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from modules.utils import * |
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class Image2Canny: |
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def __init__(self, device, pretrained_model_dir): |
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print("Initializing Image2Canny") |
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self.low_threshold = 100 |
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self.high_threshold = 200 |
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@prompts(name="Edge Detection On Image", |
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description="useful when you want to detect the edge of the image. " |
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"like: detect the edges of this image, or canny detection on image, " |
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"or perform edge detection on this image, or detect the canny image of this image. " |
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"The input to this tool should be a string, representing the image_path") |
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def inference(self, inputs): |
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image = Image.open(inputs) |
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image = np.array(image) |
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canny = cv2.Canny(image, self.low_threshold, self.high_threshold) |
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canny = canny[:, :, None] |
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canny = np.concatenate([canny, canny, canny], axis=2) |
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canny = Image.fromarray(canny) |
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updated_image_path = get_new_image_name(inputs, func_name="edge") |
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canny.save(updated_image_path) |
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print(f"\nProcessed Image2Canny, Input Image: {inputs}, Output Text: {updated_image_path}") |
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return updated_image_path |
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class CannyText2Image: |
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def __init__(self, device, pretrained_model_dir): |
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print("Initializing CannyText2Image to %s" % device) |
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self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 |
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self.controlnet = ControlNetModel.from_pretrained(f"{pretrained_model_dir}/sd-controlnet-canny", |
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torch_dtype=self.torch_dtype) |
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self.pipe = StableDiffusionControlNetPipeline.from_pretrained( |
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f"{pretrained_model_dir}/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, |
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torch_dtype=self.torch_dtype) |
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self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config) |
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self.pipe.to(device) |
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self.seed = -1 |
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self.a_prompt = 'best quality, extremely detailed' |
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self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \ |
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'fewer digits, cropped, worst quality, low quality' |
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@prompts(name="Generate Image Condition On Canny Image", |
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description="useful when you want to generate a new real image from both the user desciption and a canny image." |
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" like: generate a real image of a object or something from this canny image," |
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" or generate a new real image of a object or something from this edge image. " |
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"The input to this tool should be a comma seperated string of two, " |
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"representing the image_path and the user description. ") |
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def inference(self, inputs): |
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image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:]) |
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image = Image.open(image_path) |
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self.seed = random.randint(0, 65535) |
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seed_everything(self.seed) |
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prompt = instruct_text + ', ' + self.a_prompt |
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image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt, |
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guidance_scale=9.0).images[0] |
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updated_image_path = get_new_image_name(image_path, func_name="canny2image") |
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image.save(updated_image_path) |
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print(f"\nProcessed CannyText2Image, Input Canny: {image_path}, Input Text: {instruct_text}, " |
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f"Output Text: {updated_image_path}") |
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return updated_image_path |
