终于,在SDXL下实现Tile controlnet 本期介绍X-adapter打通SDXL与SD1.5生态后的内容与comfyui里的效果 #comfyui #aigc #sdxl #xadapter #人工智能 comfyUI x-ad - 人工治障于20240304发布在抖音,已经收获了9233个喜欢,来抖音,记录美好生活!
ControlNet support StableSR support Tiled Noise Inversion Tiled VAE Regional Prompt Control Img2img upscale Ultra-Large image generation Tiled Diffusion Tip Settile_overlapto 0 anddenoiseto 1 to see the tile seams and then adjust the options to your needs. ...
//huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union-alpha/resolve/main/images/canny.jpg")prompt="A girl in city, 25 years old, cool, futuristic."control_mode=0# tilecontrol_image=load_image("https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union-alpha/resolve/main/images/tile.jpg...
(w, h)) # resize to integer multiple of 32 mask = mask.resize((w // 8, h // 8), resample=PIL.Image.BILINEAR) # LANCZOS) mask = np.array(mask).astype(np.float32) / 255.0 mask = np.tile(mask, (4, 1, 1)) mask = mask[None].transpose(0, 1, 2, 3) # what do...
Set `--controlnet_tile_path` to ControlNet-tile's path in the bash script. The default value is its huggingface model card. sh ./bash_scripts/controlnet_tile_inference.sh ## Cite If you find X-Adapter useful for your research and applications, please cite us using this BibTeX: ``...
tile_overlap_factor_width=1 / 12, ) inputs = self.get_dummy_inputs(generator_device) inputs["height"] = inputs["width"] = 128 output_with_tiling = pipe(**inputs)[0] self.assertLess( (to_np(output_without_tiling) - to_np(output_with_tiling)).max(), expected_diff_max, "VAE...
tile_overlap_factor_width=1 / 12, ) inputs = self.get_dummy_inputs(generator_device) inputs["height"] = inputs["width"] = 128 output_with_tiling = pipe(**inputs)[0] self.assertLess( (to_np(output_without_tiling) - to_np(output_with_tiling)).max(), expected_diff_max, "VAE...
(mean=VGG16_IMAGE_MEAN, std=VGG16_IMAGE_STD) # ControlNet self.control_nets: List[ControlNetInfo] = [] self.control_net_enabled = True # control_netsが空ならTrueでもFalseでもControlNetは動作しない # Textual Inversion def add_token_replacement(self, target_token_id, rep_token_...
(mean=VGG16_IMAGE_MEAN, std=VGG16_IMAGE_STD) # ControlNet self.control_nets: List[ControlNetInfo] = [] self.control_net_enabled = True # control_netsが空ならTrueでもFalseでもControlNetは動作しない # Textual Inversion def add_token_replacement(self, target_token_id, rep_token...
(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 mask = mask.resize((w // 8, h // 8), resample=PIL.Image.LANCZOS) mask = np.array(mask).astype(np.float32) / 255.0 mask = np.tile(mask, (4, 1, 1)) mask = mask[None].transpose(0, 1, 2, 3) ...