First working version
This commit is contained in:
matt3o
+162
-84
@@ -12,6 +12,7 @@ from .resampler import Resampler
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from insightface.app import FaceAnalysis
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import torchvision.transforms.v2 as T
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import torch.nn.functional as F
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MODELS_DIR = os.path.join(folder_paths.models_dir, "instantid")
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if "instantid" not in folder_paths.folder_names_and_paths:
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@@ -50,19 +51,6 @@ def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,2
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out_img_pil = PIL.Image.fromarray(out_img.astype(np.uint8))
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return out_img_pil
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def set_model_patch_replace(model, patch_kwargs, key):
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to = model.model_options["transformer_options"]
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if "patches_replace" not in to:
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to["patches_replace"] = {}
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if "attn2" not in to["patches_replace"]:
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to["patches_replace"]["attn2"] = {}
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if key not in to["patches_replace"]["attn2"]:
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patch = CrossAttentionPatch(**patch_kwargs)
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to["patches_replace"]["attn2"][key] = patch
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else:
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to["patches_replace"]["attn2"][key].set_new_condition(**patch_kwargs)
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class CrossAttentionPatch:
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# forward for patching
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def __init__(self, weight, instantid, number, cond, uncond, mask=None, sigma_start=0.0, sigma_end=1.0):
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@@ -90,7 +78,9 @@ class CrossAttentionPatch:
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def __call__(self, n, context_attn2, value_attn2, extra_options):
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org_dtype = n.dtype
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cond_or_uncond = extra_options["cond_or_uncond"]
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sigma = extra_options["sigmas"][0].item() if 'sigmas' in extra_options else 999999999.9
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sigma = extra_options["sigmas"][0] if 'sigmas' in extra_options else None
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sigma = sigma.item() if sigma else 999999999.9
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q = n
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k = context_attn2
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@@ -102,27 +92,46 @@ class CrossAttentionPatch:
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_, _, lh, lw = extra_options["original_shape"]
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for weight, cond, uncond, instantid, mask, sigma_start, sigma_end in zip(self.weights, self.conds, self.unconds, self.instantid, self.masks, self.sigma_start, self.sigma_end):
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#if sigma > sigma_start or sigma < sigma_end:
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# continue
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if sigma < sigma_start and sigma > sigma_end:
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k_cond = instantid.ip_layers.to_kvs[self.k_key](cond).repeat(batch_prompt, 1, 1)
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k_uncond = instantid.ip_layers.to_kvs[self.k_key](uncond).repeat(batch_prompt, 1, 1)
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v_cond = instantid.ip_layers.to_kvs[self.v_key](cond).repeat(batch_prompt, 1, 1)
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v_uncond = instantid.ip_layers.to_kvs[self.v_key](uncond).repeat(batch_prompt, 1, 1)
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k_cond = instantid.ip_layers.to_kvs[self.k_key](cond).repeat(b, 1, 1)
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k_uncond = instantid.ip_layers.to_kvs[self.k_key](uncond).repeat(batch_prompt, 1, 1)
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v_cond = instantid.ip_layers.to_kvs[self.v_key](cond).repeat(b, 1, 1)
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v_uncond = instantid.ip_layers.to_kvs[self.v_key](uncond).repeat(batch_prompt, 1, 1)
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iid_k = torch.cat([(k_cond, k_uncond)[i] for i in cond_or_uncond], dim=0)
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iid_v = torch.cat([(v_cond, v_uncond)[i] for i in cond_or_uncond], dim=0)
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ip_k = torch.cat([(k_cond, k_uncond)[i] for i in cond_or_uncond], dim=0)
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ip_v = torch.cat([(v_cond, v_uncond)[i] for i in cond_or_uncond], dim=0)
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out_iid = optimized_attention(q, iid_k, iid_v, extra_options["n_heads"])
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out_iid = out_iid * weight
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out_iid = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
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out_iid = out_iid * weight
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if mask is not None:
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# TODO: needs checking
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mask_h = lh / math.sqrt(lh * lw / qs)
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mask_h = int(mask_h) + int((qs % int(mask_h)) != 0)
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mask_w = qs // mask_h
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out = out + out_iid
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mask_downsample = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bicubic").squeeze(1)
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# if we don't have enough masks repeat the last one until we reach the right size
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if mask_downsample.shape[0] < batch_prompt:
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mask_downsample = torch.cat((mask_downsample, mask_downsample[-1:, :, :].repeat((batch_prompt-mask_downsample.shape[0], 1, 1))), dim=0)
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# if we have too many remove the exceeding
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elif mask_downsample.shape[0] > batch_prompt:
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mask_downsample = mask_downsample[:batch_prompt, :, :]
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# repeat the masks
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mask_downsample = mask_downsample.repeat(len(cond_or_uncond), 1, 1)
