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| from diffusers import CycleDiffusionPipeline, DDIMScheduler | |
| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| import utils | |
| import ptp_utils | |
| import seq_aligner | |
| import torch.nn.functional as nnf | |
| from typing import Optional, Union, Tuple, List, Callable, Dict | |
| import abc | |
| LOW_RESOURCE = False | |
| MAX_NUM_WORDS = 77 | |
| is_colab = utils.is_google_colab() | |
| model_id_or_path = "CompVis/stable-diffusion-v1-4" | |
| if is_colab: | |
| scheduler = DDIMScheduler.from_config(model_id_or_path, subfolder="scheduler") | |
| pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path, scheduler=scheduler) | |
| else: | |
| import streamlit as st | |
| scheduler = DDIMScheduler.from_config(model_id_or_path, use_auth_token=st.secrets["USER_TOKEN"], subfolder="scheduler") | |
| pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path, use_auth_token=st.secrets["USER_TOKEN"], scheduler=scheduler) | |
| tokenizer = pipe.tokenizer | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to("cuda") | |
| device_print = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶" | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| class LocalBlend: | |
| def __call__(self, x_t, attention_store): | |
| k = 1 | |
| maps = attention_store["down_cross"][2:4] + attention_store["up_cross"][:3] | |
| maps = [item.reshape(self.alpha_layers.shape[0], -1, 1, 16, 16, MAX_NUM_WORDS) for item in maps] | |
| maps = torch.cat(maps, dim=1) | |
| maps = (maps * self.alpha_layers).sum(-1).mean(1) | |
| mask = nnf.max_pool2d(maps, (k * 2 + 1, k * 2 + 1), (1, 1), padding=(k, k)) | |
| mask = nnf.interpolate(mask, size=(x_t.shape[2:])) | |
| mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0] | |
| mask = mask.gt(self.threshold) | |
| mask = (mask[:1] + mask[1:]).float() | |
| x_t = x_t[:1] + mask * (x_t - x_t[:1]) | |
| return x_t | |
| def __init__(self, prompts: List[str], words: [List[List[str]]], threshold=.3): | |
| alpha_layers = torch.zeros(len(prompts), 1, 1, 1, 1, MAX_NUM_WORDS) | |
| for i, (prompt, words_) in enumerate(zip(prompts, words)): | |
| if type(words_) is str: | |
| words_ = [words_] | |
| for word in words_: | |
| ind = ptp_utils.get_word_inds(prompt, word, tokenizer) | |
| alpha_layers[i, :, :, :, :, ind] = 1 | |
| self.alpha_layers = alpha_layers.to(device) | |
| self.threshold = threshold | |
| class AttentionControl(abc.ABC): | |
| def step_callback(self, x_t): | |
| return x_t | |
| def between_steps(self): | |
| return | |
| def num_uncond_att_layers(self): | |
| return self.num_att_layers if LOW_RESOURCE else 0 | |
| def forward(self, attn, is_cross: bool, place_in_unet: str): | |
| raise NotImplementedError | |
| def __call__(self, attn, is_cross: bool, place_in_unet: str): | |
| if self.cur_att_layer >= self.num_uncond_att_layers: | |
| if LOW_RESOURCE: | |
| attn = self.forward(attn, is_cross, place_in_unet) | |
| else: | |
| h = attn.shape[0] | |
| attn[h // 2:] = self.forward(attn[h // 2:], is_cross, place_in_unet) | |
| self.cur_att_layer += 1 | |
| if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers: | |
| self.cur_att_layer = 0 | |
| self.cur_step += 1 | |
| self.between_steps() | |
| return attn | |
| def reset(self): | |
| self.cur_step = 0 | |
| self.cur_att_layer = 0 | |
| def __init__(self): | |
| self.cur_step = 0 | |
| self.num_att_layers = -1 | |
| self.cur_att_layer = 0 | |
| class EmptyControl(AttentionControl): | |
| def forward(self, attn, is_cross: bool, place_in_unet: str): | |
| return attn | |
| class AttentionStore(AttentionControl): | |
| def get_empty_store(): | |
| return {"down_cross": [], "mid_cross": [], "up_cross": [], | |
| "down_self": [], "mid_self": [], "up_self": []} | |
| def forward(self, attn, is_cross: bool, place_in_unet: str): | |
| key = f"{place_in_unet}_{'cross' if is_cross else 'self'}" | |
| if attn.shape[1] <= 32 ** 2: # avoid memory overhead | |
| self.step_store[key].append(attn) | |
| return attn | |
| def between_steps(self): | |
| if len(self.attention_store) == 0: | |
| self.attention_store = self.step_store | |
| else: | |
| for key in self.attention_store: | |
| for i in range(len(self.attention_store[key])): | |
| self.attention_store[key][i] += self.step_store[key][i] | |
| self.step_store = self.get_empty_store() | |
| def get_average_attention(self): | |
| average_attention = {key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store} | |
