add controlnet 1.1
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@ -42,23 +42,43 @@ NAMES_MAP = {
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"realisticVision1.4": "Sanster/Realistic_Vision_V1.4-inpainting",
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}
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NATIVE_NAMES_MAP = {
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"sd1.5": "runwayml/stable-diffusion-v1-5",
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"anything4": "andite/anything-v4.0",
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"realisticVision1.4": "SG161222/Realistic_Vision_V1.4",
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}
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def load_from_local_model(local_model_path, torch_dtype, controlnet):
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def make_inpaint_condition(image, image_mask):
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"""
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image: [H, W, C] RGB
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mask: [H, W, 1] 255 means area to repaint
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"""
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image = image.astype(np.float32) / 255.0
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image[image_mask[:, :, -1] > 128] = -1.0 # set as masked pixel
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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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def load_from_local_model(
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local_model_path, torch_dtype, controlnet, pipe_class, is_native_control_inpaint
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):
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from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
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load_pipeline_from_original_stable_diffusion_ckpt,
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download_from_original_stable_diffusion_ckpt,
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)
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from .pipeline import StableDiffusionControlNetInpaintPipeline
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logger.info(f"Converting {local_model_path} to diffusers controlnet pipeline")
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pipe = load_pipeline_from_original_stable_diffusion_ckpt(
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pipe = download_from_original_stable_diffusion_ckpt(
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local_model_path,
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num_in_channels=9,
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num_in_channels=4 if is_native_control_inpaint else 9,
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from_safetensors=local_model_path.endswith("safetensors"),
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device="cpu",
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load_safety_checker=False
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)
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inpaint_pipe = StableDiffusionControlNetInpaintPipeline(
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inpaint_pipe = pipe_class(
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vae=pipe.vae,
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text_encoder=pipe.text_encoder,
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tokenizer=pipe.tokenizer,
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@ -81,9 +101,6 @@ class ControlNet(DiffusionInpaintModel):
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min_size = 512
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def init_model(self, device: torch.device, **kwargs):
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from .pipeline import StableDiffusionControlNetInpaintPipeline
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model_id = NAMES_MAP[kwargs["name"]]
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fp16 = not kwargs.get("no_half", False)
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model_kwargs = {
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@ -102,17 +119,35 @@ class ControlNet(DiffusionInpaintModel):
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use_gpu = device == torch.device("cuda") and torch.cuda.is_available()
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torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
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sd_controlnet_method = kwargs["sd_controlnet_method"]
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if sd_controlnet_method == "control_v11p_sd15_inpaint":
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from diffusers import StableDiffusionControlNetPipeline as PipeClass
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self.is_native_control_inpaint = True
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else:
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from .pipeline import StableDiffusionControlNetInpaintPipeline as PipeClass
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self.is_native_control_inpaint = False
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if self.is_native_control_inpaint:
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model_id = NATIVE_NAMES_MAP[kwargs["name"]]
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else:
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model_id = NAMES_MAP[kwargs["name"]]
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controlnet = ControlNetModel.from_pretrained(
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f"lllyasviel/sd-controlnet-canny", torch_dtype=torch_dtype
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f"lllyasviel/{sd_controlnet_method}", torch_dtype=torch_dtype
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)
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if kwargs.get("sd_local_model_path", None):
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self.model = load_from_local_model(
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kwargs["sd_local_model_path"],
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torch_dtype=torch_dtype,
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controlnet=controlnet,
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pipe_class=PipeClass,
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is_native_control_inpaint=self.is_native_control_inpaint,
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)
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else:
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self.model = StableDiffusionControlNetInpaintPipeline.from_pretrained(
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self.model = PipeClass.from_pretrained(
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model_id,
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controlnet=controlnet,
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revision="fp16" if use_gpu and fp16 else "main",
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@ -156,9 +191,26 @@ class ControlNet(DiffusionInpaintModel):
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img_h, img_w = image.shape[:2]
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if self.is_native_control_inpaint:
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control_image = make_inpaint_condition(image, mask)
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output = self.model(
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prompt=config.prompt,
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image=control_image,
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height=img_h,
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width=img_w,
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num_inference_steps=config.sd_steps,
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guidance_scale=config.sd_guidance_scale,
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controlnet_conditioning_scale=config.controlnet_conditioning_scale,
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negative_prompt=config.negative_prompt,
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generator=torch.manual_seed(config.sd_seed),
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output_type="np.array",
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).images[0]
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else:
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canny_image = cv2.Canny(image, 100, 200)
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canny_image = canny_image[:, :, None]
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canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
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canny_image = np.concatenate(
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[canny_image, canny_image, canny_image], axis=2
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)
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canny_image = PIL.Image.fromarray(canny_image)
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mask_image = PIL.Image.fromarray(mask[:, :, -1], mode="L")
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image = PIL.Image.fromarray(image)
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@ -38,6 +38,14 @@ def parse_args():
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"--sd-cpu-textencoder", action="store_true", help=SD_CPU_TEXTENCODER_HELP
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)
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parser.add_argument("--sd-controlnet", action="store_true", help=SD_CONTROLNET_HELP)
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parser.add_argument(
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"--sd-controlnet-method",
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default="control_v11p_sd15_inpaint",
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choices=[
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"control_v11p_sd15_canny",
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"control_v11p_sd15_inpaint",
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],
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)
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parser.add_argument("--sd-local-model-path", default=None, help=SD_LOCAL_MODEL_HELP)
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parser.add_argument(
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"--local-files-only", action="store_true", help=LOCAL_FILES_ONLY_HELP
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@ -86,7 +94,7 @@ def parse_args():
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"--interactive-seg-model",
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default="vit_l",
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choices=AVAILABLE_INTERACTIVE_SEG_MODELS,
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help=INTERACTIVE_SEG_MODEL_HELP
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help=INTERACTIVE_SEG_MODEL_HELP,
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)
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parser.add_argument(
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"--interactive-seg-device",
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@ -168,11 +176,11 @@ def parse_args():
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if args.config_installer:
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if args.installer_config is None:
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parser.error(
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f"args.config_installer==True, must set args.installer_config to store config file"
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"args.config_installer==True, must set args.installer_config to store config file"
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)
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from lama_cleaner.web_config import main
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logger.info(f"Launching installer web config page")
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logger.info("Launching installer web config page")
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main(args.installer_config)
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exit()
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@ -194,10 +202,6 @@ def parse_args():
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"torch.cuda.is_available() is False, please use --device cpu or check your pytorch installation"
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)
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if args.sd_controlnet:
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if args.model not in SD15_MODELS:
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logger.warning(f"--sd_controlnet only support {SD15_MODELS}")
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if args.sd_local_model_path and args.model == "sd1.5":
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if not os.path.exists(args.sd_local_model_path):
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parser.error(
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@ -537,6 +537,7 @@ def main(args):
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model = ModelManager(
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name=args.model,
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sd_controlnet=args.sd_controlnet,
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sd_controlnet_method=args.sd_controlnet_method,
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device=device,
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no_half=args.no_half,
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hf_access_token=args.hf_access_token,
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@ -7,7 +7,7 @@ pydantic
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rich
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loguru
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yacs
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diffusers[torch]==0.14.0
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diffusers==0.16.1
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transformers==4.27.4
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gradio
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piexif==1.1.3
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