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Browse files- .gitignore +3 -0
- README.md +1 -1
- app.py +393 -0
- assets/0.jpg +0 -0
- assets/1.jpg +0 -0
- assets/2.jpg +0 -0
- assets/3.jpg +0 -0
- assets/yann-lecun.jpg +0 -0
- ip_adapter/__init__.py +9 -0
- ip_adapter/attention_processor.py +562 -0
- ip_adapter/ip_adapter.py +461 -0
- ip_adapter/resampler.py +158 -0
- ip_adapter/utils.py +93 -0
- requirements.txt +16 -0
.gitignore
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__pycache__
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sdxl_models/
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gradio_cached_examples/
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README.md
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---
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-
title: InstantStyle
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emoji: 👁
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colorFrom: blue
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colorTo: purple
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---
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title: InstantStyle + SDXL Lightning
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emoji: 👁
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colorFrom: blue
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colorTo: purple
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app.py
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import cv2
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import torch
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import random
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import numpy as np
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from PIL import Image
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from diffusers import (
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ControlNetModel,
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StableDiffusionXLControlNetPipeline,
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UNet2DConditionModel,
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EulerDiscreteScheduler,
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)
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import spaces
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import gradio as gr
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from huggingface_hub import hf_hub_download, snapshot_download
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from ip_adapter import IPAdapterXL
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from safetensors.torch import load_file
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snapshot_download(
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repo_id="h94/IP-Adapter", allow_patterns="sdxl_models/*", local_dir="."
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)
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# global variable
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MAX_SEED = np.iinfo(np.int32).max
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device = "cuda" if torch.cuda.is_available() else "cpu"
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dtype = torch.float16 if str(device).__contains__("cuda") else torch.float32
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# initialization
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base_model_path = "stabilityai/stable-diffusion-xl-base-1.0"
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image_encoder_path = "sdxl_models/image_encoder"
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ip_ckpt = "sdxl_models/ip-adapter_sdxl.bin"
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controlnet_path = "diffusers/controlnet-canny-sdxl-1.0"
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controlnet = ControlNetModel.from_pretrained(
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controlnet_path, use_safetensors=False, torch_dtype=torch.float16
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).to(device)
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# load SDXL lightnining
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pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
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base_model_path,
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controlnet=controlnet,
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torch_dtype=torch.float16,
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variant="fp16",
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add_watermarker=False,
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).to(device)
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pipe.scheduler = EulerDiscreteScheduler.from_config(
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pipe.scheduler.config, timestep_spacing="trailing", prediction_type="epsilon"
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)
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pipe.unet.load_state_dict(
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load_file(
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hf_hub_download(
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"ByteDance/SDXL-Lightning", "sdxl_lightning_2step_unet.safetensors"
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),
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device="cuda",
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)
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)
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# load ip-adapter
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# target_blocks=["block"] for original IP-Adapter
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# target_blocks=["up_blocks.0.attentions.1"] for style blocks only
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# target_blocks = ["up_blocks.0.attentions.1", "down_blocks.2.attentions.1"] # for style+layout blocks
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ip_model = IPAdapterXL(
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pipe,
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image_encoder_path,
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ip_ckpt,
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device,
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target_blocks=["up_blocks.0.attentions.1"],
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)
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+
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+
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def resize_img(
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input_image,
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max_side=1280,
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min_side=1024,
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size=None,
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pad_to_max_side=False,
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mode=Image.BILINEAR,
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base_pixel_number=64,
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):
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w, h = input_image.size
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if size is not None:
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w_resize_new, h_resize_new = size
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else:
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ratio = min_side / min(h, w)
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w, h = round(ratio * w), round(ratio * h)
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ratio = max_side / max(h, w)
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input_image = input_image.resize([round(ratio * w), round(ratio * h)], mode)
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w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number
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h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number
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input_image = input_image.resize([w_resize_new, h_resize_new], mode)
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if pad_to_max_side:
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res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255
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offset_x = (max_side - w_resize_new) // 2
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offset_y = (max_side - h_resize_new) // 2
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res[offset_y : offset_y + h_resize_new, offset_x : offset_x + w_resize_new] = (
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np.array(input_image)
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)
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input_image = Image.fromarray(res)
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return input_image
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examples = [
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[
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"./assets/0.jpg",
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None,
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"a cat, masterpiece, best quality, high quality",
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1.0,
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0.0,
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],
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[
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"./assets/1.jpg",
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None,
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+
"a cat, masterpiece, best quality, high quality",
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1.0,
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0.0,
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],
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[
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"./assets/2.jpg",
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None,
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"a cat, masterpiece, best quality, high quality",
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1.0,
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+
0.0,
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+
],
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+
[
|
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"./assets/3.jpg",
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None,
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"a cat, masterpiece, best quality, high quality",
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+
1.0,
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+
0.0,
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131 |
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],
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[
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"./assets/2.jpg",
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"./assets/yann-lecun.jpg",
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"a man, masterpiece, best quality, high quality",
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1.0,
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0.6,
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],
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]
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def run_for_examples(style_image, source_image, prompt, scale, control_scale):
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return create_image(
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image_pil=style_image,
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input_image=source_image,
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prompt=prompt,
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n_prompt="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
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scale=scale,
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control_scale=control_scale,
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guidance_scale=0.0,
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num_inference_steps=2,
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seed=42,
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target="Load only style blocks",
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neg_content_prompt="",
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neg_content_scale=0,
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)
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@spaces.GPU(enable_queue=True)
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def create_image(
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image_pil,
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input_image,
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prompt,
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n_prompt,
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scale,
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control_scale,
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guidance_scale,
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num_inference_steps,
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seed,
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target="Load only style blocks",
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neg_content_prompt=None,
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neg_content_scale=0,
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):
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seed = random.randint(0, MAX_SEED) if seed == -1 else seed
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if target == "Load original IP-Adapter":
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# target_blocks=["blocks"] for original IP-Adapter
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ip_model = IPAdapterXL(
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pipe, image_encoder_path, ip_ckpt, device, target_blocks=["blocks"]
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)
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elif target == "Load only style blocks":
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# target_blocks=["up_blocks.0.attentions.1"] for style blocks only
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ip_model = IPAdapterXL(
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pipe,
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image_encoder_path,
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ip_ckpt,
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device,
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target_blocks=["up_blocks.0.attentions.1"],
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)
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189 |
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elif target == "Load style+layout block":
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190 |
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# target_blocks = ["up_blocks.0.attentions.1", "down_blocks.2.attentions.1"] # for style+layout blocks
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ip_model = IPAdapterXL(
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pipe,
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image_encoder_path,
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194 |
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ip_ckpt,
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device,
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target_blocks=["up_blocks.0.attentions.1", "down_blocks.2.attentions.1"],
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)
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198 |
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199 |
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if input_image is not None:
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input_image = resize_img(input_image, max_side=1024)
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201 |
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cv_input_image = pil_to_cv2(input_image)
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202 |
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detected_map = cv2.Canny(cv_input_image, 50, 200)
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203 |
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canny_map = Image.fromarray(cv2.cvtColor(detected_map, cv2.COLOR_BGR2RGB))
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204 |
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else:
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205 |
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canny_map = Image.new("RGB", (1024, 1024), color=(255, 255, 255))
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206 |
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control_scale = 0
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207 |
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208 |
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if float(control_scale) == 0:
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canny_map = canny_map.resize((1024, 1024))
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210 |
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211 |
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if len(neg_content_prompt) > 0 and neg_content_scale != 0:
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212 |
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images = ip_model.generate(
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213 |
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pil_image=image_pil,
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prompt=prompt,
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negative_prompt=n_prompt,
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216 |
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scale=scale,
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217 |
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guidance_scale=guidance_scale,
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num_samples=1,
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219 |
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num_inference_steps=num_inference_steps,
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220 |
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seed=seed,
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221 |
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image=canny_map,
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222 |
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controlnet_conditioning_scale=float(control_scale),
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neg_content_prompt=neg_content_prompt,
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224 |
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neg_content_scale=neg_content_scale,
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)
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else:
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images = ip_model.generate(
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pil_image=image_pil,
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229 |
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prompt=prompt,
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negative_prompt=n_prompt,
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scale=scale,
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guidance_scale=guidance_scale,
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num_samples=1,
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num_inference_steps=num_inference_steps,
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seed=seed,
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image=canny_map,
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controlnet_conditioning_scale=float(control_scale),
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)
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239 |
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return images
|
240 |
+
|
241 |
+
|
242 |
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def pil_to_cv2(image_pil):
|
243 |
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image_np = np.array(image_pil)
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244 |
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image_cv2 = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
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return image_cv2
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|
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|
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# Description
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title = r"""
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<h1 align="center">InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation</h1>
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"""
|
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description = r"""
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<b>Forked from <a href='https://github.com/InstantStyle/InstantStyle' target='_blank'><b>InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation</b></a>.<br>
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<b>Model by <a href='https://huggingface.co/ByteDance/SDXL-Lightning' target='_blank'>SDXL Lightning</a> and <a href='https://huggingface.co/h94/IP-Adapter' target='_blank'>IP-Adapter</a>.</b><br>
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"""
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|
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article = r"""
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---
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260 |
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📝 **Citation**
|
261 |
+
<br>
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+
If our work is helpful for your research or applications, please cite us via:
|
263 |
+
```bibtex
|
264 |
+
@article{wang2024instantstyle,
|
265 |
+
title={InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation},
|
266 |
+
author={Wang, Haofan and Wang, Qixun and Bai, Xu and Qin, Zekui and Chen, Anthony},
|
267 |
+
journal={arXiv preprint arXiv:2404.02733},
|
268 |
+
year={2024}
|
269 |
+
}
|
270 |
+
```
|
271 |
+
📧 **Contact**
|
272 |
+
<br>
|
273 |
+
If you have any questions, please feel free to open an issue or directly reach us out at <b>[email protected]</b>.