| return average_attention | |
| def reset(self): | |
| super(AttentionStore, self).reset() | |
| self.step_store = self.get_empty_store() | |
| self.attention_store = {} | |
| def __init__(self): | |
| super(AttentionStore, self).__init__() | |
| self.step_store = self.get_empty_store() | |
| self.attention_store = {} | |
| class AttentionControlEdit(AttentionStore, abc.ABC): | |
| def step_callback(self, x_t): | |
| if self.local_blend is not None: | |
| x_t = self.local_blend(x_t, self.attention_store) | |
| return x_t | |
| def replace_self_attention(self, attn_base, att_replace): | |
| if att_replace.shape[2] <= 16 ** 2: | |
| return attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape) | |
| else: | |
| return att_replace | |
| def replace_cross_attention(self, attn_base, att_replace): | |
| raise NotImplementedError | |
| def forward(self, attn, is_cross: bool, place_in_unet: str): | |
| super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet) | |
| if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]): | |
| h = attn.shape[0] // self.batch_size | |
| attn = attn.reshape(self.batch_size, h, *attn.shape[1:]) | |
| attn_base, attn_repalce = attn[0], attn[1:] | |
| if is_cross: | |
| alpha_words = self.cross_replace_alpha[self.cur_step] | |
| attn_replace_new = self.replace_cross_attention(attn_base, attn_repalce) * alpha_words + (1 - alpha_words) * attn_repalce | |
| attn[1:] = attn_replace_new | |
| else: | |
| attn[1:] = self.replace_self_attention(attn_base, attn_repalce) | |
| attn = attn.reshape(self.batch_size * h, *attn.shape[2:]) | |
| return attn | |
| def __init__(self, prompts, num_steps: int, | |
| cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]], | |
| self_replace_steps: Union[float, Tuple[float, float]], | |
| local_blend: Optional[LocalBlend]): | |
| super(AttentionControlEdit, self).__init__() | |
| self.batch_size = len(prompts) | |
| self.cross_replace_alpha = ptp_utils.get_time_words_attention_alpha(prompts, num_steps, cross_replace_steps, tokenizer).to(device) | |
| if type(self_replace_steps) is float: | |
| self_replace_steps = 0, self_replace_steps | |
| self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1]) | |
| self.local_blend = local_blend | |
| class AttentionReplace(AttentionControlEdit): | |
| def replace_cross_attention(self, attn_base, att_replace): | |
| return torch.einsum('hpw,bwn->bhpn', attn_base, self.mapper) | |
| def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float, | |
| local_blend: Optional[LocalBlend] = None): | |
| super(AttentionReplace, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend) | |
| self.mapper = seq_aligner.get_replacement_mapper(prompts, tokenizer).to(device) | |
| class AttentionRefine(AttentionControlEdit): | |
| def replace_cross_attention(self, attn_base, att_replace): | |
| attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3) | |
| attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas) | |
| return attn_replace | |
| def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float, | |
| local_blend: Optional[LocalBlend] = None): | |
| super(AttentionRefine, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend) | |
| self.mapper, alphas = seq_aligner.get_refinement_mapper(prompts, tokenizer) | |
| self.mapper, alphas = self.mapper.to(device), alphas.to(device) | |
| self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1]) | |
| def get_equalizer(text: str, word_select: Union[int, Tuple[int, ...]], values: Union[List[float], Tuple[float, ...]]): | |
| if type(word_select) is int or type(word_select) is str: | |
| word_select = (word_select,) | |
| equalizer = torch.ones(len(values), 77) | |
| values = torch.tensor(values, dtype=torch.float32) | |
| for word in word_select: | |
| inds = ptp_utils.get_word_inds(text, word, tokenizer) | |
| equalizer[:, inds] = values | |
| return equalizer | |
| def inference(source_prompt, target_prompt, source_guidance_scale=1, guidance_scale=5, num_inference_steps=100, | |
| width=512, height=512, seed=0, img=None, strength=0.7, | |
| cross_attention_control="None", cross_replace_steps=0.8, self_replace_steps=0.4): | |
| torch.manual_seed(seed) | |
| ratio = min(height / img.height, width / img.width) | |
| img = img.resize((int(img.width * ratio), int(img.height * ratio))) | |
| # create the CAC controller. | |
| if cross_attention_control == "Replace": | |