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mask_downsample = mask_downsample.view(mask_downsample.shape[0], -1, 1).repeat(1, 1, out.shape[2])
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out_ip = out_ip * mask_downsample
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out = out + out_iid
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return out.to(dtype=org_dtype)
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class InstantID(torch.nn.Module):
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def __init__(self, instantid_model, cross_attention_dim=1024, output_cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
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def __init__(self, instantid_model, cross_attention_dim=1280, output_cross_attention_dim=1024, clip_embeddings_dim=512, clip_extra_context_tokens=16):
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super().__init__()
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self.clip_embeddings_dim = clip_embeddings_dim
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@@ -150,13 +159,10 @@ class InstantID(torch.nn.Module):
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@torch.inference_mode()
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def get_image_embeds(self, clip_embed, clip_embed_zeroed):
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image_prompt_embeds = clip_embed.clone().detach()
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image_prompt_embeds = self.image_proj_model(image_prompt_embeds)
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#image_prompt_embeds = image_prompt_embeds.reshape([1, -1, 512])
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uncond_image_prompt_embeds = clip_embed_zeroed.clone().detach()
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uncond_image_prompt_embeds = self.image_proj_model(uncond_image_prompt_embeds)
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#uncond_image_prompt_embeds = uncond_image_prompt_embeds.reshape([1, -1, 512])
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#image_prompt_embeds = clip_embed.clone().detach()
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image_prompt_embeds = self.image_proj_model(clip_embed)
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#uncond_image_prompt_embeds = clip_embed_zeroed.clone().detach()
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uncond_image_prompt_embeds = self.image_proj_model(clip_embed_zeroed)
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return image_prompt_embeds, uncond_image_prompt_embeds
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@@ -181,8 +187,9 @@ class To_KV(torch.nn.Module):
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self.to_kvs = torch.nn.ModuleDict()
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for key, value in state_dict.items():
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self.to_kvs[key.replace(".weight", "").replace(".", "_")] = torch.nn.Linear(value.shape[1], value.shape[0], bias=False)
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self.to_kvs[key.replace(".weight", "").replace(".", "_")].weight.data = value
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k = key.replace(".weight", "").replace(".", "_")
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self.to_kvs[k] = torch.nn.Linear(value.shape[1], value.shape[0], bias=False)
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self.to_kvs[k].weight.data = value
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def set_model_patch_replace(model, patch_kwargs, key):
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to = model.model_options["transformer_options"]
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@@ -196,7 +203,6 @@ def set_model_patch_replace(model, patch_kwargs, key):
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else:
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to["patches_replace"]["attn2"][key].set_new_condition(**patch_kwargs)
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class InstantIDModelLoader:
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@classmethod
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def INPUT_TYPES(s):
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@@ -222,7 +228,45 @@ class InstantIDModelLoader:
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return (model,)
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class InsightFaceLoader:
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def tensorToNP(image):
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out = torch.clamp(255. * image.detach().cpu(), 0, 255).to(torch.uint8)
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out = out[..., [2, 1, 0]]
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out = out.numpy()
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return out
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def extractFeatures(insightface, image, extract_kps=False):
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face_img = tensorToNP(image)
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out = []
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insightface.det_model.input_size = (640,640) # reset the detection size
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for i in range(face_img.shape[0]):
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for size in [(size, size) for size in range(640, 128, -64)]:
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insightface.det_model.input_size = size # TODO: hacky but seems to be working
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face = insightface.get(face_img[i])
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if face:
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face = sorted(face, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1]
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if extract_kps:
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out.append(draw_kps(face_img[i], face['kps']))
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else:
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out.append(torch.from_numpy(face['embedding']).unsqueeze(0))
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if 640 not in size:
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print(f"\033[33mINFO: InsightFace detection resolution lowered to {size}.\033[0m")
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break
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if out:
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if extract_kps:
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out = torch.stack(T.ToTensor()(out), dim=0).permute([0,2,3,1])
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else:
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out = torch.stack(out, dim=0)
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else:
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out = None
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return out
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class InstantIDFaceAnalysis:
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@classmethod
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def INPUT_TYPES(s):
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return {
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@@ -231,7 +275,7 @@ class InsightFaceLoader:
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},
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}
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RETURN_TYPES = ("INSIGHTFACE",)
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RETURN_TYPES = ("FACEANALYSIS",)
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FUNCTION = "load_insight_face"
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CATEGORY = "InstantID"
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@@ -241,12 +285,28 @@ class InsightFaceLoader:
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return (model,)
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def tensorToNP(image):
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out = torch.clamp(255. * image.detach().cpu(), 0, 255).to(torch.uint8)
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out = out[..., [2, 1, 0]]
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out = out.numpy()
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class FaceKeypointsPreprocessor:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"faceanalysis": ("FACEANALYSIS", ),
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"image": ("IMAGE", ),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "preprocess_image"
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CATEGORY = "InstantID"
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return out
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def preprocess_image(self, faceanalysis, image):
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face_kps = extractFeatures(faceanalysis, image, extract_kps=True)
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if face_kps is None:
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face_kps = torch.zeros_like(image)
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print(f"\033[33mWARNING: no face detected, unable to extract the keypoints!\033[0m")
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#raise Exception('Face Keypoints Image: No face detected.')
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return (face_kps,)
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class ApplyInstantID:
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@classmethod
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@@ -254,47 +314,38 @@ class ApplyInstantID:
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return {
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"required": {
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"instantid": ("INSTANTID", ),
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"insightface": ("INSIGHTFACE", ),
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"insightface": ("FACEANALYSIS", ),
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"image_features": ("IMAGE", ),
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"model": ("MODEL", ),
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"image": ("IMAGE", )
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"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"weight": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01,}),
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"start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001,}),
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"end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001,}),
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},
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"optional": {
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"attn_mask": ("MASK",),
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}
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}
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RETURN_TYPES = ("MODEL", "IMAGE")
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RETURN_NAMES = ("MODEL", "IMAGE_KPS")
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RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING",)
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RETURN_NAMES = ("MODEL", "POSITIVE", "NEGATIVE", )
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FUNCTION = "apply_instantid"
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CATEGORY = "InstantID"
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def apply_instantid(self, instantid, insightface, model, image):
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def apply_instantid(self, instantid, insightface, image_features, model, positive, negative, weight, start_at, end_at, attn_mask=None):
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self.dtype = torch.float16 if comfy.model_management.should_use_fp16() else torch.float32
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self.device = comfy.model_management.get_torch_device()
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self.weight = 1.0
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self.weight = weight
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output_cross_attention_dim = instantid["ip_adapter"]["1.to_k_ip.weight"].shape[1]
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is_sdxl = output_cross_attention_dim == 2048
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cross_attention_dim = 1280
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clip_extra_context_tokens = 16
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insightface.det_model.input_size = (640,640) # reset the detection size
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face_img = tensorToNP(image)
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face_embed = []
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face_kps = []
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for i in range(face_img.shape[0]):
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for size in [(size, size) for size in range(640, 128, -64)]:
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insightface.det_model.input_size = size # TODO: hacky but seems to be working
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face = insightface.get(face_img[i])
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if face:
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face_embed.append(torch.from_numpy(face[0].embedding).unsqueeze(0))
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face_kps.append(draw_kps(face_img[i], face[0].kps))
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if 640 not in size:
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print(f"\033[33mINFO: InsightFace detection resolution lowered to {size}.\033[0m")
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break
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else:
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raise Exception('InsightFace: No face detected.')
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face_embed = torch.stack(face_embed, dim=0)
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face_kps = torch.stack(T.ToTensor()(face_kps), dim=0).permute([0,2,3,1])
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face_embed = extractFeatures(insightface, image_features)
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if face_embed is None:
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raise Exception('Feature Extractor: No face detected.')
