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Core ip_adapter files from https://github.com/tencent-ailab/IP-Adapter
Copied into InvokeAI since IP-Adapter repo is not a package. Is there a better way to do this for non-packaged Python code while still keeping InvokeAI install easy?
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user1
243
invokeai/backend/ip_adapter/ip_adapter.py
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243
invokeai/backend/ip_adapter/ip_adapter.py
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import os
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from typing import List
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import torch
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from diffusers import StableDiffusionPipeline
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from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
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from PIL import Image
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from .utils import is_torch2_available
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if is_torch2_available:
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from .attention_processor import IPAttnProcessor2_0 as IPAttnProcessor, AttnProcessor2_0 as AttnProcessor
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else:
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from .attention_processor import IPAttnProcessor, AttnProcessor
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from .resampler import Resampler
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class ImageProjModel(torch.nn.Module):
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"""Projection Model"""
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def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
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super().__init__()
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self.cross_attention_dim = cross_attention_dim
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self.clip_extra_context_tokens = clip_extra_context_tokens
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self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
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self.norm = torch.nn.LayerNorm(cross_attention_dim)
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def forward(self, image_embeds):
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embeds = image_embeds
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clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim)
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clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
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return clip_extra_context_tokens
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class IPAdapter:
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def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_tokens=4):
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self.device = device
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self.image_encoder_path = image_encoder_path
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self.ip_ckpt = ip_ckpt
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self.num_tokens = num_tokens
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self.pipe = sd_pipe.to(self.device)
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self.set_ip_adapter()
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# load image encoder
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self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(self.device, dtype=torch.float16)
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self.clip_image_processor = CLIPImageProcessor()
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# image proj model
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self.image_proj_model = self.init_proj()
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self.load_ip_adapter()
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def init_proj(self):
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image_proj_model = ImageProjModel(
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cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
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clip_embeddings_dim=self.image_encoder.config.projection_dim,
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clip_extra_context_tokens=self.num_tokens,
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).to(self.device, dtype=torch.float16)
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return image_proj_model
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def set_ip_adapter(self):
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unet = self.pipe.unet
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attn_procs = {}
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for name in unet.attn_processors.keys():
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cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
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if name.startswith("mid_block"):
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hidden_size = unet.config.block_out_channels[-1]
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elif name.startswith("up_blocks"):
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block_id = int(name[len("up_blocks.")])
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hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
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elif name.startswith("down_blocks"):
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block_id = int(name[len("down_blocks.")])
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hidden_size = unet.config.block_out_channels[block_id]
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if cross_attention_dim is None:
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attn_procs[name] = AttnProcessor()
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else:
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attn_procs[name] = IPAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim,
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scale=1.0).to(self.device, dtype=torch.float16)
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unet.set_attn_processor(attn_procs)
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def load_ip_adapter(self):
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state_dict = torch.load(self.ip_ckpt, map_location="cpu")
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self.image_proj_model.load_state_dict(state_dict["image_proj"])
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ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
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ip_layers.load_state_dict(state_dict["ip_adapter"])
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@torch.inference_mode()
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def get_image_embeds(self, pil_image):
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if isinstance(pil_image, Image.Image):
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pil_image = [pil_image]
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clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
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clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
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image_prompt_embeds = self.image_proj_model(clip_image_embeds)
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uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(clip_image_embeds))
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return image_prompt_embeds, uncond_image_prompt_embeds
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def set_scale(self, scale):
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for attn_processor in self.pipe.unet.attn_processors.values():
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if isinstance(attn_processor, IPAttnProcessor):
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attn_processor.scale = scale
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def generate(
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self,
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pil_image,
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prompt=None,
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negative_prompt=None,
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scale=1.0,
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num_samples=4,
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seed=-1,
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guidance_scale=7.5,
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num_inference_steps=30,
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**kwargs,
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):
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self.set_scale(scale)
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if isinstance(pil_image, Image.Image):
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num_prompts = 1
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else:
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num_prompts = len(pil_image)
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if prompt is None:
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prompt = "best quality, high quality"
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if negative_prompt is None:
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negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
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if not isinstance(prompt, List):
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prompt = [prompt] * num_prompts
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if not isinstance(negative_prompt, List):
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negative_prompt = [negative_prompt] * num_prompts
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image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(pil_image)
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bs_embed, seq_len, _ = image_prompt_embeds.shape
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image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
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image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
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uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
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uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
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with torch.inference_mode():
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prompt_embeds = self.pipe._encode_prompt(
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prompt, device=self.device, num_images_per_prompt=num_samples, do_classifier_free_guidance=True, negative_prompt=negative_prompt)
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negative_prompt_embeds_, prompt_embeds_ = prompt_embeds.chunk(2)
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prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
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negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)
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generator = torch.Generator(self.device).manual_seed(seed) if seed is not None else None
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images = self.pipe(
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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guidance_scale=guidance_scale,
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num_inference_steps=num_inference_steps,
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generator=generator,
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**kwargs,
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).images
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return images
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class IPAdapterXL(IPAdapter):
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"""SDXL"""
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def generate(
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self,
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pil_image,
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prompt=None,
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negative_prompt=None,
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scale=1.0,
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num_samples=4,
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seed=-1,
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num_inference_steps=30,
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**kwargs,
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):
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self.set_scale(scale)
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if isinstance(pil_image, Image.Image):
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num_prompts = 1
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else:
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num_prompts = len(pil_image)
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if prompt is None:
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prompt = "best quality, high quality"
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if negative_prompt is None:
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negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
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if not isinstance(prompt, List):
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prompt = [prompt] * num_prompts
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if not isinstance(negative_prompt, List):
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negative_prompt = [negative_prompt] * num_prompts
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image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(pil_image)
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bs_embed, seq_len, _ = image_prompt_embeds.shape
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image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
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image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
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uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
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uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
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with torch.inference_mode():
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prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = self.pipe.encode_prompt(
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prompt, num_images_per_prompt=num_samples, do_classifier_free_guidance=True, negative_prompt=negative_prompt)
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prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
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negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_image_prompt_embeds], dim=1)
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generator = torch.Generator(self.device).manual_seed(seed) if seed is not None else None
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images = self.pipe(
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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pooled_prompt_embeds=pooled_prompt_embeds,
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negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
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num_inference_steps=num_inference_steps,
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generator=generator,
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**kwargs,
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).images
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return images
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class IPAdapterPlus(IPAdapter):
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"""IP-Adapter with fine-grained features"""
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def init_proj(self):
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image_proj_model = Resampler(
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dim=self.pipe.unet.config.cross_attention_dim,
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depth=4,
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dim_head=64,
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heads=12,
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num_queries=self.num_tokens,
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embedding_dim=self.image_encoder.config.hidden_size,
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output_dim=self.pipe.unet.config.cross_attention_dim,
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ff_mult=4
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).to(self.device, dtype=torch.float16)
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return image_proj_model
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@torch.inference_mode()
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def get_image_embeds(self, pil_image):
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if isinstance(pil_image, Image.Image):
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pil_image = [pil_image]
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clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
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clip_image = clip_image.to(self.device, dtype=torch.float16)
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clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
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image_prompt_embeds = self.image_proj_model(clip_image_embeds)
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uncond_clip_image_embeds = self.image_encoder(torch.zeros_like(clip_image), output_hidden_states=True).hidden_states[-2]
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uncond_image_prompt_embeds = self.image_proj_model(uncond_clip_image_embeds)
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return image_prompt_embeds, uncond_image_prompt_embeds
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