| controller = AttentionReplace([source_prompt, target_prompt], | |
| num_inference_steps, | |
| cross_replace_steps=cross_replace_steps, | |
| self_replace_steps=self_replace_steps, | |
| ) | |
| ptp_utils.register_attention_control(pipe, controller) | |
| elif cross_attention_control == "Refine": | |
| controller = AttentionRefine([source_prompt, target_prompt], | |
| num_inference_steps, | |
| cross_replace_steps=cross_replace_steps, | |
| self_replace_steps=self_replace_steps, | |
| ) | |
| ptp_utils.register_attention_control(pipe, controller) | |
| elif cross_attention_control == "None": | |
| controller = EmptyControl() | |
| ptp_utils.register_attention_control(pipe, controller) | |
| else: | |
| raise ValueError("Unknown cross_attention_control: {}".format(cross_attention_control)) | |
| results = pipe(prompt=target_prompt, | |
| source_prompt=source_prompt, | |
| init_image=img, | |
| num_inference_steps=num_inference_steps, | |
| eta=0.1, | |
| strength=strength, | |
| guidance_scale=guidance_scale, | |
| source_guidance_scale=source_guidance_scale, | |
| ) | |
| return replace_nsfw_images(results) | |
| def replace_nsfw_images(results): | |
| for i in range(len(results.images)): | |
| if results.nsfw_content_detected[i]: | |
| results.images[i] = Image.open("nsfw.png") | |
| return results.images[0] | |
| css = """.cycle-diffusion-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.cycle-diffusion-div div h1{font-weight:900;margin-bottom:7px}.cycle-diffusion-div p{margin-bottom:10px;font-size:94%}.cycle-diffusion-div p a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem} | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| gr.HTML( | |
| f""" | |
| <div class="cycle-diffusion-div"> | |
| <div> | |
| <h1>CycleDiffusion with Stable Diffusion</h1> | |
| </div> | |
| <p> | |
| Demo for CycleDiffusion with Stable Diffusion. <br> | |
| CycleDiffusion (<a href="https://arxiv.org/abs/2210.05559">📄 Paper link</a> | <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/cycle_diffusion">🧨 Pipeline doc</a>) is an image-to-image translation method that supports stochastic samplers for diffusion models. <br> | |
| We also support the combination of CycleDiffusion and Cross Attention Control (CAC | <a href="https://arxiv.org/abs/2208.01626">📄 Paper link</a>). CAC is a technique to transfer the attention map from the source prompt to the target prompt. <br> | |
| </p> | |
| <p> | |
| <b>Quick start</b>: <br> | |
| 1. Click one row of Examples at the end of this page. It will fill all inputs needed. <br> | |
| 2. Click the "Edit" button. <br> | |
| </p> | |
| <p> | |
| <b>How to use:</b> <br> | |
| 1. Upload an image. <br> | |
| 2. Enter the source and target prompts. <br> | |
| 3. Select the source guidance scale (for "encoding") and the target guidance scale (for "decoding"). <br> | |
| 4. Select the strength (smaller strength means better content preservation). <br> | |
| 5 (optional). Configurate Cross Attention Control options (e.g., CAC type, cross replace steps, self replace steps). <br> | |
| 6 (optional). Configurate other options (e.g., image size, inference steps, random seed). <br> | |
| 7. Click the "Edit" button. <br> | |
| </p> | |
| <p> | |
| <b>Notes:</b> <br> | |
| 1. CycleDiffusion is likely to fail when drastic changes are intended (e.g., changing a large black car to red). <br> | |
| 2. The value of strength can be set larger when CAC is used. <br> | |
| 3. If CAC type is "Replace", the source and target prompts should differ in only one token; otherwise, an error will be raised. This is why we deliberately make some grammar mistakes in Examples.<br> | |
| 4. If CAC type is "Refine", the source prompt be a subsequence of the target prompt; otherwise, an error will be raised. <br> | |
| </p> | |
| <p>You can skip the queue using Colab: <a href="https://colab.research.google.com/gist/ChenWu98/0aa4fe7be80f6b45d3d055df9f14353a/copy-of-fine-tuned-diffusion-gradio.ipynb"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a></p> | |
| Running on <b>{device_print}</b>{(" in a <b>Google Colab</b>." if is_colab else "")} | |
| </p> | |
| </div> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=55): | |
| with gr.Group(): | |
| img = gr.Image(label="Input image", height=512, tool="editor", type="pil") | |
| image_out = gr.Image(label="Output image", height=512) | |
| # gallery = gr.Gallery( | |
| # label="Generated images", show_label=False, elem_id="gallery" | |