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clip_embed = face_embed
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clip_embed_zeroed = torch.zeros_like(clip_embed)
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@@ -318,38 +369,65 @@ class ApplyInstantID:
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work_model = model.clone()
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sigma_start = work_model.model.model_sampling.percent_to_sigma(start_at)
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sigma_end = work_model.model.model_sampling.percent_to_sigma(end_at)
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if attn_mask is not None:
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attn_mask = attn_mask.to(self.device)
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patch_kwargs = {
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"number": 0,
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"weight": self.weight,
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"instantid": self.instantid,
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"cond": image_prompt_embeds,
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"uncond": uncond_image_prompt_embeds,
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"mask": attn_mask,
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"sigma_start": sigma_start,
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"sigma_end": sigma_end,
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}
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for id in [4,5,7,8]: # id of input_blocks that have cross attention
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block_indices = range(2) if id in [4, 5] else range(10) # transformer_depth
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for index in block_indices:
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set_model_patch_replace(work_model, patch_kwargs, ("input", id, index))
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if not is_sdxl:
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for id in [1,2,4,5,7,8]: # id of input_blocks that have cross attention
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set_model_patch_replace(work_model, patch_kwargs, ("input", id))
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patch_kwargs["number"] += 1
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for id in range(6): # id of output_blocks that have cross attention
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block_indices = range(2) if id in [3, 4, 5] else range(10) # transformer_depth
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for index in block_indices:
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set_model_patch_replace(work_model, patch_kwargs, ("output", id, index))
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for id in [3,4,5,6,7,8,9,10,11]: # id of output_blocks that have cross attention
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set_model_patch_replace(work_model, patch_kwargs, ("output", id))
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patch_kwargs["number"] += 1
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set_model_patch_replace(work_model, patch_kwargs, ("middle", 0))
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else:
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for id in [4,5,7,8]: # id of input_blocks that have cross attention
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block_indices = range(2) if id in [4, 5] else range(10) # transformer_depth
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for index in block_indices:
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set_model_patch_replace(work_model, patch_kwargs, ("input", id, index))
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patch_kwargs["number"] += 1
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for id in range(6): # id of output_blocks that have cross attention
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block_indices = range(2) if id in [3, 4, 5] else range(10) # transformer_depth
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for index in block_indices:
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set_model_patch_replace(work_model, patch_kwargs, ("output", id, index))
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patch_kwargs["number"] += 1
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for index in range(10):
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set_model_patch_replace(work_model, patch_kwargs, ("middle", 0, index))
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patch_kwargs["number"] += 1
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for index in range(10):
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set_model_patch_replace(work_model, patch_kwargs, ("middle", 0, index))
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patch_kwargs["number"] += 1
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return(work_model, face_kps, )
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pos = positive.copy()
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print(pos[0][1].keys())
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pos[0][1]['cross_attn_controlnet'] = image_prompt_embeds.cpu()
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neg = negative.copy()
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neg[0][1]['cross_attn_controlnet'] = uncond_image_prompt_embeds.cpu()
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return(work_model, pos, neg, )
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NODE_CLASS_MAPPINGS = {
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"InstantIDModelLoader": InstantIDModelLoader,
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"InsightFaceLoaderIID": InsightFaceLoader,
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"InstantIDFaceAnalysis": InstantIDFaceAnalysis,
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"ApplyInstantID": ApplyInstantID,
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"FaceKeypointsPreprocessor": FaceKeypointsPreprocessor,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"InstantIDModelLoader": "Load InstantID Model",
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"InsightFaceLoaderIID": "Load InsightFace IID",
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"InstantIDFaceAnalysis": "InstantID Face Analysis",
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"ApplyInstantID": "Apply InstantID",
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"FaceKeypointsPreprocessor": "Face Keypoints Preprocessor",
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}
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@@ -1,9 +1,42 @@
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## NOT WORKING YET!! do not use
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# ComfyUI InstantID (Native Support)
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Initial work to support [InstandID](https://github.com/InstantID/InstantID) natively in ComfyUI.
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Native [InstantID](https://github.com/InstantID/InstantID) support for [ComfyUI](https://github.com/comfyanonymous/ComfyUI).
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This is mostly a placeholder, more work is needed... if I get the time.
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This extension differs from the many already available as it doesn't use *diffusers* but instead implements InstantID natively and it fully integrates with ComfyUI.
