Performance PR

.gitattributes

@@ -1,32 +1,35 @@
-nsfweffnetv2-b02-3epochs.h5 filter=lfs diff=lfs merge=lfs -text
-fifth.gemb_.pt filter=lfs diff=lfs merge=lfs -text
-ninth.im_.pt filter=lfs diff=lfs merge=lfs -text
-tenth.gemb_.pt filter=lfs diff=lfs merge=lfs -text
-third.gemb_.pt filter=lfs diff=lfs merge=lfs -text
-eigth.gemb_.pt filter=lfs diff=lfs merge=lfs -text
-first.gemb_.pt filter=lfs diff=lfs merge=lfs -text
-fourth.gemb_.pt filter=lfs diff=lfs merge=lfs -text
-ninth.gemb_.pt filter=lfs diff=lfs merge=lfs -text
-sixth.gemb_.pt filter=lfs diff=lfs merge=lfs -text
-tenth.im_.pt filter=lfs diff=lfs merge=lfs -text
-eigth.im_.pt filter=lfs diff=lfs merge=lfs -text
-seventh.gemb_.pt filter=lfs diff=lfs merge=lfs -text
-sixth.im_.pt filter=lfs diff=lfs merge=lfs -text
-third.im_.pt filter=lfs diff=lfs merge=lfs -text
-fifth.im_.pt filter=lfs diff=lfs merge=lfs -text
-first.im_.pt filter=lfs diff=lfs merge=lfs -text
-fourth.im_.pt filter=lfs diff=lfs merge=lfs -text
-second.gemb_.pt filter=lfs diff=lfs merge=lfs -text
-second.im_.pt filter=lfs diff=lfs merge=lfs -text
-seventh.im_.pt filter=lfs diff=lfs merge=lfs -text
-image';l';.png filter=lfs diff=lfs merge=lfs -text
-second.png filter=lfs diff=lfs merge=lfs -text
-sixth.png filter=lfs diff=lfs merge=lfs -text
-third.png filter=lfs diff=lfs merge=lfs -text
-fifth.png filter=lfs diff=lfs merge=lfs -text
-first.png filter=lfs diff=lfs merge=lfs -text
-fourth.png filter=lfs diff=lfs merge=lfs -text
-*.mp4 filter=lfs diff=lfs merge=lfs -text
-*.png filter=lfs diff=lfs merge=lfs -text
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
 *.safetensors filter=lfs diff=lfs merge=lfs -text
-last_epoch_ckpt/diffusion_pytorch_model.safetensors
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,2 +0,0 @@
-__pycache__*
-.gradio/

LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2025 rynmurdock
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

README.md

@@ -1,26 +1,12 @@
 ---
-license: mit
-title: The Other Tiger
+title: Generative Recsys
+emoji: 🐨
+colorFrom: purple
+colorTo: blue
 sdk: gradio
-sdk_version: 5.31.0
-emoji: 👁
-colorFrom: blue
-colorTo: purple
-pinned: true
+sdk_version: 4.25.0
+app_file: app.py
+pinned: false
 ---
 
-# The Other Tiger
-
-## tl;dr
-Train on embeddings of media preferred by a specific user -> produce embeddings of media they may enjoy.
-
-In our case here, we take the ECLIPSE `text embedding -> image embedding` prior (https://arxiv.org/abs/2312.04655) and finetune it to become a `preferred image embeddings -> heldout image embedding` prior.
-
-### Related work:
-
-Patron et al. models preference using a diffusion prior and condition on user ids with ratings: https://arxiv.org/abs/2502.18477
-
-Wang et al. models preference using a generator conditioned on averaged CLIP embeddings of users:  https://arxiv.org/abs/2304.03516
-
-My previous work based on Collaborative Filtering with CLIP embeddings: https://github.com/rynmurdock/generative_recommender
-
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,289 +1,169 @@
+DEVICE = 'cpu'
 
 import gradio as gr
+import numpy as np
+from sklearn.svm import LinearSVC
+from sklearn import preprocessing
+import pandas as pd
+
+from diffusers import LCMScheduler, AutoencoderTiny, EulerDiscreteScheduler, UNet2DConditionModel
+from diffusers.models import ImageProjection
+from patch_sdxl import SDEmb
+import torch
+import spaces
+
 import random
 import time
+
 import torch
-import glob
+from urllib.request import urlopen
 
-import config
-from model import get_model_and_tokenizer
+from PIL import Image
+import requests
+from io import BytesIO, StringIO
 
-model, model.prior_pipe.image_encoder = get_model_and_tokenizer(config.model_path, 
-                                                                'cuda', torch.bfloat16)
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
 
-# TODO unify/merge origin and this
-# TODO save & restart from (if it exists) dataframe parquet
+prompt_list = [p for p in list(set(
+                pd.read_csv('./twitter_prompts.csv').iloc[:, 1].tolist())) if type(p) == str]
 
-device = "cuda"
+start_time = time.time()
 
+####################### Setup Model
+model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+sdxl_lightening = "ByteDance/SDXL-Lightning"
+ckpt = "sdxl_lightning_2step_unet.safetensors"
+unet = UNet2DConditionModel.from_config(model_id, subfolder="unet").to("cuda", torch.float16)
+unet.load_state_dict(load_file(hf_hub_download(sdxl_lightening, ckpt), device="cuda"))
+pipe = SDEmb.from_pretrained(model_id, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
+pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesdxl", torch_dtype=torch.float16)
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+pipe.to(device='cuda')
+pipe.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
+
+output_hidden_state = False
+#######################
 
-import spaces
-import matplotlib.pyplot as plt
+@spaces.GPU
+def predict(
+        prompt,
+        im_emb=None,
+        progress=gr.Progress(track_tqdm=True)
+    ):
+    """Run a single prediction on the model"""
+    with torch.no_grad():
+        if im_emb == None:
+            im_emb = torch.zeros(1, 1280, dtype=torch.float16, device='cuda')
+        image = pipe(
+            prompt=prompt,
+            ip_adapter_emb=im_emb.to('cuda'),
+            height=1024,
+            width=1024,                                                                                                             
+            num_inference_steps=2,
+            guidance_scale=0,
+            ).images[0]
+        im_emb, _ = pipe.encode_image(
+                image, 'cuda', 1, output_hidden_state
+            )
+        return image, im_emb.to(DEVICE)
 