|
274 |
+
"""
|
275 |
+
|
276 |
+
block = gr.Blocks(css="footer {visibility: hidden}").queue(max_size=10, api_open=False)
|
277 |
+
with block:
|
278 |
+
# description
|
279 |
+
gr.Markdown(title)
|
280 |
+
gr.Markdown(description)
|
281 |
+
|
282 |
+
with gr.Tabs():
|
283 |
+
with gr.Row():
|
284 |
+
with gr.Column():
|
285 |
+
with gr.Row():
|
286 |
+
with gr.Column():
|
287 |
+
image_pil = gr.Image(label="Style Image", type="pil")
|
288 |
+
|
289 |
+
target = gr.Radio(
|
290 |
+
[
|
291 |
+
"Load only style blocks",
|
292 |
+
"Load style+layout block",
|
293 |
+
"Load original IP-Adapter",
|
294 |
+
],
|
295 |
+
value="Load only style blocks",
|
296 |
+
label="Style mode",
|
297 |
+
)
|
298 |
+
|
299 |
+
prompt = gr.Textbox(
|
300 |
+
label="Prompt",
|
301 |
+
value="a cat, masterpiece, best quality, high quality",
|
302 |
+
)
|
303 |
+
|
304 |
+
scale = gr.Slider(
|
305 |
+
minimum=0, maximum=2.0, step=0.01, value=1.0, label="Scale"
|
306 |
+
)
|
307 |
+
|
308 |
+
with gr.Accordion(open=False, label="Advanced Options"):
|
309 |
+
with gr.Column():
|
310 |
+
src_image_pil = gr.Image(
|
311 |
+
label="Source Image (optional)", type="pil"
|
312 |
+
)
|
313 |
+
control_scale = gr.Slider(
|
314 |
+
minimum=0,
|
315 |
+
maximum=1.0,
|
316 |
+
step=0.01,
|
317 |
+
value=0.5,
|
318 |
+
label="Controlnet conditioning scale",
|
319 |
+
)
|
320 |
+
|
321 |
+
n_prompt = gr.Textbox(
|
322 |
+
label="Neg Prompt",
|
323 |
+
value="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
|
324 |
+
)
|
325 |
+
|
326 |
+
neg_content_prompt = gr.Textbox(
|
327 |
+
label="Neg Content Prompt", value=""
|
328 |
+
)
|
329 |
+
neg_content_scale = gr.Slider(
|
330 |
+
minimum=0,
|
331 |
+
maximum=1.0,
|
332 |
+
step=0.01,
|
333 |
+
value=0.5,
|
334 |
+
label="Neg Content Scale",
|
335 |
+
)
|
336 |
+
|
337 |
+
guidance_scale = gr.Slider(
|
338 |
+
minimum=1,
|
339 |
+
maximum=15.0,
|
340 |
+
step=0.01,
|
341 |
+
value=5.0,
|
342 |
+
label="guidance scale",
|
343 |
+
)
|
344 |
+
num_inference_steps = gr.Slider(
|
345 |
+
minimum=2,
|
346 |
+
maximum=50.0,
|
347 |
+
step=1.0,
|
348 |
+
value=2,
|
349 |
+
label="num inference steps",
|
350 |
+
)
|
351 |
+
seed = gr.Slider(
|
352 |
+
minimum=-1,
|
353 |
+
maximum=MAX_SEED,
|
354 |
+
value=-1,
|
355 |
+
step=1,
|
356 |
+
label="Seed Value",
|
357 |
+
)
|
358 |
+
|
359 |
+
generate_button = gr.Button("Generate Image")
|
360 |
+
|
361 |
+
with gr.Column():
|
362 |
+
generated_image = gr.Gallery(label="Generated Image")
|
363 |
+
|
364 |
+
generate_button.click(
|
365 |
+
fn=create_image,
|
366 |
+
inputs=[
|
367 |
+
image_pil,
|
368 |
+
src_image_pil,
|
369 |
+
prompt,
|
370 |
+
n_prompt,
|
371 |
+
scale,
|
372 |
+
control_scale,
|
373 |
+
guidance_scale,
|
374 |
+
num_inference_steps,
|
375 |
+
seed,
|
376 |
+
target,
|
377 |
+
neg_content_prompt,
|
378 |
+
neg_content_scale,
|
379 |
+
],
|
380 |
+
outputs=[generated_image],
|
381 |
+
)
|
382 |
+
|
383 |
+
gr.Examples(
|
384 |
+
examples=examples,
|
385 |
+
inputs=[image_pil, src_image_pil, prompt, scale, control_scale],
|
386 |
+
fn=run_for_examples,
|
387 |
+
outputs=[generated_image],
|
388 |
+
cache_examples=True,
|
389 |
+
)
|
390 |
+
|
391 |
+
gr.Markdown(article)
|
392 |
+
|
393 |
+
block.launch()
|
assets/0.jpg
ADDED
assets/1.jpg
ADDED
assets/2.jpg
ADDED
assets/3.jpg
ADDED
assets/yann-lecun.jpg
ADDED
ip_adapter/__init__.py
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from .ip_adapter import IPAdapter, IPAdapterPlus, IPAdapterPlusXL, IPAdapterXL, IPAdapterFull
|
2 |
+
|
3 |
+
__all__ = [
|
4 |
+
"IPAdapter",
|
5 |
+
"IPAdapterPlus",
|
6 |
+
"IPAdapterPlusXL",
|
7 |
+
"IPAdapterXL",
|
8 |
+
"IPAdapterFull",
|
9 |
+
]
|
ip_adapter/attention_processor.py
ADDED
@@ -0,0 +1,562 @@
|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
|
2 |
+
import torch
|
3 |
+
import torch.nn as nn
|
4 |
+
import torch.nn.functional as F
|
5 |
+
|
6 |
+
|
7 |
+
class AttnProcessor(nn.Module):
|
8 |
+
r"""
|
9 |
+
Default processor for performing attention-related computations.
|
10 |
+
"""
|
11 |
+
|
12 |
+
def __init__(
|
13 |
+
self,
|
14 |
+
hidden_size=None,
|
15 |
+
cross_attention_dim=None,
|
16 |
+
):
|
17 |
+
super().__init__()
|
18 |
+
|
19 |
+
def __call__(
|
20 |
+
self,
|
21 |
+
attn,
|
22 |
+
hidden_states,
|
23 |
+
encoder_hidden_states=None,
|
24 |
+
attention_mask=None,
|
25 |
+
temb=None,
|
26 |
+
):
|
27 |
+
residual = hidden_states
|
28 |
+
|
29 |
+
if attn.spatial_norm is not None:
|
30 |
+
hidden_states = attn.spatial_norm(hidden_states, temb)
|
31 |
+
|
32 |
+
input_ndim = hidden_states.ndim
|
33 |
+
|
34 |
+
if input_ndim == 4:
|
35 |
+
batch_size, channel, height, width = hidden_states.shape
|
36 |
+
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
37 |
+
|
38 |
+
batch_size, sequence_length, _ = (
|
39 |
+
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
40 |
+
)
|
41 |
+
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
42 |
+
|
43 |
+
if attn.group_norm is not None:
|
44 |
+
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
45 |
+
|
46 |
+
query = attn.to_q(hidden_states)
|
47 |
+
|
48 |
+
if encoder_hidden_states is None:
|
49 |
+
encoder_hidden_states = hidden_states
|
50 |
+
elif attn.norm_cross:
|
51 |
+
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
52 |
+
|
53 |
+
key = attn.to_k(encoder_hidden_states)
|
54 |
+
value = attn.to_v(encoder_hidden_states)
|
55 |
+
|
56 |
+
query = attn.head_to_batch_dim(query)
|
57 |
+
key = attn.head_to_batch_dim(key)
|
58 |
+
value = attn.head_to_batch_dim(value)
|
59 |
+
|
60 |
+
attention_probs = attn.get_attention_scores(query, key, attention_mask)
|
61 |
+
hidden_states = torch.bmm(attention_probs, value)
|
62 |
+
hidden_states = attn.batch_to_head_dim(hidden_states)
|
63 |
+
|
64 |
+
# linear proj
|
65 |
+
hidden_states = attn.to_out[0](hidden_states)
|
66 |
+
# dropout
|
67 |
+
hidden_states = attn.to_out[1](hidden_states)
|
68 |
+
|
69 |
+
if input_ndim == 4:
|
70 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
71 |
+
|
72 |
+
if attn.residual_connection:
|
73 |
+
hidden_states = hidden_states + residual
|
74 |
+
|
75 |
+
hidden_states = hidden_states / attn.rescale_output_factor
|
76 |
+
|
77 |
+
return hidden_states
|
78 |
+
|
79 |
+
|
80 |
+
class IPAttnProcessor(nn.Module):
|
81 |
+
r"""
|
82 |
+
Attention processor for IP-Adapater.