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||||
|
||||
Model go in ComfyUI/models/instantid, you need "antelopev2" models for insightface.
|
||||
Please note this still could be considered beta stage, looking forward to your feedback.
|
||||
|
||||
This repo is temporary and might be removed.
|
||||
## Basic Workflow
|
||||
|
||||
In the `examples` directory you'll find some basic workflows.
|
||||
|
||||
|
||||
|
||||
## Installation
|
||||
|
||||
**Upgrade ComfyUI to the latest version!** ComfyUI required a small update to work with InstantID that was pushed recently.
|
||||
|
||||
Download or `git clone` this repository into the `ComfyUI/custom_nodes/` directory. I guess the Manager will soon have this added to the list.
|
||||
|
||||
InstantID requires `insightface`, you need to add it to your libraries together with `onnxruntine` and `onnxruntime-gpu`.
|
||||
|
||||
The **main model** can be downloaded from [HuggingFace](https://huggingface.co/InstantX/InstantID/resolve/main/ip-adapter.bin?download=true) and should be placed into the `ComfyUI/models/instantid` directory. (Note that the model is called *ip_adapter* as it is based on the [IPAdapter](https://github.com/tencent-ailab/IP-Adapter) models).
|
||||
|
||||
You also needs a [controlnet](https://huggingface.co/InstantX/InstantID/resolve/main/ControlNetModel/diffusion_pytorch_model.safetensors?download=true), place it in the ComfyUI controlnet directory.
|
||||
|
||||
**Remember at the moment this is only for SDXL.**
|
||||
|
||||
## Watermarks!
|
||||
|
||||
The training data is full of watermarks, to avoid them to show up in your generations use a resolution slightly different from 1024×1024 for example **1016×1016** works pretty well.
|
||||
|
||||
## Lower the CFG!
|
||||
|
||||
It's important to lower the CFG to at least 4/5 or you can use the `RescaleCFG` node.
|
||||
|
||||
## Other notes
|
||||
|
||||
It works very well with SDXL Turbo. Best results with community's checkpoints.
|
||||
<div style="text-align:center">
|
||||
<img src="examples/daydreaming.jpg" width="386" height="386" alt="Day Dreaming" />
|
||||
</div>
|
||||
@@ -0,0 +1,794 @@
|
||||
{
|
||||
"last_node_id": 59,
|
||||
"last_link_id": 197,
|
||||
"nodes": [
|
||||
{
|
||||
"id": 8,
|
||||
"type": "VAEDecode",
|
||||
"pos": [
|
||||
2200,
|
||||
410
|
||||
],
|
||||
"size": {
|
||||
"0": 210,
|
||||
"1": 46
|
||||
},
|
||||
"flags": {},
|
||||
"order": 12,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "samples",
|
||||
"type": "LATENT",
|
||||
"link": 7
|
||||
},
|
||||
{
|
||||
"name": "vae",
|
||||
"type": "VAE",
|
||||
"link": 8
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
19
|
||||
],
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "VAEDecode"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 5,
|
||||
"type": "EmptyLatentImage",
|
||||
"pos": [
|
||||
1410,
|
||||
610
|
||||
],
|
||||
"size": {
|
||||
"0": 315,
|
||||
"1": 106
|
||||
},
|
||||
"flags": {},
|
||||
"order": 0,
|
||||
"mode": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "LATENT",
|
||||
"type": "LATENT",
|
||||
"links": [
|
||||
2
|
||||
],
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "EmptyLatentImage"
|
||||
},
|
||||
"widgets_values": [
|
||||
1016,
|
||||
1016,
|
||||
1
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 23,
|
||||
"type": "ControlNetApplyAdvanced",
|
||||
"pos": [
|
||||
1410,
|
||||
380
|
||||
],
|
||||
"size": {
|
||||
"0": 315,
|
||||
"1": 166
|
||||
},
|
||||
"flags": {},
|
||||
"order": 10,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "positive",
|
||||
"type": "CONDITIONING",
|
||||
"link": 184
|
||||
},
|
||||
{
|
||||
"name": "negative",
|
||||
"type": "CONDITIONING",
|
||||
"link": 185
|
||||
},
|
||||
{
|
||||
"name": "control_net",
|
||||
"type": "CONTROL_NET",
|
||||
"link": 53
|
||||
},
|
||||
{
|
||||
"name": "image",
|
||||
"type": "IMAGE",
|
||||
"link": 192
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "positive",
|
||||
"type": "CONDITIONING",
|
||||
"links": [
|
||||
95