-import os
-import gradio as gr
-import pandas as pd
-from apscheduler.schedulers.background import BackgroundScheduler
+# TODO add to state instead of shared across all
+glob_idx = 0
 
-import random
-import time
-from PIL import Image
-# from safety_checker_improved import maybe_nsfw
+def next_image(embs, ys, calibrate_prompts):
+    global glob_idx
+    glob_idx = glob_idx + 1
 
+    # handle case where every instance of calibration prompts is 'Neither' or 'Like' or 'Dislike'
+    if len(calibrate_prompts) == 0 and len(list(set(ys))) <= 1:
+        embs.append(.01*torch.randn(1, 1280))
+        embs.append(.01*torch.randn(1, 1280))
+        ys.append(0)
+        ys.append(1)
+        
+    with torch.no_grad():
+        if len(calibrate_prompts) > 0:
+            print('######### Calibrating with sample prompts #########')
+            prompt = calibrate_prompts.pop(0)
+            print(prompt)
+            image, img_emb = predict(prompt)
+            embs.append(img_emb)
+            return image, embs, ys, calibrate_prompts
+        else:
+            print('######### Roaming #########')
+            # sample only as many negatives as there are positives
+            indices = range(len(ys))
+            pos_indices = [i for i in indices if ys[i] == 1]
+            neg_indices = [i for i in indices if ys[i] == 0]
+            lower = min(len(pos_indices), len(neg_indices))
+            neg_indices = random.sample(neg_indices, lower)
+            pos_indices = random.sample(pos_indices, lower)
 
-torch.set_grad_enabled(False)
-torch.backends.cuda.matmul.allow_tf32 = True
-torch.backends.cudnn.allow_tf32 = True
+            cut_embs = [embs[i] for i in neg_indices] + [embs[i] for i in pos_indices]
+            cut_ys = [ys[i] for i in neg_indices] + [ys[i] for i in pos_indices]
 
-prevs_df = pd.DataFrame(columns=['paths', 'embeddings', 'ips', 'user:rating', 'latest_user_to_rate', 'from_user_id', 'text', 'gemb'])
+            feature_embs = torch.stack([e[0].detach().cpu() for e in cut_embs])
+            scaler = preprocessing.StandardScaler().fit(feature_embs)
+            feature_embs = scaler.transform(feature_embs)
+            print(np.array(feature_embs).shape, np.array(ys).shape)
 
-import spaces
-start_time = time.time()
+            lin_class = LinearSVC(max_iter=50000, dual='auto', class_weight='balanced').fit(np.array(feature_embs), np.array(cut_ys))
+            lin_class.coef_ = torch.tensor(lin_class.coef_, dtype=torch.double)
+            lin_class.coef_ = (lin_class.coef_.flatten() / (lin_class.coef_.flatten().norm())).unsqueeze(0)
 
-####################### Setup Model
-from diffusers import EulerDiscreteScheduler
-from PIL import Image
-import uuid
 
+            rng_prompt = random.choice(prompt_list)
 
-@spaces.GPU()
-def generate_gpu(in_im_embs, prompt='the scene'):
-    with torch.no_grad():
-        in_im_embs = in_im_embs.to('cuda')
-
-        negative_image_embeds = in_im_embs[0] # model.prior_pipe.get_zero_embed()
-        positive_image_embeds = in_im_embs[1]
-
-        images = model.kandinsky_pipe(
-            num_inference_steps=50,
-            image_embeds=positive_image_embeds,
-            negative_image_embeds=negative_image_embeds,
-            guidance_scale=11,
-        ).images[0]
-        cond = (
-                    model.prior_pipe.image_processor(images, return_tensors="pt")
-                    .pixel_values[0]
-                    .unsqueeze(0)
-                    .to(dtype=model.prior_pipe.image_encoder.dtype, device=device)
-                    )
-        im_emb = model.prior_pipe.image_encoder(cond)["image_embeds"]
-    return images, im_emb
-
-
-def generate(in_im_embs, ):
-    output, im_emb = generate_gpu(in_im_embs)
-    nsfw = False#maybe_nsfw(output.images[0])
-    
-    name = str(uuid.uuid4()).replace("-", "")
-    path = f"/tmp/{name}.png"
-    
-    if nsfw:
-        gr.Warning("NSFW content detected.")
-        # TODO could return an automatic dislike of auto dislike on the backend for neither as well; just would need refactoring.
-        return None, im_emb
-    
-    output.save(path)
-    return path, im_emb
+            w = 1# if len(embs) % 2 == 0 else 0
+            im_emb = w * lin_class.coef_.to(device=DEVICE, dtype=torch.float16)
+            prompt= 'an image' if glob_idx % 2 == 0 else rng_prompt
+            print(prompt)
+            image, im_emb = predict(prompt, im_emb)
+            embs.append(im_emb)
+            return image, embs, ys, calibrate_prompts
 