|
83 |
+
Args:
|
84 |
+
hidden_size (`int`):
|
85 |
+
The hidden size of the attention layer.
|
86 |
+
cross_attention_dim (`int`):
|
87 |
+
The number of channels in the `encoder_hidden_states`.
|
88 |
+
scale (`float`, defaults to 1.0):
|
89 |
+
the weight scale of image prompt.
|
90 |
+
num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
|
91 |
+
The context length of the image features.
|
92 |
+
"""
|
93 |
+
|
94 |
+
def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4, skip=False):
|
95 |
+
super().__init__()
|
96 |
+
|
97 |
+
self.hidden_size = hidden_size
|
98 |
+
self.cross_attention_dim = cross_attention_dim
|
99 |
+
self.scale = scale
|
100 |
+
self.num_tokens = num_tokens
|
101 |
+
self.skip = skip
|
102 |
+
|
103 |
+
self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
104 |
+
self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
105 |
+
|
106 |
+
def __call__(
|
107 |
+
self,
|
108 |
+
attn,
|
109 |
+
hidden_states,
|
110 |
+
encoder_hidden_states=None,
|
111 |
+
attention_mask=None,
|
112 |
+
temb=None,
|
113 |
+
):
|
114 |
+
residual = hidden_states
|
115 |
+
|
116 |
+
if attn.spatial_norm is not None:
|
117 |
+
hidden_states = attn.spatial_norm(hidden_states, temb)
|
118 |
+
|
119 |
+
input_ndim = hidden_states.ndim
|
120 |
+
|
121 |
+
if input_ndim == 4:
|
122 |
+
batch_size, channel, height, width = hidden_states.shape
|
123 |
+
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
124 |
+
|
125 |
+
batch_size, sequence_length, _ = (
|
126 |
+
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
127 |
+
)
|
128 |
+
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
129 |
+
|
130 |
+
if attn.group_norm is not None:
|
131 |
+
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
132 |
+
|
133 |
+
query = attn.to_q(hidden_states)
|
134 |
+
|
135 |
+
if encoder_hidden_states is None:
|
136 |
+
encoder_hidden_states = hidden_states
|
137 |
+
else:
|
138 |
+
# get encoder_hidden_states, ip_hidden_states
|
139 |
+
end_pos = encoder_hidden_states.shape[1] - self.num_tokens
|
140 |
+
encoder_hidden_states, ip_hidden_states = (
|
141 |
+
encoder_hidden_states[:, :end_pos, :],
|
142 |
+
encoder_hidden_states[:, end_pos:, :],
|
143 |
+
)
|
144 |
+
if attn.norm_cross:
|
145 |
+
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
146 |
+
|
147 |
+
key = attn.to_k(encoder_hidden_states)
|
148 |
+
value = attn.to_v(encoder_hidden_states)
|
149 |
+
|
150 |
+
query = attn.head_to_batch_dim(query)
|
151 |
+
key = attn.head_to_batch_dim(key)
|
152 |
+
value = attn.head_to_batch_dim(value)
|
153 |
+
|
154 |
+
attention_probs = attn.get_attention_scores(query, key, attention_mask)
|
155 |
+
hidden_states = torch.bmm(attention_probs, value)
|
156 |
+
hidden_states = attn.batch_to_head_dim(hidden_states)
|
157 |
+
|
158 |
+
if not self.skip:
|
159 |
+
# for ip-adapter
|
160 |
+
ip_key = self.to_k_ip(ip_hidden_states)
|
161 |
+
ip_value = self.to_v_ip(ip_hidden_states)
|
162 |
+
|
163 |
+
ip_key = attn.head_to_batch_dim(ip_key)
|
164 |
+
ip_value = attn.head_to_batch_dim(ip_value)
|
165 |
+
|
166 |
+
ip_attention_probs = attn.get_attention_scores(query, ip_key, None)
|
167 |
+
self.attn_map = ip_attention_probs
|
168 |
+
ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
|
169 |
+
ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)
|
170 |
+
|
171 |
+
hidden_states = hidden_states + self.scale * ip_hidden_states
|
172 |
+
|
173 |
+
# linear proj
|
174 |
+
hidden_states = attn.to_out[0](hidden_states)
|
175 |
+
# dropout
|
176 |
+
hidden_states = attn.to_out[1](hidden_states)
|
177 |
+
|
178 |
+
if input_ndim == 4:
|
179 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
180 |
+
|
181 |
+
if attn.residual_connection:
|
182 |
+
hidden_states = hidden_states + residual
|
183 |
+
|
184 |
+
hidden_states = hidden_states / attn.rescale_output_factor
|
185 |
+
|
186 |
+
return hidden_states
|
187 |
+
|
188 |
+
|
189 |
+
class AttnProcessor2_0(torch.nn.Module):
|
190 |
+
r"""
|
191 |
+
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
|
192 |
+
"""
|
193 |
+
|
194 |
+
def __init__(
|
195 |
+
self,
|
196 |
+
hidden_size=None,
|
197 |
+
cross_attention_dim=None,
|
198 |
+
):
|
199 |
+
super().__init__()
|
200 |
+
if not hasattr(F, "scaled_dot_product_attention"):
|
201 |
+
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
|
202 |
+
|
203 |
+
def __call__(
|
204 |
+
self,
|
205 |
+
attn,
|
206 |
+
hidden_states,
|
207 |
+
encoder_hidden_states=None,
|
208 |
+
attention_mask=None,
|
209 |
+
temb=None,
|
210 |
+
):
|
211 |
+
residual = hidden_states
|
212 |
+
|
213 |
+
if attn.spatial_norm is not None:
|
214 |
+
hidden_states = attn.spatial_norm(hidden_states, temb)
|
215 |
+
|
216 |
+
input_ndim = hidden_states.ndim
|
217 |
+
|
218 |
+
if input_ndim == 4:
|
219 |
+
batch_size, channel, height, width = hidden_states.shape
|
220 |
+
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
221 |
+
|
222 |
+
batch_size, sequence_length, _ = (
|
223 |
+
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
224 |
+
)
|
225 |
+
|
226 |
+
if attention_mask is not None:
|
227 |
+
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
228 |
+
# scaled_dot_product_attention expects attention_mask shape to be
|
229 |
+
# (batch, heads, source_length, target_length)
|
230 |
+
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
|
231 |
+
|
232 |
+
if attn.group_norm is not None:
|
233 |
+
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
234 |
+
|
235 |
+
query = attn.to_q(hidden_states)
|
236 |
+
|
237 |
+
if encoder_hidden_states is None:
|
238 |
+
encoder_hidden_states = hidden_states
|
239 |
+
elif attn.norm_cross:
|
240 |
+
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
241 |
+
|
242 |
+
key = attn.to_k(encoder_hidden_states)
|
243 |
+
value = attn.to_v(encoder_hidden_states)
|
244 |
+
|
245 |
+
inner_dim = key.shape[-1]
|
246 |
+
head_dim = inner_dim // attn.heads
|
247 |
+
|
248 |
+
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
249 |
+
|
250 |
+
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
251 |
+
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
252 |
+
|
253 |
+
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
254 |
+
# TODO: add support for attn.scale when we move to Torch 2.1
|
255 |
+
hidden_states = F.scaled_dot_product_attention(
|
256 |
+
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
|
257 |
+
)
|
258 |
+
|
259 |
+
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
260 |
+
hidden_states = hidden_states.to(query.dtype)
|
261 |
+
|
262 |
+
# linear proj
|
263 |
+
hidden_states = attn.to_out[0](hidden_states)
|
264 |
+
# dropout
|
265 |
+
hidden_states = attn.to_out[1](hidden_states)
|
266 |
+
|
267 |
+
if input_ndim == 4:
|
268 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
269 |
+
|
270 |
+
if attn.residual_connection:
|
271 |
+
hidden_states = hidden_states + residual
|
272 |
+
|
273 |
+
hidden_states = hidden_states / attn.rescale_output_factor
|
274 |
+
|
275 |
+
return hidden_states
|
276 |
+
|
277 |
+
|
278 |
+
class IPAttnProcessor2_0(torch.nn.Module):
|
279 |
+
r"""
|
280 |
+
Attention processor for IP-Adapater for PyTorch 2.0.
|
281 |
+
Args:
|
282 |
+
hidden_size (`int`):
|
283 |
+
The hidden size of the attention layer.