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
},
|
||||
{
|
||||
"name": "negative",
|
||||
"type": "CONDITIONING",
|
||||
"links": [
|
||||
102
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 1
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "ControlNetApplyAdvanced"
|
||||
},
|
||||
"widgets_values": [
|
||||
0.65,
|
||||
0,
|
||||
1
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 16,
|
||||
"type": "ControlNetLoader",
|
||||
"pos": [
|
||||
1050,
|
||||
540
|
||||
],
|
||||
"size": {
|
||||
"0": 250.07241821289062,
|
||||
"1": 58
|
||||
},
|
||||
"flags": {},
|
||||
"order": 1,
|
||||
"mode": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "CONTROL_NET",
|
||||
"type": "CONTROL_NET",
|
||||
"links": [
|
||||
53
|
||||
],
|
||||
"shape": 3
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "ControlNetLoader"
|
||||
},
|
||||
"widgets_values": [
|
||||
"instantid/diffusion_pytorch_model.safetensors"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 4,
|
||||
"type": "CheckpointLoaderSimple",
|
||||
"pos": [
|
||||
80,
|
||||
670
|
||||
],
|
||||
"size": {
|
||||
"0": 315,
|
||||
"1": 98
|
||||
},
|
||||
"flags": {},
|
||||
"order": 2,
|
||||
"mode": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "MODEL",
|
||||
"type": "MODEL",
|
||||
"links": [
|
||||
182
|
||||
],
|
||||
"slot_index": 0
|
||||
},
|
||||
{
|
||||
"name": "CLIP",
|
||||
"type": "CLIP",
|
||||
"links": [
|
||||
122,
|
||||
123
|
||||
],
|
||||
"slot_index": 1
|
||||
},
|
||||
{
|
||||
"name": "VAE",
|
||||
"type": "VAE",
|
||||
"links": [
|
||||
8
|
||||
],
|
||||
"slot_index": 2
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "CheckpointLoaderSimple"
|
||||
},
|
||||
"widgets_values": [
|
||||
"sdxl/TurboVisionXL.safetensors"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 58,
|
||||
"type": "FaceKeypointsPreprocessor",
|
||||
"pos": [
|
||||
1060,
|
||||
640
|
||||
],
|
||||
"size": {
|
||||
"0": 229.20001220703125,
|
||||
"1": 46
|
||||
},
|
||||
"flags": {},
|
||||
"order": 8,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "faceanalysis",
|
||||
"type": "FACEANALYSIS",
|
||||
"link": 193
|
||||
},
|
||||
{
|
||||
"name": "image",
|
||||
"type": "IMAGE",
|
||||
"link": 197
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
192
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "FaceKeypointsPreprocessor"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 11,
|
||||
"type": "InstantIDModelLoader",
|
||||
"pos": [
|
||||
690,
|
||||
90
|
||||
],
|
||||
"size": {
|
||||
"0": 238.72393798828125,
|
||||
"1": 58
|
||||
},
|
||||
"flags": {},
|
||||
"order": 3,
|
||||
"mode": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "INSTANTID",
|
||||
"type": "INSTANTID",
|
||||
"links": [
|
||||
189
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "InstantIDModelLoader"
|
||||
},
|
||||
"widgets_values": [
|
||||
"ip-adapter.bin"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 38,
|
||||
"type": "InstantIDFaceAnalysis",
|
||||
"pos": [
|
||||
700,
|
||||
200
|
||||
],
|
||||
"size": {
|
||||
"0": 227.09793090820312,
|
||||
"1": 58
|
||||
},
|
||||
"flags": {},
|
||||
"order": 4,
|
||||
"mode": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "FACEANALYSIS",
|
||||
"type": "FACEANALYSIS",
|
||||
"links": [
|
||||
190,
|
||||
193
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "InstantIDFaceAnalysis"
|
||||
},
|
||||
"widgets_values": [
|
||||
"CPU"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 40,
|
||||
"type": "CLIPTextEncode",
|
||||
"pos": [
|
||||
560,
|
||||
830
|
||||
],
|
||||
"size": {
|
||||
"0": 286.3603515625,
|
||||
"1": 112.35245513916016
|
||||
},
|
||||
"flags": {},
|
||||
"order": 7,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "clip",
|
||||
"type": "CLIP",
|
||||
"link": 123
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "CONDITIONING",
|
||||
"type": "CONDITIONING",
|
||||
"links": [
|
||||
187
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "CLIPTextEncode"
|
||||
},
|
||||
"widgets_values": [
|
||||