 
-#######################
 
-@spaces.GPU()
-def sample_embs(prompt_embeds):
-    latent = torch.randn(prompt_embeds.shape[0], 1, prompt_embeds.shape[-1])
-    if prompt_embeds.shape[1] < 8: # TODO grab as `k` arg from config
-            prompt_embeds = torch.nn.functional.pad(prompt_embeds, [0, 0, 0, 8-prompt_embeds.shape[1]])
-    assert prompt_embeds.shape[1] == 8, f"The model is set to take `k`` cond image embeds but is shape {prompt_embeds.shape}"
-    image_embeds = model(latent.to('cuda'), prompt_embeds.to('cuda')).predicted_image_embedding
-
-    return image_embeds
-
-@spaces.GPU()
-def get_user_emb(embs, ys):
-    positives = [e for e, ys in zip(embs, ys) if ys == 1]
-    embs = random.sample(positives, min(8, len(positives)))
-    if len(embs) == 0:
-        positives = torch.zeros_like(im_emb)[None]
-    else:
-        positives = torch.stack(embs, 1)
-
-    negs = [e for e, ys in zip(embs, ys) if ys == 0]
-    negative_embs = random.sample(negs, min(8, len(negs)))
-    if len(negative_embs) == 0:
-        negatives = torch.zeros_like(im_emb)[None]
-    else:
-        negatives = torch.stack(negative_embs, 1)
-
-    image_embeds = torch.stack([sample_embs(negatives), sample_embs(positives)])
-
-    return image_embeds
-
-
-def background_next_image():
-        global prevs_df
-        # only let it get N (maybe 3) ahead of the user
-        #not_rated_rows = prevs_df[[i[1]['user:rating'] == {' ': ' '} for i in prevs_df.iterrows()]]
-        rated_rows = prevs_df[[i[1]['user:rating'] != {' ': ' '} for i in prevs_df.iterrows()]]
-        if len(rated_rows) < 4:
-            time.sleep(.1)
-        #    not_rated_rows = prevs_df[[i[1]['user:rating'] == {' ': ' '} for i in prevs_df.iterrows()]]
-            return
-
-        user_id_list = set(rated_rows['latest_user_to_rate'].to_list())
-        for uid in user_id_list:
-            rated_rows = prevs_df[[i[1]['user:rating'].get(uid, None) is not None for i in prevs_df.iterrows()]]
-            not_rated_rows = prevs_df[[i[1]['user:rating'].get(uid, None) is None for i in prevs_df.iterrows()]]
-            
-            # we need to intersect not_rated_rows from this user's embed > 7. Just add a new column on which user_id spawned the 
-            #   media. 
-            
-            unrated_from_user = not_rated_rows[[i[1]['from_user_id'] == uid for i in not_rated_rows.iterrows()]]
-
-            # we don't compute more after n are in the queue for them
-            if len(unrated_from_user) >= 10:
-                continue
-            
-            if len(rated_rows) < 4:
-                continue
-
-            global glob_idx
-            glob_idx += 1
-            
-            ems = rated_rows['embeddings'].to_list()
-            ys = [i[uid][0] for i in rated_rows['user:rating'].to_list()]
-
-            emz = get_user_emb(ems, ys)
-            img, embs = generate(emz)
-            
-            if img:
-                tmp_df = pd.DataFrame(columns=['paths', 'embeddings', 'ips', 'user:rating', 'latest_user_to_rate', 'text', 'gemb'])
-                tmp_df['paths'] = [img]
-                tmp_df['embeddings'] = [embs.to(torch.float32).to('cpu')]
-                tmp_df['user:rating'] = [{' ': ' '}]
-                tmp_df['from_user_id'] = [uid]
-                tmp_df['text'] = ['']
-                prevs_df = pd.concat((prevs_df, tmp_df))
-                # we can free up storage by deleting the image
-                if len(prevs_df) > 500:
-                    oldest_path = prevs_df.iloc[6]['paths']
-                    if os.path.isfile(oldest_path):
-                        os.remove(oldest_path)
-                    else:
-                        # If it fails, inform the user.
-                        print("Error: %s file not found" % oldest_path)
-                    # only keep 50 images & embeddings & ips, then remove oldest besides calibrating
-                    prevs_df = pd.concat((prevs_df.iloc[:6], prevs_df.iloc[7:]))
-    
-def pluck_img(user_id):
-    rated_rows = prevs_df[[i[1]['user:rating'].get(user_id, None) is not None for i in prevs_df.iterrows()]]
-    ems = rated_rows['embeddings'].to_list()
-    ys = [i[user_id][0] for i in rated_rows['user:rating'].to_list()]
-    user_emb = get_user_emb(ems, ys)
-
-    not_rated_rows = prevs_df[[i[1]['user:rating'].get(user_id, 'gone') == 'gone' for i in prevs_df.iterrows()]]
-    while len(not_rated_rows) == 0:
-        not_rated_rows = prevs_df[[i[1]['user:rating'].get(user_id, 'gone') == 'gone' for i in prevs_df.iterrows()]]
-        time.sleep(.1)
-        # TODO optimize this lol
-
-    unrated_from_user = not_rated_rows[[i[1]['from_user_id'] == user_id for i in not_rated_rows.iterrows()]]
-    if len(unrated_from_user) > 0:
-        print(unrated_from_user)
-        # NOTE the way I've setup pandas here is so gdm horrible. TODO overhaul
-        img = unrated_from_user['paths'].to_list()[-1]
-        return img
-
-    best_sim = -10000000
-    for i in not_rated_rows.iterrows():
-        # TODO sloppy .to but it is 3am.
-        sim = torch.cosine_similarity(i[1]['embeddings'].detach().to('cpu'), user_emb.detach().to('cpu'), -1)
-        if len(sim) > 1: sim = sim[1]
-        if sim.squeeze() > best_sim:
-            best_sim = sim
-            best_row = i[1]
-    img = best_row['paths']
-    return img
-
-def next_image(calibrate_prompts, user_id):
-    with torch.no_grad():
-        # once we've done so many random calibration prompts out of the full media
-        if len(m_calibrate) - len(calibrate_prompts) < 5:
-            cal_video = calibrate_prompts.pop(random.randint(0, len(calibrate_prompts)-1))
-            image = prevs_df[prevs_df['paths'] == cal_video]['paths'].to_list()[0]
-        # we switch to just getting media by similarity.
-        else:
-            image = pluck_img(user_id)
-        return image, calibrate_prompts
 
 
 
 
 
 
-def start(_, calibrate_prompts, user_id, request: gr.Request):
-    user_id = int(str(time.time())[-7:].replace('.', ''))
-    image, calibrate_prompts = next_image(calibrate_prompts, user_id)
+def start(_, embs, ys, calibrate_prompts):
+    image, embs, ys, calibrate_prompts = next_image(embs, ys, calibrate_prompts)
     return [
-            gr.Button(value='👍', interactive=True), 
-            gr.Button(value='Neither (Space)', interactive=True, visible=False), 
-            gr.Button(value='👎', interactive=True),
+            gr.Button(value='Like (L)', interactive=True), 
+            gr.Button(value='Neither (Space)', interactive=True), 
+            gr.Button(value='Dislike (A)', interactive=True),
             gr.Button(value='Start', interactive=False),
-            gr.Button(value='👍 Content', interactive=True, visible=False),
-            gr.Button(value='👍 Style', interactive=True, visible=False),
             image,
-            calibrate_prompts,
-            user_id,
+            embs,
+            ys,
+            calibrate_prompts
             ]
 