|
284 |
+
cross_attention_dim (`int`):
|
285 |
+
The number of channels in the `encoder_hidden_states`.
|
286 |
+
scale (`float`, defaults to 1.0):
|
287 |
+
the weight scale of image prompt.
|
288 |
+
num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
|
289 |
+
The context length of the image features.
|
290 |
+
"""
|
291 |
+
|
292 |
+
def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4, skip=False):
|
293 |
+
super().__init__()
|
294 |
+
|
295 |
+
if not hasattr(F, "scaled_dot_product_attention"):
|
296 |
+
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
|
297 |
+
|
298 |
+
self.hidden_size = hidden_size
|
299 |
+
self.cross_attention_dim = cross_attention_dim
|
300 |
+
self.scale = scale
|
301 |
+
self.num_tokens = num_tokens
|
302 |
+
self.skip = skip
|
303 |
+
|
304 |
+
self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
305 |
+
self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
306 |
+
|
307 |
+
def __call__(
|
308 |
+
self,
|
309 |
+
attn,
|
310 |
+
hidden_states,
|
311 |
+
encoder_hidden_states=None,
|
312 |
+
attention_mask=None,
|
313 |
+
temb=None,
|
314 |
+
):
|
315 |
+
residual = hidden_states
|
316 |
+
|
317 |
+
if attn.spatial_norm is not None:
|
318 |
+
hidden_states = attn.spatial_norm(hidden_states, temb)
|
319 |
+
|
320 |
+
input_ndim = hidden_states.ndim
|
321 |
+
|
322 |
+
if input_ndim == 4:
|
323 |
+
batch_size, channel, height, width = hidden_states.shape
|
324 |
+
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
325 |
+
|
326 |
+
batch_size, sequence_length, _ = (
|
327 |
+
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
328 |
+
)
|
329 |
+
|
330 |
+
if attention_mask is not None:
|
331 |
+
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
332 |
+
# scaled_dot_product_attention expects attention_mask shape to be
|
333 |
+
# (batch, heads, source_length, target_length)
|
334 |
+
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
|
335 |
+
|
336 |
+
if attn.group_norm is not None:
|
337 |
+
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
338 |
+
|
339 |
+
query = attn.to_q(hidden_states)
|
340 |
+
|
341 |
+
if encoder_hidden_states is None:
|
342 |
+
encoder_hidden_states = hidden_states
|
343 |
+
else:
|
344 |
+
# get encoder_hidden_states, ip_hidden_states
|
345 |
+
end_pos = encoder_hidden_states.shape[1] - self.num_tokens
|
346 |
+
encoder_hidden_states, ip_hidden_states = (
|
347 |
+
encoder_hidden_states[:, :end_pos, :],
|
348 |
+
encoder_hidden_states[:, end_pos:, :],
|
349 |
+
)
|
350 |
+
if attn.norm_cross:
|
351 |
+
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
352 |
+
|
353 |
+
key = attn.to_k(encoder_hidden_states)
|
354 |
+
value = attn.to_v(encoder_hidden_states)
|
355 |
+
|
356 |
+
inner_dim = key.shape[-1]
|
357 |
+
head_dim = inner_dim // attn.heads
|
358 |
+
|
359 |
+
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
360 |
+
|
361 |
+
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
362 |
+
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
363 |
+
|
364 |
+
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
365 |
+
# TODO: add support for attn.scale when we move to Torch 2.1
|
366 |
+
hidden_states = F.scaled_dot_product_attention(
|
367 |
+
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
|
368 |
+
)
|
369 |
+
|
370 |
+
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
371 |
+
hidden_states = hidden_states.to(query.dtype)
|
372 |
+
|
373 |
+
if not self.skip:
|
374 |
+
# for ip-adapter
|
375 |
+
ip_key = self.to_k_ip(ip_hidden_states)
|
376 |
+
ip_value = self.to_v_ip(ip_hidden_states)
|
377 |
+
|
378 |
+
ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
379 |
+
ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
380 |
+
|
381 |
+
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
382 |
+
# TODO: add support for attn.scale when we move to Torch 2.1
|
383 |
+
ip_hidden_states = F.scaled_dot_product_attention(
|
384 |
+
query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
|
385 |
+
)
|
386 |
+
with torch.no_grad():
|
387 |
+
self.attn_map = query @ ip_key.transpose(-2, -1).softmax(dim=-1)
|
388 |
+
#print(self.attn_map.shape)
|
389 |
+
|
390 |
+
ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
391 |
+
ip_hidden_states = ip_hidden_states.to(query.dtype)
|
392 |
+
|
393 |
+
hidden_states = hidden_states + self.scale * ip_hidden_states
|
394 |
+
|
395 |
+
# linear proj
|
396 |
+
hidden_states = attn.to_out[0](hidden_states)
|
397 |
+
# dropout
|
398 |
+
hidden_states = attn.to_out[1](hidden_states)
|
399 |
+
|
400 |
+
if input_ndim == 4:
|
401 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
402 |
+
|
403 |
+
if attn.residual_connection:
|
404 |
+
hidden_states = hidden_states + residual
|
405 |
+
|
406 |
+
hidden_states = hidden_states / attn.rescale_output_factor
|
407 |
+
|
408 |
+
return hidden_states
|
409 |
+
|
410 |
+
|
411 |
+
## for controlnet
|
412 |
+
class CNAttnProcessor:
|
413 |
+
r"""
|
414 |
+
Default processor for performing attention-related computations.
|
415 |
+
"""
|
416 |
+
|
417 |
+
def __init__(self, num_tokens=4):
|
418 |
+
self.num_tokens = num_tokens
|
419 |
+
|
420 |
+
def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None, temb=None):
|
421 |
+
residual = hidden_states
|
422 |
+
|
423 |
+
if attn.spatial_norm is not None:
|
424 |
+
hidden_states = attn.spatial_norm(hidden_states, temb)
|
425 |
+
|
426 |
+
input_ndim = hidden_states.ndim
|
427 |
+
|
428 |
+
if input_ndim == 4:
|
429 |
+
batch_size, channel, height, width = hidden_states.shape
|
430 |
+
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
431 |
+
|
432 |
+
batch_size, sequence_length, _ = (
|
433 |
+
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
434 |
+
)
|
435 |
+
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
436 |
+
|
437 |
+
if attn.group_norm is not None:
|
438 |
+
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
439 |
+
|
440 |
+
query = attn.to_q(hidden_states)
|
441 |
+
|
442 |
+
if encoder_hidden_states is None:
|
443 |
+
encoder_hidden_states = hidden_states
|
444 |
+
else:
|
445 |
+
end_pos = encoder_hidden_states.shape[1] - self.num_tokens
|
446 |
+
encoder_hidden_states = encoder_hidden_states[:, :end_pos] # only use text
|
447 |
+
if attn.norm_cross:
|
448 |
+
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
449 |
+
|
450 |
+
key = attn.to_k(encoder_hidden_states)
|
451 |
+
value = attn.to_v(encoder_hidden_states)
|
452 |
+
|
453 |
+
query = attn.head_to_batch_dim(query)
|
454 |
+
key = attn.head_to_batch_dim(key)
|
455 |
+
value = attn.head_to_batch_dim(value)
|
456 |
+
|
457 |
+
attention_probs = attn.get_attention_scores(query, key, attention_mask)
|
458 |
+
hidden_states = torch.bmm(attention_probs, value)
|
459 |
+
hidden_states = attn.batch_to_head_dim(hidden_states)
|
460 |
+
|
461 |
+
# linear proj
|
462 |
+
hidden_states = attn.to_out[0](hidden_states)
|
463 |
+
# dropout
|
464 |
+
hidden_states = attn.to_out[1](hidden_states)
|
465 |
+
|
466 |
+
if input_ndim == 4:
|
467 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
468 |
+
|
469 |
+
if attn.residual_connection:
|
470 |
+
hidden_states = hidden_states + residual
|
471 |
+
|
472 |
+
hidden_states = hidden_states / attn.rescale_output_factor
|
473 |
+
|
474 |
+
return hidden_states
|
475 |
+
|
476 |
+
|
477 |
+
class CNAttnProcessor2_0:
|
478 |
+
r"""
|
479 |
+