"low quality, blurry, malformed, distorted"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 15,
|
||||
"type": "PreviewImage",
|
||||
"pos": [
|
||||
2200,
|
||||
510
|
||||
],
|
||||
"size": {
|
||||
"0": 584.0855712890625,
|
||||
"1": 610.4592895507812
|
||||
},
|
||||
"flags": {},
|
||||
"order": 13,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "images",
|
||||
"type": "IMAGE",
|
||||
"link": 19
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "PreviewImage"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 13,
|
||||
"type": "LoadImage",
|
||||
"pos": [
|
||||
410,
|
||||
270
|
||||
],
|
||||
"size": {
|
||||
"0": 210,
|
||||
"1": 290
|
||||
},
|
||||
"flags": {},
|
||||
"order": 5,
|
||||
"mode": 0,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
188,
|
||||
197
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
},
|
||||
{
|
||||
"name": "MASK",
|
||||
"type": "MASK",
|
||||
"links": null,
|
||||
"shape": 3
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "LoadImage"
|
||||
},
|
||||
"widgets_values": [
|
||||
"face4.jpg",
|
||||
"image"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 57,
|
||||
"type": "ApplyInstantID",
|
||||
"pos": [
|
||||
1040,
|
||||
260
|
||||
],
|
||||
"size": [
|
||||
260,
|
||||
226
|
||||
],
|
||||
"flags": {},
|
||||
"order": 9,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "instantid",
|
||||
"type": "INSTANTID",
|
||||
"link": 189
|
||||
},
|
||||
{
|
||||
"name": "insightface",
|
||||
"type": "FACEANALYSIS",
|
||||
"link": 190
|
||||
},
|
||||
{
|
||||
"name": "image_features",
|
||||
"type": "IMAGE",
|
||||
"link": 188
|
||||
},
|
||||
{
|
||||
"name": "model",
|
||||
"type": "MODEL",
|
||||
"link": 182
|
||||
},
|
||||
{
|
||||
"name": "positive",
|
||||
"type": "CONDITIONING",
|
||||
"link": 186
|
||||
},
|
||||
{
|
||||
"name": "negative",
|
||||
"type": "CONDITIONING",
|
||||
"link": 187
|
||||
},
|
||||
{
|
||||
"name": "attn_mask",
|
||||
"type": "MASK",
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "MODEL",
|
||||
"type": "MODEL",
|
||||
"links": [
|
||||
196
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
},
|
||||
{
|
||||
"name": "POSITIVE",
|
||||
"type": "CONDITIONING",
|
||||
"links": [
|
||||
184
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 1
|
||||
},
|
||||
{
|
||||
"name": "NEGATIVE",
|
||||
"type": "CONDITIONING",
|
||||
"links": [
|
||||
185
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 2
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "ApplyInstantID"
|
||||
},
|
||||
"widgets_values": [
|
||||
0.8,
|
||||
0,
|
||||
1
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 39,
|
||||
"type": "CLIPTextEncode",
|
||||
"pos": [
|
||||
560,
|
||||
650
|
||||
],
|
||||
"size": {
|
||||
"0": 291.9967346191406,
|
||||
"1": 128.62518310546875
|
||||
},
|
||||
"flags": {},
|
||||
"order": 6,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "clip",
|
||||
"type": "CLIP",
|
||||
"link": 122
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "CONDITIONING",
|
||||
"type": "CONDITIONING",
|
||||
"links": [
|
||||
186
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "CLIPTextEncode"
|
||||
},
|
||||
"widgets_values": [
|
||||
"watercolors portrait of a woman (happy laughing:1.15), masterpiece, artistry"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 3,
|
||||
"type": "KSampler",
|
||||
"pos": [
|
||||
1810,
|
||||
290
|
||||
],
|
||||
"size": {
|
||||
"0": 315,
|
||||
"1": 262
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Binary file not shown.
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After Width: | Height: | Size: 129 KiB |
Binary file not shown.
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After Width: | Height: | Size: 249 KiB |
@@ -0,0 +1,3 @@
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insightface
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onnxruntime
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onnxruntime-gpu
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Reference in New Issue
Block a user