 
-def choose(img, choice, calibrate_prompts, user_id, request: gr.Request):
-    global prevs_df
-    
-    if choice == '👍':
-        choice = [1, 1]
-    elif choice == 'Neither (Space)':
-        img, calibrate_prompts = next_image(calibrate_prompts, user_id)
-        return img, calibrate_prompts
-    elif choice == '👎':
-        choice = [0, 0]
-    elif choice == '👍 Style':
-        choice = [0, 1]
-    elif choice == '👍 Content':
-        choice = [1, 0]
-    else:
-        assert False, f'choice is {choice}'
-    
-    # if we detected NSFW, leave that area of latent space regardless of how they rated chosen.
-    # TODO skip allowing rating & just continue
-    if img is None:
-        print('NSFW -- choice is disliked')
-        choice = [0, 0]
-    
-    row_mask = [p.split('/')[-1] in img for p in prevs_df['paths'].to_list()]
-    # if it's still in the dataframe, add the choice
-    if len(prevs_df.loc[row_mask, 'user:rating']) > 0:
-        prevs_df.loc[row_mask, 'user:rating'][0][user_id] = choice
-        prevs_df.loc[row_mask, 'latest_user_to_rate'] = [user_id]
+def choose(choice, embs, ys, calibrate_prompts):
+    if choice == 'Like':
+        choice = 1
+    elif choice == 'Neither':
+        _ = embs.pop(-1)
+        img, embs, ys, calibrate_prompts = next_image(embs, ys, calibrate_prompts)
+        return img, embs, ys, calibrate_prompts
     else:
-        print('Image apparently removed', img)
-    img, calibrate_prompts = next_image(calibrate_prompts, user_id)
-    return img, calibrate_prompts
+        choice = 0
+    ys.append(choice)
+    img, embs, ys, calibrate_prompts = next_image(embs, ys, calibrate_prompts)
+    return img, embs, ys, calibrate_prompts
 
 css = '''.gradio-container{max-width: 700px !important}
 #description{text-align: center}
-#description h1, #description h3{display: block}
+#description h1{display: block}
 #description p{margin-top: 0}
-.fade-in-out {animation: fadeInOut 3s forwards}
-@keyframes fadeInOut {
-    0% {
-      background: var(--bg-color);
-    }
-    100% {
-      background: var(--button-secondary-background-fill);
-    }
-}
 '''
-js_head = '''
+js = '''
 <script>
 document.addEventListener('keydown', function(event) {
     if (event.key === 'a' || event.key === 'A') {
@@ -297,143 +177,54 @@ document.addEventListener('keydown', function(event) {
         document.getElementById('like').click();
     }
 });
-function fadeInOut(button, color) {
-  button.style.setProperty('--bg-color', color);
-  button.classList.remove('fade-in-out');
-  void button.offsetWidth; // This line forces a repaint by accessing a DOM property
-  
-  button.classList.add('fade-in-out');
-  button.addEventListener('animationend', () => {
-    button.classList.remove('fade-in-out'); // Reset the animation state
-  }, {once: true});
-}
-document.body.addEventListener('click', function(event) {
-    const target = event.target;
-    if (target.id === 'dislike') {
-      fadeInOut(target, '#ff1717');
-    } else if (target.id === 'like') {
-      fadeInOut(target, '#006500');
-    } else if (target.id === 'neither') {
-      fadeInOut(target, '#cccccc');
-    }
-});
-
 </script>
 '''
 
-with gr.Blocks(head=js_head, css=css) as demo:
-    gr.Markdown('''# The Other Tiger
-### Generative Recommenders for Exporation of Possible Images
-
-Explore the latent space using binary feedback.
-
-[rynmurdock.github.io](https://rynmurdock.github.io/)
+with gr.Blocks(css=css, head=js) as demo:
+    gr.Markdown('''# Generative Recommenders
+    Explore the latent space without text prompts, based on your preferences. [Learn more on the blog](https://rynmurdock.github.io/posts/2024/3/generative_recomenders/)
     ''', elem_id="description")
-    user_id = gr.State()
-    # calibration videos -- this is a misnomer now :D
-    calibrate_prompts = gr.State( glob.glob('image_init/*') )
-    def l():
-        return None
+    embs = gr.State([])
+    ys = gr.State([])
+    calibrate_prompts = gr.State([
+    "4k photo",
+    'surrealist art',
+    # 'a psychedelic, fractal view',
+    'a beautiful collage',
+    'abstract art',
+    'an eldritch image',
+    'a sketch',
+    # 'a city full of darkness and graffiti',
+    '',
+    ])
 