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
|
480 |
+
"""
|
481 |
+
|
482 |
+
def __init__(self, num_tokens=4):
|
483 |
+
if not hasattr(F, "scaled_dot_product_attention"):
|
484 |
+
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
|
485 |
+
self.num_tokens = num_tokens
|
486 |
+
|
487 |
+
def __call__(
|
488 |
+
self,
|
489 |
+
attn,
|
490 |
+
hidden_states,
|
491 |
+
encoder_hidden_states=None,
|
492 |
+
attention_mask=None,
|
493 |
+
temb=None,
|
494 |
+
):
|
495 |
+
residual = hidden_states
|
496 |
+
|
497 |
+
if attn.spatial_norm is not None:
|
498 |
+
hidden_states = attn.spatial_norm(hidden_states, temb)
|
499 |
+
|
500 |
+
input_ndim = hidden_states.ndim
|
501 |
+
|
502 |
+
if input_ndim == 4:
|
503 |
+
batch_size, channel, height, width = hidden_states.shape
|
504 |
+
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
505 |
+
|
506 |
+
batch_size, sequence_length, _ = (
|
507 |
+
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
508 |
+
)
|
509 |
+
|
510 |
+
if attention_mask is not None:
|
511 |
+
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
512 |
+
# scaled_dot_product_attention expects attention_mask shape to be
|
513 |
+
# (batch, heads, source_length, target_length)
|
514 |
+
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
|
515 |
+
|
516 |
+
if attn.group_norm is not None:
|
517 |
+
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
518 |
+
|
519 |
+
query = attn.to_q(hidden_states)
|
520 |
+
|
521 |
+
if encoder_hidden_states is None:
|
522 |
+
encoder_hidden_states = hidden_states
|
523 |
+
else:
|
524 |
+
end_pos = encoder_hidden_states.shape[1] - self.num_tokens
|
525 |
+
encoder_hidden_states = encoder_hidden_states[:, :end_pos] # only use text
|
526 |
+
if attn.norm_cross:
|
527 |
+
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
528 |
+
|
529 |
+
key = attn.to_k(encoder_hidden_states)
|
530 |
+
value = attn.to_v(encoder_hidden_states)
|
531 |
+
|
532 |
+
inner_dim = key.shape[-1]
|
533 |
+
head_dim = inner_dim // attn.heads
|
534 |
+
|
535 |
+
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
536 |
+
|
537 |
+
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
538 |
+
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
539 |
+
|
540 |
+
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
541 |
+
# TODO: add support for attn.scale when we move to Torch 2.1
|
542 |
+
hidden_states = F.scaled_dot_product_attention(
|
543 |
+
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
|
544 |
+
)
|
545 |
+
|
546 |
+
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
547 |
+
hidden_states = hidden_states.to(query.dtype)
|
548 |
+
|
549 |
+
# linear proj
|
550 |
+
hidden_states = attn.to_out[0](hidden_states)
|
551 |
+
# dropout
|
552 |
+
hidden_states = attn.to_out[1](hidden_states)
|
553 |
+
|
554 |
+
if input_ndim == 4:
|
555 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
556 |
+
|
557 |
+
if attn.residual_connection:
|
558 |
+
hidden_states = hidden_states + residual
|
559 |
+
|
560 |
+
hidden_states = hidden_states / attn.rescale_output_factor
|
561 |
+
|
562 |
+
return hidden_states
|
ip_adapter/ip_adapter.py
ADDED
@@ -0,0 +1,461 @@
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|
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|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
from typing import List
|
3 |
+
|
4 |
+
import torch
|
5 |
+
from diffusers import StableDiffusionPipeline
|
6 |
+
from diffusers.pipelines.controlnet import MultiControlNetModel
|
7 |
+
from PIL import Image
|
8 |
+
from safetensors import safe_open
|
9 |
+
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
|
10 |
+
|
11 |
+
from .utils import is_torch2_available, get_generator
|
12 |
+
|
13 |
+
if is_torch2_available():
|
14 |
+
from .attention_processor import (
|
15 |
+
AttnProcessor2_0 as AttnProcessor,
|
16 |
+
)
|
17 |
+
from .attention_processor import (
|
18 |
+
CNAttnProcessor2_0 as CNAttnProcessor,
|
19 |
+
)
|
20 |
+
from .attention_processor import (
|
21 |
+
IPAttnProcessor2_0 as IPAttnProcessor,
|
22 |
+
)
|
23 |
+
else:
|
24 |
+
from .attention_processor import AttnProcessor, CNAttnProcessor, IPAttnProcessor
|
25 |
+
from .resampler import Resampler
|
26 |
+
|
27 |
+
|
28 |
+
class ImageProjModel(torch.nn.Module):
|
29 |
+
"""Projection Model"""
|
30 |
+
|
31 |
+
def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
|
32 |
+
super().__init__()
|
33 |
+
|
34 |
+
self.generator = None
|
35 |
+
self.cross_attention_dim = cross_attention_dim
|
36 |
+
self.clip_extra_context_tokens = clip_extra_context_tokens
|
37 |
+
self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
|
38 |
+
self.norm = torch.nn.LayerNorm(cross_attention_dim)
|
39 |
+
|
40 |
+
def forward(self, image_embeds):
|
41 |
+
embeds = image_embeds
|
42 |
+
clip_extra_context_tokens = self.proj(embeds).reshape(
|
43 |
+
-1, self.clip_extra_context_tokens, self.cross_attention_dim
|
44 |
+
)
|
45 |
+
clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
|
46 |
+
return clip_extra_context_tokens
|
47 |
+
|
48 |
+
|
49 |
+
class MLPProjModel(torch.nn.Module):
|
50 |
+
"""SD model with image prompt"""
|
51 |
+
def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024):
|
52 |
+
super().__init__()
|
53 |
+
|
54 |
+
self.proj = torch.nn.Sequential(
|
55 |
+
torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
|
56 |
+
torch.nn.GELU(),
|
57 |
+
torch.nn.Linear(clip_embeddings_dim, cross_attention_dim),
|
58 |
+
torch.nn.LayerNorm(cross_attention_dim)
|
59 |
+
)
|
60 |
+
|
61 |
+
def forward(self, image_embeds):
|
62 |
+
clip_extra_context_tokens = self.proj(image_embeds)
|
63 |
+
return clip_extra_context_tokens
|
64 |
+
|
65 |
+
|
66 |
+
class IPAdapter:
|
67 |
+
def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_tokens=4, target_blocks=["block"]):
|
68 |
+
self.device = device
|
69 |
+
self.image_encoder_path = image_encoder_path
|
70 |
+
self.ip_ckpt = ip_ckpt
|
71 |
+
self.num_tokens = num_tokens
|
72 |
+
self.target_blocks = target_blocks
|
73 |
+
|
74 |
+
self.pipe = sd_pipe.to(self.device)
|
75 |
+
self.set_ip_adapter()
|
76 |
+
|
77 |
+
# load image encoder
|
78 |
+
self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
|
79 |
+
self.device, dtype=torch.float16
|
80 |
+
)
|
81 |
+
self.clip_image_processor = CLIPImageProcessor()
|
82 |
+
# image proj model
|
83 |
+
self.image_proj_model = self.init_proj()
|
84 |
+
|
85 |
+
self.load_ip_adapter()
|
86 |
+
|
87 |
+
|
88 |
+
def init_proj(self):
|
89 |
+
image_proj_model = ImageProjModel(
|
90 |
+
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
91 |
+
clip_embeddings_dim=self.image_encoder.config.projection_dim,
|
92 |
+
clip_extra_context_tokens=self.num_tokens,
|
93 |
+
).to(self.device, dtype=torch.float16)
|
94 |
+
return image_proj_model
|
95 |
+
|
96 |
+
def set_ip_adapter(self):
|
97 |
+
unet = self.pipe.unet
|
98 |
+
attn_procs = {}
|
99 |
+
for name in unet.attn_processors.keys():
|
100 |
+
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
|
101 |
+
if name.startswith("mid_block"):
|
102 |
+
hidden_size = unet.config.block_out_channels[-1]
|
103 |
+
elif name.startswith("up_blocks"):
|
104 |
+
block_id = int(name[len("up_blocks.")])
|
105 |
+
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
|
106 |
+
elif name.startswith("down_blocks"):
|
107 |
+
block_id = int(name[len("down_blocks.")])
