_hf_cache_dir(), |
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) |
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self.__controlnet_seg = ControlNetModel.from_pretrained( |
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"lllyasviel/control_v11p_sd15_seg", |
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torch_dtype=torch.float16, |
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cache_dir=get_hf_cache_dir(), |
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) |
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kwargs = {**pipeline.pipe.components} |
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kwargs.pop("image_encoder", None) |
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self.pipe = StableDiffusionControlNetImg2ImgPipeline( |
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**kwargs, controlnet=self.__controlnet_seg |
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).to("cuda") |
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self.pipe.safety_checker = None |
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self.pipe2 = StableDiffusionControlNetImg2ImgPipeline( |
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**kwargs, controlnet=[self.__controlnet_scribble, self.__controlnet_seg] |
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).to("cuda") |
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self.pipe2.safety_checker = None |
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def process_seg( |
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self, |
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image: Image.Image, |
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prompt: str, |
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negative_prompt: str, |
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seed: int, |
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): |
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if get_is_sdxl(): |
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raise Exception("SDXL is not supported for this method") |
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generator = get_generators(seed, get_num_return_sequences()) |
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image = ImageUtil.resize_image(image, 512) |
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img = self.pipe.__call__( |
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image=image, |
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control_image=image, |
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prompt=prompt, |
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num_inference_steps=15, |
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negative_prompt=negative_prompt, |
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generator=generator, |
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guidance_scale=10, |
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strength=0.8, |
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).images[0] |
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return img |
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def process_img( |
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self, |
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prompt: str, |
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negative_prompt: str, |
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seed: int, |
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image: Optional[Image.Image] = None, |
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image2: Optional[Image.Image] = None, |
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): |
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generator = get_generators(seed, get_num_return_sequences()) |
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b_dimen = get_base_dimension() |
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if not image: |
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size = (b_dimen, b_dimen) |
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if image2: |
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size = image2.size |
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image = Image.new("RGB", size, color=0) |
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if not image2: |
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size = (b_dimen, b_dimen) |
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if image: |
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size = image.size |
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image2 = Image.new("RGB", size, color=0) |
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if get_is_sdxl(): |
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size = HighRes.find_closest_sdxl_aspect_ratio(image.size[0], image.size[1]) |
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image = image.resize(size) |
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torch.manual_seed(seed) |
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images = self.pipe.__call__( |
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image=image, |
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condition_image=image, |
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negative_prompt=negative_prompt, |
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prompt=prompt, |
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seed=seed, |
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num_inference_steps=10, |
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width=image.size[0], |
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height=image.size[1], |
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) |
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img = images[0] |
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else: |
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image = ImageUtil.resize_image(image, b_dimen) |
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scribble = ControlNet.scribble_image(image) |
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image2 = ImageUtil.resize_image(image2, b_dimen) |
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img = self.pipe2.__call__( |
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image=image, |
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control_image=[scribble, image2], |
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prompt=prompt, |
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num_inference_steps=15, |
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negative_prompt=negative_prompt, |
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guidance_scale=10, |
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generator=generator, |
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strength=0.9, |
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width=image.size[0], |
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height=image.size[1], |
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controlnet_conditioning_scale=[1.0, 0.8], |
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).images[0] |
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img = ImageUtil.resize_image(img, 512) |
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return img |
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