     with gr.Row(elem_id='output-image'):
-        img = gr.Image(
-            label='Lightning',
-            interactive=False,
-            elem_id="output_im",
-            type='filepath',
-            height=700,
-            width=700,
-        )
-    
-    
-    
+        img = gr.Image(interactive=False, elem_id='output-image',width=700)
     with gr.Row(equal_height=True):
-        b3 = gr.Button(value='👎', interactive=False, elem_id="dislike")
-
-        b2 = gr.Button(value='Neither (Space)', interactive=False, elem_id="neither", visible=False)
-
-        b1 = gr.Button(value='👍', interactive=False, elem_id="like")
-    with gr.Row(equal_height=True):
-        b6 = gr.Button(value='👍 Style', interactive=False, elem_id="dislike like", visible=False)
-        
-        b5 = gr.Button(value='👍 Content', interactive=False, elem_id="like dislike", visible=False) 
-        
+        b3 = gr.Button(value='Dislike (A)', interactive=False, elem_id="dislike")
+        b2 = gr.Button(value='Neither (Space)', interactive=False, elem_id="neither")
+        b1 = gr.Button(value='Like (L)', interactive=False, elem_id="like")
         b1.click(
         choose, 
-        [img, b1, calibrate_prompts, user_id],
-        [img, calibrate_prompts, ],
+        [b1, embs, ys, calibrate_prompts],
+        [img, embs, ys, calibrate_prompts]
         )
         b2.click(
         choose, 
-        [img, b2, calibrate_prompts, user_id],
-        [img, calibrate_prompts, ],
+        [b2, embs, ys, calibrate_prompts],
+        [img, embs, ys, calibrate_prompts]
         )
         b3.click(
         choose, 
-        [img, b3, calibrate_prompts, user_id],
-        [img, calibrate_prompts, ],
-        )
-        b5.click(
-        choose, 
-        [img, b5, calibrate_prompts, user_id],
-        [img, calibrate_prompts, ],
-        )
-        b6.click(
-        choose, 
-        [img, b6, calibrate_prompts, user_id],
-        [img, calibrate_prompts, ],
+        [b3, embs, ys, calibrate_prompts],
+        [img, embs, ys, calibrate_prompts]
         )
     with gr.Row():
         b4 = gr.Button(value='Start')
         b4.click(start,
-                 [b4, calibrate_prompts, user_id],
-                 [b1, b2, b3, b4, b5, b6, img, calibrate_prompts, user_id, ]
-                 )
+                 [b4, embs, ys, calibrate_prompts],
+                 [b1, b2, b3, b4, img, embs, ys, calibrate_prompts])
     with gr.Row():
-        html = gr.HTML('''<div style='text-align:center; font-size:20px'>You will calibrate for several images and then roam. </ div><br><br><br>
-
-<br><br>
-<div style='text-align:center; font-size:14px'>Thanks to @multimodalart for their contributions to the demo, esp. the interface and @maxbittker for feedback.
-</ div>''')
-
-# TODO quiet logging
-scheduler = BackgroundScheduler()
-scheduler.add_job(func=background_next_image, trigger="interval", seconds=.2)
-scheduler.start()
-
-# TODO shouldn't call this before gradio launch, yeah?
-@spaces.GPU()
-def encode_space(x):
-    im = (
-            model.prior_pipe.image_processor(x, return_tensors="pt")
-            .pixel_values[0]
-            .unsqueeze(0)
-            .to(dtype=model.prior_pipe.image_encoder.dtype, device=device)
-            )
-    im_emb = model.prior_pipe.image_encoder(im)["image_embeds"]
-    return im_emb.detach().to('cpu').to(torch.float32)
-
-# NOTE:
-# media is moved into a random tmp folder so we need to parse filenames carefully.
-# do not have any cases where a file name is the same or could be `in` another filename
-# you also maybe can't use jpegs lmao
-
-# prep our calibration videos
-m_calibrate = glob.glob('image_init/*')
-for im in m_calibrate:
-    tmp_df = pd.DataFrame(columns=['paths', 'embeddings', 'ips', 'user:rating', 'text', 'gemb', 'from_user_id'])
-    tmp_df['paths'] = [im]
-    image = Image.open(im).convert('RGB')
-    im_emb = encode_space(image)
-    
-    tmp_df['embeddings'] = [im_emb.detach().to('cpu')]
-    tmp_df['user:rating'] = [{' ': ' '}]
-    tmp_df['text'] = ['']
-
-    # seems to break things...
-    tmp_df['from_user_id'] = [0]
-    tmp_df['latest_user_to_rate'] = [0]
-    prevs_df = pd.concat((prevs_df, tmp_df))
+        html = gr.HTML('''<div style='text-align:center; font-size:20px'>You will calibrate for several prompts and then roam.</ div>''')
 
-glob_idx = 0
-demo.launch(share=True,)
+demo.launch()  # Share your demo with just 1 extra parameter 🚀

config.py

@@ -1,16 +0,0 @@
-import torch
-
-# NOTE model path name changed
-model_path = './last_epoch_ckpt/'
-lr = 1e-5
-device = 'cuda'
-dtype = torch.bfloat16
-data_path = '../data/lke_2017'
-save_path = './'
-epochs = 4
-batch_size = 16
-number_k_clip_embed = 16 # divide by this to determine bundling together of sequences -> CLIP
-num_workers = 32
-seed = 107
-
-# TODO config option to swap to diffusion?