|
108 |
+
hidden_size = unet.config.block_out_channels[block_id]
|
109 |
+
if cross_attention_dim is None:
|
110 |
+
attn_procs[name] = AttnProcessor()
|
111 |
+
else:
|
112 |
+
selected = False
|
113 |
+
for block_name in self.target_blocks:
|
114 |
+
if block_name in name:
|
115 |
+
selected = True
|
116 |
+
break
|
117 |
+
if selected:
|
118 |
+
attn_procs[name] = IPAttnProcessor(
|
119 |
+
hidden_size=hidden_size,
|
120 |
+
cross_attention_dim=cross_attention_dim,
|
121 |
+
scale=1.0,
|
122 |
+
num_tokens=self.num_tokens,
|
123 |
+
).to(self.device, dtype=torch.float16)
|
124 |
+
else:
|
125 |
+
attn_procs[name] = IPAttnProcessor(
|
126 |
+
hidden_size=hidden_size,
|
127 |
+
cross_attention_dim=cross_attention_dim,
|
128 |
+
scale=1.0,
|
129 |
+
num_tokens=self.num_tokens,
|
130 |
+
skip=True
|
131 |
+
).to(self.device, dtype=torch.float16)
|
132 |
+
unet.set_attn_processor(attn_procs)
|
133 |
+
if hasattr(self.pipe, "controlnet"):
|
134 |
+
if isinstance(self.pipe.controlnet, MultiControlNetModel):
|
135 |
+
for controlnet in self.pipe.controlnet.nets:
|
136 |
+
controlnet.set_attn_processor(CNAttnProcessor(num_tokens=self.num_tokens))
|
137 |
+
else:
|
138 |
+
self.pipe.controlnet.set_attn_processor(CNAttnProcessor(num_tokens=self.num_tokens))
|
139 |
+
|
140 |
+
def load_ip_adapter(self):
|
141 |
+
if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors":
|
142 |
+
state_dict = {"image_proj": {}, "ip_adapter": {}}
|
143 |
+
with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f:
|
144 |
+
for key in f.keys():
|
145 |
+
if key.startswith("image_proj."):
|
146 |
+
state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
|
147 |
+
elif key.startswith("ip_adapter."):
|
148 |
+
state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
|
149 |
+
else:
|
150 |
+
state_dict = torch.load(self.ip_ckpt, map_location="cpu")
|
151 |
+
self.image_proj_model.load_state_dict(state_dict["image_proj"])
|
152 |
+
ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
|
153 |
+
ip_layers.load_state_dict(state_dict["ip_adapter"], strict=False)
|
154 |
+
|
155 |
+
@torch.inference_mode()
|
156 |
+
def get_image_embeds(self, pil_image=None, clip_image_embeds=None, content_prompt_embeds=None):
|
157 |
+
if pil_image is not None:
|
158 |
+
if isinstance(pil_image, Image.Image):
|
159 |
+
pil_image = [pil_image]
|
160 |
+
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
161 |
+
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
162 |
+
else:
|
163 |
+
clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
|
164 |
+
|
165 |
+
if content_prompt_embeds is not None:
|
166 |
+
clip_image_embeds = clip_image_embeds - content_prompt_embeds
|
167 |
+
|
168 |
+
image_prompt_embeds = self.image_proj_model(clip_image_embeds)
|
169 |
+
uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(clip_image_embeds))
|
170 |
+
return image_prompt_embeds, uncond_image_prompt_embeds
|
171 |
+
|
172 |
+
def set_scale(self, scale):
|
173 |
+
for attn_processor in self.pipe.unet.attn_processors.values():
|
174 |
+
if isinstance(attn_processor, IPAttnProcessor):
|
175 |
+
attn_processor.scale = scale
|
176 |
+
|
177 |
+
def generate(
|
178 |
+
self,
|
179 |
+
pil_image=None,
|
180 |
+
clip_image_embeds=None,
|
181 |
+
prompt=None,
|
182 |
+
negative_prompt=None,
|
183 |
+
scale=1.0,
|
184 |
+
num_samples=4,
|
185 |
+
seed=None,
|
186 |
+
guidance_scale=7.5,
|
187 |
+
num_inference_steps=30,
|
188 |
+
neg_content_emb=None,
|
189 |
+
**kwargs,
|
190 |
+
):
|
191 |
+
self.set_scale(scale)
|
192 |
+
|
193 |
+
if pil_image is not None:
|
194 |
+
num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
|
195 |
+
else:
|
196 |
+
num_prompts = clip_image_embeds.size(0)
|
197 |
+
|
198 |
+
if prompt is None:
|
199 |
+
prompt = "best quality, high quality"
|
200 |
+
if negative_prompt is None:
|
201 |
+
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
|
202 |
+
|
203 |
+
if not isinstance(prompt, List):
|
204 |
+
prompt = [prompt] * num_prompts
|
205 |
+
if not isinstance(negative_prompt, List):
|
206 |
+
negative_prompt = [negative_prompt] * num_prompts
|
207 |
+
|
208 |
+
image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(
|
209 |
+
pil_image=pil_image, clip_image_embeds=clip_image_embeds, content_prompt_embeds=neg_content_emb
|
210 |
+
)
|
211 |
+
bs_embed, seq_len, _ = image_prompt_embeds.shape
|
212 |
+
image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
|
213 |
+
image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
214 |
+
uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
|
215 |
+
uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
216 |
+
|
217 |
+
with torch.inference_mode():
|
218 |
+
prompt_embeds_, negative_prompt_embeds_ = self.pipe.encode_prompt(
|
219 |
+
prompt,
|
220 |
+
device=self.device,
|
221 |
+
num_images_per_prompt=num_samples,
|
222 |
+
do_classifier_free_guidance=True,
|
223 |
+
negative_prompt=negative_prompt,
|
224 |
+
)
|
225 |
+
prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
|
226 |
+
negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)
|
227 |
+
|
228 |
+
generator = get_generator(seed, self.device)
|
229 |
+
|
230 |
+
images = self.pipe(
|
231 |
+
prompt_embeds=prompt_embeds,
|
232 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
233 |
+
guidance_scale=guidance_scale,
|
234 |
+
num_inference_steps=num_inference_steps,
|
235 |
+
generator=generator,
|
236 |
+
**kwargs,
|
237 |
+
).images
|
238 |
+
|
239 |
+
return images
|
240 |
+
|
241 |
+
|
242 |
+
class IPAdapterXL(IPAdapter):
|
243 |
+
"""SDXL"""
|
244 |
+
|
245 |
+
def generate(
|
246 |
+
self,
|
247 |
+
pil_image,
|
248 |
+
prompt=None,
|
249 |
+
negative_prompt=None,
|
250 |
+
scale=1.0,
|
251 |
+
num_samples=4,
|
252 |
+
seed=None,
|
253 |
+
num_inference_steps=30,
|
254 |
+
neg_content_emb=None,
|
255 |
+
neg_content_prompt=None,
|
256 |
+
neg_content_scale=1.0,
|
257 |
+
**kwargs,
|
258 |
+
):
|
259 |
+
self.set_scale(scale)
|
260 |
+
|
261 |
+
num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
|
262 |
+
|
263 |
+
if prompt is None:
|
264 |
+
prompt = "best quality, high quality"
|
265 |
+
if negative_prompt is None:
|
266 |
+
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
|
267 |
+
|
268 |
+
if not isinstance(prompt, List):
|
269 |
+
prompt = [prompt] * num_prompts
|
270 |
+
if not isinstance(negative_prompt, List):
|
271 |
+
negative_prompt = [negative_prompt] * num_prompts
|
272 |
+
|
273 |
+
if neg_content_emb is None:
|
274 |
+
if neg_content_prompt is not None:
|
275 |
+
with torch.inference_mode():
|
276 |
+
(
|
277 |
+
prompt_embeds_, # torch.Size([1, 77, 2048])
|
278 |
+
negative_prompt_embeds_,
|
279 |
+
pooled_prompt_embeds_, # torch.Size([1, 1280])
|
280 |
+
negative_pooled_prompt_embeds_,
|
281 |
+
) = self.pipe.encode_prompt(
|
282 |
+
neg_content_prompt,
|
283 |
+
num_images_per_prompt=num_samples,
|
284 |
+
do_classifier_free_guidance=True,
|
285 |
+
negative_prompt=negative_prompt,
|
286 |
+
)
|
287 |
+
pooled_prompt_embeds_ *= neg_content_scale
|
288 |
+
else:
|
289 |
+
pooled_prompt_embeds_ = neg_content_emb
|
290 |
+
else:
|
291 |
+
pooled_prompt_embeds_ = None
|
292 |
+
|
293 |
+
image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(pil_image, content_prompt_embeds=pooled_prompt_embeds_)
|
294 |
+
bs_embed, seq_len, _ = image_prompt_embeds.shape
|
295 |
+
image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
|
296 |
+
image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
297 |
+
uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
|
298 |
+
uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
299 |
+
|
300 |
+
with torch.inference_mode():