| # ).style(grid=[1], height="auto") | |
| with gr.Column(scale=45): | |
| with gr.Tab("Edit options"): | |
| with gr.Group(): | |
| with gr.Row(): | |
| source_prompt = gr.Textbox(label="Source prompt", placeholder="Source prompt describes the input image") | |
| source_guidance_scale = gr.Slider(label="Source guidance scale", value=1, minimum=1, maximum=10) | |
| with gr.Row(): | |
| target_prompt = gr.Textbox(label="Target prompt", placeholder="Target prompt describes the output image") | |
| guidance_scale = gr.Slider(label="Target guidance scale", value=5, minimum=1, maximum=10) | |
| with gr.Row(): | |
| strength = gr.Slider(label="Strength", value=0.7, minimum=0.5, maximum=1, step=0.01) | |
| with gr.Row(): | |
| generate1 = gr.Button(value="Edit") | |
| with gr.Tab("CAC options"): | |
| with gr.Group(): | |
| with gr.Row(): | |
| cross_attention_control = gr.Radio(label="CAC type", choices=["None", "Replace", "Refine"], value="None") | |
| with gr.Row(): | |
| # If not "None", the following two parameters will be used. | |
| cross_replace_steps = gr.Slider(label="Cross replace steps", value=0.8, minimum=0.0, maximum=1, step=0.01) | |
| self_replace_steps = gr.Slider(label="Self replace steps", value=0.4, minimum=0.0, maximum=1, step=0.01) | |
| with gr.Row(): | |
| generate2 = gr.Button(value="Edit") | |
| with gr.Tab("Other options"): | |
| with gr.Group(): | |
| with gr.Row(): | |
| num_inference_steps = gr.Slider(label="Number of inference steps", value=100, minimum=25, maximum=500, step=1) | |
| width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8) | |
| height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8) | |
| with gr.Row(): | |
| seed = gr.Slider(0, 2147483647, label='Seed', value=0, step=1) | |
| with gr.Row(): | |
| generate3 = gr.Button(value="Edit") | |
| inputs = [source_prompt, target_prompt, source_guidance_scale, guidance_scale, num_inference_steps, | |
| width, height, seed, img, strength, | |
| cross_attention_control, cross_replace_steps, self_replace_steps] | |
| generate1.click(inference, inputs=inputs, outputs=image_out) | |
| generate2.click(inference, inputs=inputs, outputs=image_out) | |
| generate3.click(inference, inputs=inputs, outputs=image_out) | |
| ex = gr.Examples( | |
| [ | |
| ["An astronaut riding a horse", "An astronaut riding an elephant", 1, 2, 100, "images/astronaut_horse.png", 0.8, "None", 0, 0], | |
| ["An astronaut riding a horse", "An astronaut riding a elephant", 1, 2, 100, "images/astronaut_horse.png", 0.9, "Replace", 0.15, 0.10], | |
| ["A black colored car.", "A blue colored car.", 1, 3, 100, "images/black_car.png", 0.85, "None", 0, 0], | |
| ["A black colored car.", "A blue colored car.", 1, 5, 100, "images/black_car.png", 0.95, "Replace", 0.8, 0.4], | |
| ["A black colored car.", "A red colored car.", 1, 5, 100, "images/black_car.png", 1, "Replace", 0.8, 0.4], | |
| ["An aerial view of autumn scene.", "An aerial view of winter scene.", 1, 5, 100, "images/mausoleum.png", 0.9, "None", 0, 0], | |
| ["An aerial view of autumn scene.", "An aerial view of winter scene.", 1, 5, 100, "images/mausoleum.png", 1, "Replace", 0.8, 0.4], | |
| ["A green apple and a black backpack on the floor.", "A red apple and a black backpack on the floor.", 1, 7, 100, "images/apple_bag.png", 0.9, "None", 0, 0], | |
| ["A green apple and a black backpack on the floor.", "A red apple and a black backpack on the floor.", 1, 7, 100, "images/apple_bag.png", 0.9, "Replace", 0.8, 0.4], | |
| ["A hotel room with red flowers on the bed.", "A hotel room with a cat sitting on the bed.", 1, 4, 100, "images/flower_hotel.png", 0.8, "None", 0, 0], | |
| ["A hotel room with red flowers on the bed.", "A hotel room with blue flowers on the bed.", 1, 5, 100, "images/flower_hotel.png", 0.95, "None", 0, 0], | |
| ["A green apple and a black backpack on the floor.", "Two green apples and a black backpack on the floor.", 1, 5, 100, "images/apple_bag.png", 0.89, "None", 0, 0], | |
| ], | |
| [source_prompt, target_prompt, source_guidance_scale, guidance_scale, num_inference_steps, | |
| img, strength, | |
| cross_attention_control, cross_replace_steps, self_replace_steps], | |
| image_out, inference, cache_examples=False) | |
| gr.Markdown(''' | |
| Space built with Diffusers 🧨 by HuggingFace 🤗. | |
| [](https://twitter.com/ChenHenryWu) | |
|  | |
| ''') | |
| if not is_colab: | |
| demo.queue(concurrency_count=1) | |
| demo.launch(debug=is_colab, share=is_colab) | |