data.py

@@ -1,147 +0,0 @@
-import torch
-from PIL import Image
-import random
-import logging
-import torchvision
-
-import torchvision.transforms as T
-from torchvision.transforms.functional import InterpolationMode
-
-IMAGENET_MEAN = (0.485, 0.456, 0.406)
-IMAGENET_STD = (0.229, 0.224, 0.225)
-
-def build_transform(input_size):
-    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
-    transform = T.Compose([
-        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
-        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
-        T.ToTensor(),
-        T.Normalize(mean=MEAN, std=STD)
-    ])
-    return transform
-
-def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
-    best_ratio_diff = float('inf')
-    best_ratio = (1, 1)
-    area = width * height
-    for ratio in target_ratios:
-        target_aspect_ratio = ratio[0] / ratio[1]
-        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
-        if ratio_diff < best_ratio_diff:
-            best_ratio_diff = ratio_diff
-            best_ratio = ratio
-        elif ratio_diff == best_ratio_diff:
-            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
-                best_ratio = ratio
-    return best_ratio
-
-def dynamic_preprocess(image, min_num=1, max_num=8, image_size=448, use_thumbnail=False):
-    orig_width, orig_height = image.size
-    aspect_ratio = orig_width / orig_height
-
-    # calculate the existing image aspect ratio
-    target_ratios = set(
-        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
-        i * j <= max_num and i * j >= min_num)
-    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
-
-    # find the closest aspect ratio to the target
-    target_aspect_ratio = find_closest_aspect_ratio(
-        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
-
-    # calculate the target width and height
-    target_width = image_size * target_aspect_ratio[0]
-    target_height = image_size * target_aspect_ratio[1]
-    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
-
-    # resize the image
-    resized_img = image.resize((target_width, target_height))
-    processed_images = []
-    for i in range(blocks):
-        box = (
-            (i % (target_width // image_size)) * image_size,
-            (i // (target_width // image_size)) * image_size,
-            ((i % (target_width // image_size)) + 1) * image_size,
-            ((i // (target_width // image_size)) + 1) * image_size
-        )
-        # split the image
-        split_img = resized_img.crop(box)
-        processed_images.append(split_img)
-    assert len(processed_images) == blocks
-    if use_thumbnail and len(processed_images) != 1:
-        thumbnail_img = image.resize((image_size, image_size))
-        processed_images.append(thumbnail_img)
-    return processed_images
-
-
-def load_image(image_file, pil_image=None, input_size=224,):
-    if not pil_image:
-        pil_image = Image.open(image_file)
-    image = pil_image.convert('RGB')
-    transform = build_transform(input_size=input_size)
-    # images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
-    pixel_values = [transform(image) for image in [image]]
-    pixel_values = torch.stack(pixel_values)
-    return pixel_values
-
-def my_collate(batch):
-    try:
-        targets = torch.stack([s['target'] for s in batch])
-        samples = torch.stack([s['samples'] for s in batch])
-
-        # targets = torch.stack([s['target'] for s in batch if s is not None])
-        # samples = torch.stack([s['samples'] for s in batch if s is not None])
-    except Exception as e:
-      logging.warning('my_collate issue ', e)
-      return None
-    return samples, targets
-
-
-class ImageFolderSample(torchvision.datasets.ImageFolder):
-    def __init__(self, data_path, k, processor):
-        super().__init__(data_path)
-        self.k = k
-        self.processor = processor
-
-    def safe_getitem(self, index):
-        try:
-            target_path, class_type = self.samples[index]
-            target = torch.from_numpy(self.processor(self.loader(target_path)).data['pixel_values'][0])
-
-            input_paths = random.choices([p[0] for p in self.samples if p != target_path and class_type in p], k=self.k)
-            assert len(input_paths) == self.k # I think it may do this by default...
-            samples = torch.stack([torch.from_numpy(self.processor(self.loader(i)).data['pixel_values'][0]) for i in input_paths])
-        except Exception as e:
-            logging.warning('getitem issue ', e)
-            samples, target = None, None
-
-        drop_mask = torch.rand(samples.shape[0],) < .2
-        samples[drop_mask] = 0
-
-        drop_whole_set_mask = torch.rand(1,) < .1
-        if drop_whole_set_mask:
-            samples = torch.zeros_like(samples)
-        return {'samples': samples[:, :3], 'target': target[:3]}
-
-    def __getitem__(self, index: int):
-        return self.safe_getitem(index)
-
-
-# https://data.mendeley.com/datasets/fs4k2zc5j5/3
-# Gomez, J. C., Ibarra-Manzano, M. A., & Almanza-Ojeda, D. L. (2017). User Identification in Pinterest Through the Refinement of Cascade Fusion of Text and Images. Research in Computing Science, 144, 41-52.
-def get_dataset(data_path, processor):
-    return ImageFolderSample(data_path, 8, processor)
-
-
-def get_dataloader(data_path, batch_size, num_workers, processor):
-    dataloader = torch.utils.data.DataLoader(
-                                            get_dataset(data_path, processor=processor), 
-                                            num_workers=num_workers, 
-                                            collate_fn=my_collate, 
-                                            batch_size=batch_size, 
-                                            shuffle=True, 
-                                            drop_last=True
-                                            )
-    return dataloader
-
-

last_epoch_ckpt/config.json

@@ -1,18 +0,0 @@
-{
-  "_class_name": "PriorTransformer",
-  "_diffusers_version": "0.34.0.dev0",
-  "_name_or_path": "./last_epoch_ckpt/",
-  "added_emb_type": "prd",
-  "additional_embeddings": 3,
-  "attention_head_dim": 32,
-  "clip_embed_dim": null,
-  "dropout": 0.0,
-  "embedding_dim": 1280,
-  "embedding_proj_dim": null,
-  "embedding_proj_norm_type": null,
-  "encoder_hid_proj_type": "linear",
-  "norm_in_type": null,
-  "num_attention_heads": 16,
-  "num_embeddings": 77,
-  "num_layers": 10
-}

last_epoch_ckpt/diffusion_pytorch_model.safetensors

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:91ca25967a5dd1665b5fb8f1b4a45ba0f7ad0c23929daefb26b267816616e05d
-size 136790920

model.py

@@ -1,52 +0,0 @@
-
-import torch
-import logging
-from diffusers import DiffusionPipeline
-
-from prior.pipeline_kandinsky_prior import KandinskyPriorPipeline
-from prior.prior_transformer import PriorTransformer
-
-
-class Zoo(torch.nn.Module):
-    def __init__(self, prior, prior_pipe, kandinsky_pipe, ) -> None:
-        super().__init__()
-        self.prior = prior
-        self.prior_pipe = prior_pipe
-        self.kandinsky_pipe = kandinsky_pipe
-        self.pre_prior_transformer = None 
-        # NOTE we may get better perf from freezing our prior 
-        #     and only training a transformer adapter?
-
-    def forward(self, latents, preferred_embeds):
-        pred = self.prior(latents, preferred_embeds)
-        return pred
-    
-    def do_validation(self, images): # TODO constant val seed
-        assert all([len(i) == 8 for i in images]), f'We have must have `k` images, not {len(images)}.'
-        image_embeds, negative_image_embeds = self.prior_pipe(images).to_tuple()
-        images = self.kandinsky_pipe(
-            num_inference_steps=50,
-            image_embeds=image_embeds,
-            negative_image_embeds=negative_image_embeds,
-        ).images
-        images[0].save('latest_val.png')
-        return images
-
-def get_model_and_tokenizer(path, device, dtype):
-    prior = PriorTransformer.from_pretrained("ECLIPSE-Community/ECLIPSE_KandinskyV22_Prior" 
-                                             if path is None else path).to(device)
-        
-    pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior", prior=prior).to(device)
-    pipe_prior.image_encoder = pipe_prior.image_encoder.to(device, dtype)
-    # Note: don't set the prior to `dtype`` as it may be half precision, 
-    #     and we're training with mixed precision
-    #     so we need to keep our full-precision weight for trained params
-    kandinsky_pipe = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder").to(device, dtype)
-    model = Zoo(prior, pipe_prior, kandinsky_pipe).to(device)
-
-    return model, model.prior_pipe.image_encoder
-
-def get_optimizer(params, lr):
-    logging.info(f'Training: {params}')
-    optimizer = torch.optim.AdamW(params, lr=lr)
-    return optimizer