|
301 |
+
(
|
302 |
+
prompt_embeds,
|
303 |
+
negative_prompt_embeds,
|
304 |
+
pooled_prompt_embeds,
|
305 |
+
negative_pooled_prompt_embeds,
|
306 |
+
) = self.pipe.encode_prompt(
|
307 |
+
prompt,
|
308 |
+
num_images_per_prompt=num_samples,
|
309 |
+
do_classifier_free_guidance=True,
|
310 |
+
negative_prompt=negative_prompt,
|
311 |
+
)
|
312 |
+
prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
|
313 |
+
negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_image_prompt_embeds], dim=1)
|
314 |
+
|
315 |
+
self.generator = get_generator(seed, self.device)
|
316 |
+
|
317 |
+
images = self.pipe(
|
318 |
+
prompt_embeds=prompt_embeds,
|
319 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
320 |
+
pooled_prompt_embeds=pooled_prompt_embeds,
|
321 |
+
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
322 |
+
num_inference_steps=num_inference_steps,
|
323 |
+
generator=self.generator,
|
324 |
+
**kwargs,
|
325 |
+
).images
|
326 |
+
|
327 |
+
return images
|
328 |
+
|
329 |
+
|
330 |
+
class IPAdapterPlus(IPAdapter):
|
331 |
+
"""IP-Adapter with fine-grained features"""
|
332 |
+
|
333 |
+
def init_proj(self):
|
334 |
+
image_proj_model = Resampler(
|
335 |
+
dim=self.pipe.unet.config.cross_attention_dim,
|
336 |
+
depth=4,
|
337 |
+
dim_head=64,
|
338 |
+
heads=12,
|
339 |
+
num_queries=self.num_tokens,
|
340 |
+
embedding_dim=self.image_encoder.config.hidden_size,
|
341 |
+
output_dim=self.pipe.unet.config.cross_attention_dim,
|
342 |
+
ff_mult=4,
|
343 |
+
).to(self.device, dtype=torch.float16)
|
344 |
+
return image_proj_model
|
345 |
+
|
346 |
+
@torch.inference_mode()
|
347 |
+
def get_image_embeds(self, pil_image=None, clip_image_embeds=None):
|
348 |
+
if isinstance(pil_image, Image.Image):
|
349 |
+
pil_image = [pil_image]
|
350 |
+
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
351 |
+
clip_image = clip_image.to(self.device, dtype=torch.float16)
|
352 |
+
clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
|
353 |
+
image_prompt_embeds = self.image_proj_model(clip_image_embeds)
|
354 |
+
uncond_clip_image_embeds = self.image_encoder(
|
355 |
+
torch.zeros_like(clip_image), output_hidden_states=True
|
356 |
+
).hidden_states[-2]
|
357 |
+
uncond_image_prompt_embeds = self.image_proj_model(uncond_clip_image_embeds)
|
358 |
+
return image_prompt_embeds, uncond_image_prompt_embeds
|
359 |
+
|
360 |
+
|
361 |
+
class IPAdapterFull(IPAdapterPlus):
|
362 |
+
"""IP-Adapter with full features"""
|
363 |
+
|
364 |
+
def init_proj(self):
|
365 |
+
image_proj_model = MLPProjModel(
|
366 |
+
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
367 |
+
clip_embeddings_dim=self.image_encoder.config.hidden_size,
|
368 |
+
).to(self.device, dtype=torch.float16)
|
369 |
+
return image_proj_model
|
370 |
+
|
371 |
+
|
372 |
+
class IPAdapterPlusXL(IPAdapter):
|
373 |
+
"""SDXL"""
|
374 |
+
|
375 |
+
def init_proj(self):
|
376 |
+
image_proj_model = Resampler(
|
377 |
+
dim=1280,
|
378 |
+
depth=4,
|
379 |
+
dim_head=64,
|
380 |
+
heads=20,
|
381 |
+
num_queries=self.num_tokens,
|
382 |
+
embedding_dim=self.image_encoder.config.hidden_size,
|
383 |
+
output_dim=self.pipe.unet.config.cross_attention_dim,
|
384 |
+
ff_mult=4,
|
385 |
+
).to(self.device, dtype=torch.float16)
|
386 |
+
return image_proj_model
|
387 |
+
|
388 |
+
@torch.inference_mode()
|
389 |
+
def get_image_embeds(self, pil_image):
|
390 |
+
if isinstance(pil_image, Image.Image):
|
391 |
+
pil_image = [pil_image]
|
392 |
+
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
393 |
+
clip_image = clip_image.to(self.device, dtype=torch.float16)
|
394 |
+
clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
|
395 |
+
image_prompt_embeds = self.image_proj_model(clip_image_embeds)
|
396 |
+
uncond_clip_image_embeds = self.image_encoder(
|
397 |
+
torch.zeros_like(clip_image), output_hidden_states=True
|
398 |
+
).hidden_states[-2]
|
399 |
+
uncond_image_prompt_embeds = self.image_proj_model(uncond_clip_image_embeds)
|
400 |
+
return image_prompt_embeds, uncond_image_prompt_embeds
|
401 |
+
|
402 |
+
def generate(
|
403 |
+
self,
|
404 |
+
pil_image,
|
405 |
+
prompt=None,
|
406 |
+
negative_prompt=None,
|
407 |
+
scale=1.0,
|
408 |
+
num_samples=4,
|
409 |
+
seed=None,
|
410 |
+
num_inference_steps=30,
|
411 |
+
**kwargs,
|
412 |
+
):
|
413 |
+
self.set_scale(scale)
|
414 |
+
|
415 |
+
num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
|
416 |
+
|
417 |
+
if prompt is None:
|
418 |
+
prompt = "best quality, high quality"
|
419 |
+
if negative_prompt is None:
|
420 |
+
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
|
421 |
+
|
422 |
+
if not isinstance(prompt, List):
|
423 |
+
prompt = [prompt] * num_prompts
|
424 |
+
if not isinstance(negative_prompt, List):
|
425 |
+
negative_prompt = [negative_prompt] * num_prompts
|
426 |
+
|
427 |
+
image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(pil_image)
|
428 |
+
bs_embed, seq_len, _ = image_prompt_embeds.shape
|
429 |
+
image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
|
430 |
+
image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
431 |
+
uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
|
432 |
+
uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
433 |
+
|
434 |
+
with torch.inference_mode():
|
435 |
+
(
|
436 |
+
prompt_embeds,
|
437 |
+
negative_prompt_embeds,
|
438 |
+
pooled_prompt_embeds,
|
439 |
+
negative_pooled_prompt_embeds,
|
440 |
+
) = self.pipe.encode_prompt(
|
441 |
+
prompt,
|
442 |
+
num_images_per_prompt=num_samples,
|
443 |
+
do_classifier_free_guidance=True,
|
444 |
+
negative_prompt=negative_prompt,
|
445 |
+
)
|
446 |
+
prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
|
447 |
+
negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_image_prompt_embeds], dim=1)
|
448 |
+
|
449 |
+
generator = get_generator(seed, self.device)
|
450 |
+
|
451 |
+
images = self.pipe(
|
452 |
+
prompt_embeds=prompt_embeds,
|
453 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
454 |
+
pooled_prompt_embeds=pooled_prompt_embeds,
|
455 |
+
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
456 |
+
num_inference_steps=num_inference_steps,
|
457 |
+
generator=generator,
|
458 |
+
**kwargs,
|
459 |
+
).images
|
460 |
+
|
461 |
+
return images
|
ip_adapter/resampler.py
ADDED
@@ -0,0 +1,158 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
|
2 |
+
# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py
|
3 |
+
|
4 |
+
import math
|
5 |
+
|
6 |
+
import torch
|
7 |
+
import torch.nn as nn
|
8 |
+
from einops import rearrange
|
9 |
+
from einops.layers.torch import Rearrange
|
10 |
+
|
11 |
+
|
12 |
+
# FFN
|
13 |
+
def FeedForward(dim, mult=4):
|
14 |
+
inner_dim = int(dim * mult)
|
15 |
+
return nn.Sequential(
|
16 |
+
nn.LayerNorm(dim),
|
17 |
+
nn.Linear(dim, inner_dim, bias=False),
|
18 |
+
nn.GELU(),
|
19 |
+
nn.Linear(inner_dim, dim, bias=False),
|
20 |
+
)
|
21 |
+
|
22 |
+
|
23 |
+
def reshape_tensor(x, heads):
|
24 |
+
bs, length, width = x.shape
|
25 |
+
# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
|
26 |
+
x = x.view(bs, length, heads, -1)
|
27 |
+
# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
|
28 |
+
x = x.transpose(1, 2)
|
29 |
+
# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
|
30 |
+
x = x.reshape(bs, heads, length, -1)
|
31 |
+
return x
|
32 |
+
|
33 |
+
|
34 |
+
class PerceiverAttention(nn.Module):
|
35 |
+
def __init__(self, *, dim, dim_head=64, heads=8):
|
36 |
+
super().__init__()
|
37 |
+
self.scale = dim_head**-0.5
|
38 |
+
self.dim_head = dim_head
|
39 |
+
self.heads = heads
|
40 |
+
inner_dim = dim_head * heads
|
41 |
+
|
42 |
+
