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on
A10G
Building
on
A10G
Update diffusion_webui/diffusion_models/controlnet/controlnet_inpaint/controlnet_inpaint_canny.py
Browse files
diffusion_webui/diffusion_models/controlnet/controlnet_inpaint/controlnet_inpaint_canny.py
CHANGED
@@ -2,7 +2,8 @@ import cv2
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import gradio as gr
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import numpy as np
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import torch
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from diffusers import ControlNetModel
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from PIL import Image
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from diffusion_webui.utils.model_list import (
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@@ -26,7 +27,7 @@ class StableDiffusionControlNetInpaintCannyGenerator:
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controlnet = ControlNetModel.from_pretrained(
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controlnet_model_path, torch_dtype=torch.float16
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)
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self.pipe =
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pretrained_model_name_or_path=stable_model_path,
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controlnet=controlnet,
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safety_checker=None,
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@@ -68,8 +69,12 @@ class StableDiffusionControlNetInpaintCannyGenerator:
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seed_generator: int,
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):
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pipe = self.load_model(
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stable_model_path=stable_model_path,
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controlnet_model_path=controlnet_model_path,
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@@ -84,7 +89,9 @@ class StableDiffusionControlNetInpaintCannyGenerator:
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output = pipe(
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prompt=prompt,
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image=
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negative_prompt=negative_prompt,
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num_images_per_prompt=num_images_per_prompt,
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num_inference_steps=num_inference_step,
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import gradio as gr
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import numpy as np
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import torch
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from diffusers import ControlNetModel
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from diffusion_webui.diffusion_models.controlnet.controlnet_inpaint.pipeline_stable_diffusion_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
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from PIL import Image
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from diffusion_webui.utils.model_list import (
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controlnet = ControlNetModel.from_pretrained(
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controlnet_model_path, torch_dtype=torch.float16
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)
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self.pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
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pretrained_model_name_or_path=stable_model_path,
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controlnet=controlnet,
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safety_checker=None,
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seed_generator: int,
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):
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normal_image = image_path["image"].convert("RGB").resize((512, 512))
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mask_image = image_path["mask"].convert("RGB").resize((512, 512))
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normal_image = np.array(normal_image)
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mask_image = np.array(mask_image)
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control_image = self.controlnet_canny_inpaint(image_path=image_path)
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pipe = self.load_model(
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stable_model_path=stable_model_path,
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controlnet_model_path=controlnet_model_path,
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output = pipe(
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prompt=prompt,
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image=normal_image,
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mask_image=mask_image,
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control_image=control_image,
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negative_prompt=negative_prompt,
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num_images_per_prompt=num_images_per_prompt,
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num_inference_steps=num_inference_step,
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