patch_sdxl.py

@@ -0,0 +1,559 @@
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Union, Tuple
+
+from diffusers import StableDiffusionXLPipeline
+
+import torch
+from packaging import version
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
+
+from diffusers.configuration_utils import FrozenDict
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
+from diffusers.models.attention_processor import FusedAttnProcessor2_0
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    deprecate,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
+
+
+
+from transformers import CLIPFeatureExtractor
+import numpy as np
+import torch
+from PIL import Image 
+from typing import Optional, Tuple, Union
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+torch_device = device
+torch_dtype = torch.float16
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import StableDiffusionPipeline
+
+        >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+        >>> pipe = pipe.to("cuda")
+
+        >>> prompt = "a photo of an astronaut riding a horse on mars"
+        >>> image = pipe(prompt).images[0]
+        ```
+"""
+
+
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+    """
+    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+    """
+    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+    # rescale the results from guidance (fixes overexposure)
+    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+    return noise_cfg
+
+
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used,
+            `timesteps` must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+                Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
+                timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
+                must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class SDEmb(StableDiffusionXLPipeline):
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        denoising_end: Optional[float] = None,
+        guidance_scale: float = 5.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt_2: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+        original_size: Optional[Tuple[int, int]] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        target_size: Optional[Tuple[int, int]] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        ip_adapter_emb=None,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                used in both text-encoders
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+                Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+                Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            denoising_end (`float`, *optional*):
+                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
+                completed before it is intentionally prematurely terminated. As a result, the returned sample will
+                still retain a substantial amount of noise as determined by the discrete timesteps selected by the
+                scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
+                "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
+                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
+            guidance_scale (`float`, *optional*, defaults to 5.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+                input argument.
+            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+                of a plain tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            guidance_rescale (`float`, *optional*, defaults to 0.0):
+                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
+                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
+                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+                Guidance rescale factor should fix overexposure when using zero terminal SNR.
+            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+                `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
+                explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                For most cases, `target_size` should be set to the desired height and width of the generated image. If
+                not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
+                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a target image resolution. It should be as same
+                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        if callback is not None:
+            deprecate(
+                "callback",
+                "1.0.0",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+        if callback_steps is not None:
+            deprecate(
+                "callback_steps",
+                "1.0.0",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+
+        # 0. Default height and width to unet
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        original_size = original_size or (height, width)
+        target_size = target_size or (height, width)
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            callback_steps,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._guidance_rescale = guidance_rescale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        self._denoising_end = denoising_end
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        # 3. Encode input prompt
+        lora_scale = (
+            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+        )
+
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            negative_prompt_2=negative_prompt_2,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            lora_scale=lora_scale,
+            clip_skip=self.clip_skip,
+        )
+
+        # 4. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7. Prepare added time ids & embeddings
+        add_text_embeds = pooled_prompt_embeds
+        if self.text_encoder_2 is None:
+            text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
+        else:
+            text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
+
+        add_time_ids = self._get_add_time_ids(
+            original_size,
+            crops_coords_top_left,
+            target_size,
+            dtype=prompt_embeds.dtype,
+            text_encoder_projection_dim=text_encoder_projection_dim,
+        )
+        if negative_original_size is not None and negative_target_size is not None:
+            negative_add_time_ids = self._get_add_time_ids(
+                negative_original_size,
+                negative_crops_coords_top_left,
+                negative_target_size,
+                dtype=prompt_embeds.dtype,
+                text_encoder_projection_dim=text_encoder_projection_dim,
+            )
+        else:
+            negative_add_time_ids = add_time_ids
+
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
+            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
+
+        prompt_embeds = prompt_embeds.to(device)
+        add_text_embeds = add_text_embeds.to(device)
+        add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
+
+        if ip_adapter_emb is not None:
+            image_embeds = ip_adapter_emb
+
+        elif ip_adapter_image is not None:
+            output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
+            image_embeds, negative_image_embeds = self.encode_image(
+                ip_adapter_image, device, num_images_per_prompt, output_hidden_state
+            )
+            if self.do_classifier_free_guidance:
+                image_embeds = torch.cat([negative_image_embeds, image_embeds])
+
+        # 8. Denoising loop
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+        # 8.1 Apply denoising_end
+        if (
+            self.denoising_end is not None
+            and isinstance(self.denoising_end, float)
+            and self.denoising_end > 0
+            and self.denoising_end < 1
+        ):
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (self.denoising_end * self.scheduler.config.num_train_timesteps)
+                )
+            )
+            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
+            timesteps = timesteps[:num_inference_steps]
+
+        # 9. Optionally get Guidance Scale Embedding
+        timestep_cond = None
+        if self.unet.config.time_cond_proj_dim is not None:
+            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
+            timestep_cond = self.get_guidance_scale_embedding(
+                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+            ).to(device=device, dtype=latents.dtype)
+
+        self._num_timesteps = len(timesteps)
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # predict the noise residual
+                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+                if ip_adapter_image is not None or ip_adapter_emb is not None:
+                    added_cond_kwargs["image_embeds"] = image_embeds
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep_cond=timestep_cond,
+                    cross_attention_kwargs=self.cross_attention_kwargs,