self.norm1 = nn.LayerNorm(dim)
|
43 |
+
self.norm2 = nn.LayerNorm(dim)
|
44 |
+
|
45 |
+
self.to_q = nn.Linear(dim, inner_dim, bias=False)
|
46 |
+
self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
|
47 |
+
self.to_out = nn.Linear(inner_dim, dim, bias=False)
|
48 |
+
|
49 |
+
def forward(self, x, latents):
|
50 |
+
"""
|
51 |
+
Args:
|
52 |
+
x (torch.Tensor): image features
|
53 |
+
shape (b, n1, D)
|
54 |
+
latent (torch.Tensor): latent features
|
55 |
+
shape (b, n2, D)
|
56 |
+
"""
|
57 |
+
x = self.norm1(x)
|
58 |
+
latents = self.norm2(latents)
|
59 |
+
|
60 |
+
b, l, _ = latents.shape
|
61 |
+
|
62 |
+
q = self.to_q(latents)
|
63 |
+
kv_input = torch.cat((x, latents), dim=-2)
|
64 |
+
k, v = self.to_kv(kv_input).chunk(2, dim=-1)
|
65 |
+
|
66 |
+
q = reshape_tensor(q, self.heads)
|
67 |
+
k = reshape_tensor(k, self.heads)
|
68 |
+
v = reshape_tensor(v, self.heads)
|
69 |
+
|
70 |
+
# attention
|
71 |
+
scale = 1 / math.sqrt(math.sqrt(self.dim_head))
|
72 |
+
weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards
|
73 |
+
weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
|
74 |
+
out = weight @ v
|
75 |
+
|
76 |
+
out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
|
77 |
+
|
78 |
+
return self.to_out(out)
|
79 |
+
|
80 |
+
|
81 |
+
class Resampler(nn.Module):
|
82 |
+
def __init__(
|
83 |
+
self,
|
84 |
+
dim=1024,
|
85 |
+
depth=8,
|
86 |
+
dim_head=64,
|
87 |
+
heads=16,
|
88 |
+
num_queries=8,
|
89 |
+
embedding_dim=768,
|
90 |
+
output_dim=1024,
|
91 |
+
ff_mult=4,
|
92 |
+
max_seq_len: int = 257, # CLIP tokens + CLS token
|
93 |
+
apply_pos_emb: bool = False,
|
94 |
+
num_latents_mean_pooled: int = 0, # number of latents derived from mean pooled representation of the sequence
|
95 |
+
):
|
96 |
+
super().__init__()
|
97 |
+
self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None
|
98 |
+
|
99 |
+
self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
|
100 |
+
|
101 |
+
self.proj_in = nn.Linear(embedding_dim, dim)
|
102 |
+
|
103 |
+
self.proj_out = nn.Linear(dim, output_dim)
|
104 |
+
self.norm_out = nn.LayerNorm(output_dim)
|
105 |
+
|
106 |
+
self.to_latents_from_mean_pooled_seq = (
|
107 |
+
nn.Sequential(
|
108 |
+
nn.LayerNorm(dim),
|
109 |
+
nn.Linear(dim, dim * num_latents_mean_pooled),
|
110 |
+
Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled),
|
111 |
+
)
|
112 |
+
if num_latents_mean_pooled > 0
|
113 |
+
else None
|
114 |
+
)
|
115 |
+
|
116 |
+
self.layers = nn.ModuleList([])
|
117 |
+
for _ in range(depth):
|
118 |
+
self.layers.append(
|
119 |
+
nn.ModuleList(
|
120 |
+
[
|
121 |
+
PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
|
122 |
+
FeedForward(dim=dim, mult=ff_mult),
|
123 |
+
]
|
124 |
+
)
|
125 |
+
)
|
126 |
+
|
127 |
+
def forward(self, x):
|
128 |
+
if self.pos_emb is not None:
|
129 |
+
n, device = x.shape[1], x.device
|
130 |
+
pos_emb = self.pos_emb(torch.arange(n, device=device))
|
131 |
+
x = x + pos_emb
|
132 |
+
|
133 |
+
latents = self.latents.repeat(x.size(0), 1, 1)
|
134 |
+
|
135 |
+
x = self.proj_in(x)
|
136 |
+
|
137 |
+
if self.to_latents_from_mean_pooled_seq:
|
138 |
+
meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool))
|
139 |
+
meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq)
|
140 |
+
latents = torch.cat((meanpooled_latents, latents), dim=-2)
|
141 |
+
|
142 |
+
for attn, ff in self.layers:
|
143 |
+
latents = attn(x, latents) + latents
|
144 |
+
latents = ff(latents) + latents
|
145 |
+
|
146 |
+
latents = self.proj_out(latents)
|
147 |
+
return self.norm_out(latents)
|
148 |
+
|
149 |
+
|
150 |
+
def masked_mean(t, *, dim, mask=None):
|
151 |
+
if mask is None:
|
152 |
+
return t.mean(dim=dim)
|
153 |
+
|
154 |
+
denom = mask.sum(dim=dim, keepdim=True)
|
155 |
+
mask = rearrange(mask, "b n -> b n 1")
|
156 |
+
masked_t = t.masked_fill(~mask, 0.0)
|
157 |
+
|
158 |
+
return masked_t.sum(dim=dim) / denom.clamp(min=1e-5)
|
ip_adapter/utils.py
ADDED
@@ -0,0 +1,93 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn.functional as F
|
3 |
+
import numpy as np
|
4 |
+
from PIL import Image
|
5 |
+
|
6 |
+
attn_maps = {}
|
7 |
+
def hook_fn(name):
|
8 |
+
def forward_hook(module, input, output):
|
9 |
+
if hasattr(module.processor, "attn_map"):
|
10 |
+
attn_maps[name] = module.processor.attn_map
|
11 |
+
del module.processor.attn_map
|
12 |
+
|
13 |
+
return forward_hook
|
14 |
+
|
15 |
+
def register_cross_attention_hook(unet):
|
16 |
+
for name, module in unet.named_modules():
|
17 |
+
if name.split('.')[-1].startswith('attn2'):
|
18 |
+
module.register_forward_hook(hook_fn(name))
|
19 |
+
|
20 |
+
return unet
|
21 |
+
|
22 |
+
def upscale(attn_map, target_size):
|
23 |
+
attn_map = torch.mean(attn_map, dim=0)
|
24 |
+
attn_map = attn_map.permute(1,0)
|
25 |
+
temp_size = None
|
26 |
+
|
27 |
+
for i in range(0,5):
|
28 |
+
scale = 2 ** i
|
29 |
+
if ( target_size[0] // scale ) * ( target_size[1] // scale) == attn_map.shape[1]*64:
|
30 |
+
temp_size = (target_size[0]//(scale*8), target_size[1]//(scale*8))
|
31 |
+
break
|
32 |
+
|
33 |
+
assert temp_size is not None, "temp_size cannot is None"
|
34 |
+
|
35 |
+
attn_map = attn_map.view(attn_map.shape[0], *temp_size)
|
36 |
+
|
37 |
+
attn_map = F.interpolate(
|
38 |
+
attn_map.unsqueeze(0).to(dtype=torch.float32),
|
39 |
+
size=target_size,
|
40 |
+
mode='bilinear',
|
41 |
+
align_corners=False
|
42 |
+
)[0]
|
43 |
+
|
44 |
+
attn_map = torch.softmax(attn_map, dim=0)
|
45 |
+
return attn_map
|
46 |
+
def get_net_attn_map(image_size, batch_size=2, instance_or_negative=False, detach=True):
|
47 |
+
|
48 |
+
idx = 0 if instance_or_negative else 1
|
49 |
+
net_attn_maps = []
|
50 |
+
|
51 |
+
for name, attn_map in attn_maps.items():
|
52 |
+
attn_map = attn_map.cpu() if detach else attn_map
|
53 |
+
attn_map = torch.chunk(attn_map, batch_size)[idx].squeeze()
|
54 |
+
attn_map = upscale(attn_map, image_size)
|
55 |
+
net_attn_maps.append(attn_map)
|
56 |
+
|
57 |
+
net_attn_maps = torch.mean(torch.stack(net_attn_maps,dim=0),dim=0)
|
58 |
+
|
59 |
+
return net_attn_maps
|
60 |
+
|
61 |
+
def attnmaps2images(net_attn_maps):
|
62 |
+
|
63 |
+
#total_attn_scores = 0
|
64 |
+
images = []
|
65 |
+
|
66 |
+
for attn_map in net_attn_maps:
|
67 |
+
attn_map = attn_map.cpu().numpy()
|
68 |
+
#total_attn_scores += attn_map.mean().item()
|
69 |
+
|
70 |
+
normalized_attn_map = (attn_map - np.min(attn_map)) / (np.max(attn_map) - np.min(attn_map)) * 255
|
71 |
+
normalized_attn_map = normalized_attn_map.astype(np.uint8)
|
72 |
+
#print("norm: ", normalized_attn_map.shape)
|
73 |
+
image = Image.fromarray(normalized_attn_map)
|
74 |
+
|
75 |
+
#image = fix_save_attn_map(attn_map)
|
76 |
+
images.append(image)
|
77 |
+
|
78 |
+
#print(total_attn_scores)
|
79 |
+
return images
|
80 |
+
def is_torch2_available():
|
81 |
+
return hasattr(F, "scaled_dot_product_attention")
|
82 |
+
|
83 |
+
def get_generator(seed, device):
|
84 |
+
|
85 |
+
if seed is not None:
|
86 |
+
if isinstance(seed, list):
|
87 |
+
generator = [torch.Generator(device).manual_seed(seed_item) for seed_item in seed]
|
88 |
+
else:
|
89 |
+
generator = torch.Generator(device).manual_seed(seed)
|
90 |
+
else:
|
91 |
+
generator = None
|
92 |
+
|
93 |
+
return generator
|
requirements.txt
ADDED
@@ -0,0 +1,16 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
diffusers==0.27.2
|
2 |
+
torch>=2.0.0
|
3 |
+
torchvision>=0.15.1
|
4 |
+
transformers>=4.37.1
|
5 |
+
accelerate
|
6 |
+
safetensors
|
7 |
+
einops
|
8 |
+
spaces>=0.19.4
|
9 |
+
omegaconf
|
10 |
+
peft
|
11 |
+
huggingface-hub>=0.20.2
|
12 |
+
opencv-python
|
13 |
+
gradio
|
14 |
+
controlnet_aux
|
15 |
+
gdown
|
16 |
+
peft
|