+                    added_cond_kwargs=added_cond_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
+                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+                    add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
+                    negative_pooled_prompt_embeds = callback_outputs.pop(
+                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+                    )
+                    add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+                    negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
+
+                # if XLA_AVAILABLE:
+                #     xm.mark_step()
+
+        if not output_type == "latent":
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+
+            if needs_upcasting:
+                self.upcast_vae()
+                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+
+            # cast back to fp16 if needed
+            if needs_upcasting:
+                self.vae.to(dtype=torch.float16)
+        else:
+            image = latents
+
+        if not output_type == "latent":
+            # apply watermark if available
+            if self.watermark is not None:
+                image = self.watermark.apply_watermark(image)
+            image = self.image_processor.postprocess(image, output_type=output_type)
+        #maybe_nsfw = any(check_nsfw_images(image))
+        #if maybe_nsfw:
+        #    print('This image could be NSFW so we return a blank image.')
+        #    return StableDiffusionXLPipelineOutput(images=[Image.new('RGB', (1024, 1024))])
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return StableDiffusionXLPipelineOutput(images=image)

prior/pipeline_kandinsky_prior.py

@@ -1,528 +0,0 @@
-from dataclasses import dataclass
-from typing import List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from transformers import (
-    CLIPImageProcessor,
-    CLIPTextModelWithProjection,
-    CLIPTokenizer,
-    CLIPVisionModelWithProjection,
-)
-
-from diffusers.models import PriorTransformer
-from diffusers.schedulers import UnCLIPScheduler
-from diffusers.utils import (
-    BaseOutput,
-    is_accelerate_available,
-    is_accelerate_version,
-    logging,
-    replace_example_docstring,
-)
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
-        >>> import torch
-
-        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior")
-        >>> pipe_prior.to("cuda")
-
-        >>> prompt = "red cat, 4k photo"
-        >>> out = pipe_prior(prompt)
-        >>> image_emb = out.image_embeds
-        >>> negative_image_emb = out.negative_image_embeds
-
-        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
-        >>> pipe.to("cuda")
-
-        >>> image = pipe(
-        ...     prompt,
-        ...     image_embeds=image_emb,
-        ...     negative_image_embeds=negative_image_emb,
-        ...     height=768,
-        ...     width=768,
-        ...     num_inference_steps=100,
-        ... ).images
-
-        >>> image[0].save("cat.png")
-        ```
-"""
-
-EXAMPLE_INTERPOLATE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> from diffusers import KandinskyPriorPipeline, KandinskyPipeline
-        >>> from diffusers.utils import load_image
-        >>> import PIL
-
-        >>> import torch
-        >>> from torchvision import transforms
-
-        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(
-        ...     "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
-        ... )
-        >>> pipe_prior.to("cuda")
-
-        >>> img1 = load_image(
-        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-        ...     "/kandinsky/cat.png"
-        ... )
-
-        >>> img2 = load_image(
-        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-        ...     "/kandinsky/starry_night.jpeg"
-        ... )
-
-        >>> images_texts = ["a cat", img1, img2]
-        >>> weights = [0.3, 0.3, 0.4]
-        >>> image_emb, zero_image_emb = pipe_prior.interpolate(images_texts, weights)
-
-        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
-        >>> pipe.to("cuda")
-
-        >>> image = pipe(
-        ...     "",
-        ...     image_embeds=image_emb,
-        ...     negative_image_embeds=zero_image_emb,
-        ...     height=768,
-        ...     width=768,
-        ...     num_inference_steps=150,
-        ... ).images[0]
-
-        >>> image.save("starry_cat.png")
-        ```
-"""
-
-
-@dataclass
-class KandinskyPriorPipelineOutput(BaseOutput):
-    """
-    Output class for KandinskyPriorPipeline.
-
-    Args:
-        image_embeds (`torch.FloatTensor`)
-            clip image embeddings for text prompt
-        negative_image_embeds (`List[PIL.Image.Image]` or `np.ndarray`)
-            clip image embeddings for unconditional tokens
-    """
-
-    image_embeds: Union[torch.FloatTensor, np.ndarray]
-    negative_image_embeds: Union[torch.FloatTensor, np.ndarray]
-
-
-class KandinskyPriorPipeline(DiffusionPipeline):
-    """
-    Pipeline for generating image prior for Kandinsky
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        prior ([`PriorTransformer`]):
-            The canonincal unCLIP prior to approximate the image embedding from the text embedding.
-        image_encoder ([`CLIPVisionModelWithProjection`]):
-            Frozen image-encoder.
-        text_encoder ([`CLIPTextModelWithProjection`]):
-            Frozen text-encoder.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        scheduler ([`UnCLIPScheduler`]):
-            A scheduler to be used in combination with `prior` to generate image embedding.
-    """
-
-    _exclude_from_cpu_offload = ["prior"]
-
-    def __init__(
-        self,
-        prior: PriorTransformer,
-        image_encoder: CLIPVisionModelWithProjection,
-        text_encoder: CLIPTextModelWithProjection,
-        tokenizer: CLIPTokenizer,
-        scheduler: UnCLIPScheduler,
-        image_processor: CLIPImageProcessor,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            prior=prior,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            scheduler=scheduler,
-            image_encoder=image_encoder,
-            image_processor=image_processor,
-        )
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_INTERPOLATE_DOC_STRING)
-    def interpolate(
-        self,
-        images_and_prompts: List[Union[str, PIL.Image.Image, torch.FloatTensor]],
-        weights: List[float],
-        num_images_per_prompt: int = 1,
-        num_inference_steps: int = 25,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        negative_prior_prompt: Optional[str] = None,
-        negative_prompt: str = "",
-        guidance_scale: float = 4.0,
-        device=None,
-    ):
-        """
-        Function invoked when using the prior pipeline for interpolation.
-
-        Args:
-            images_and_prompts (`List[Union[str, PIL.Image.Image, torch.FloatTensor]]`):
-                list of prompts and images to guide the image generation.
-            weights: (`List[float]`):
-                list of weights for each condition in `images_and_prompts`
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            num_inference_steps (`int`, *optional*, defaults to 25):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            negative_prior_prompt (`str`, *optional*):
-                The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored if
-                `guidance_scale` is less than `1`).
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt not to guide the image generation. Ignored when not using guidance (i.e., ignored if
-                `guidance_scale` is less than `1`).
-            guidance_scale (`float`, *optional*, defaults to 4.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-
-        Examples:
-
-        Returns:
-            [`KandinskyPriorPipelineOutput`] or `tuple`
-        """
-
-        device = device or self.device
-
-        if len(images_and_prompts) != len(weights):
-            raise ValueError(
-                f"`images_and_prompts` contains {len(images_and_prompts)} items and `weights` contains {len(weights)} items - they should be lists of same length"
-            )
-
-        image_embeddings = []
-        for cond, weight in zip(images_and_prompts, weights):
-            if isinstance(cond, str):
-                image_emb = self(
-                    cond,
-                    num_inference_steps=num_inference_steps,
-                    num_images_per_prompt=num_images_per_prompt,
-                    generator=generator,
-                    latents=latents,
-                    negative_prompt=negative_prior_prompt,
-                    guidance_scale=guidance_scale,
-                ).image_embeds
-
-            elif isinstance(cond, (PIL.Image.Image, torch.Tensor)):
-                if isinstance(cond, PIL.Image.Image):
-                    cond = (
-                        self.image_processor(cond, return_tensors="pt")
-                        .pixel_values[0]
-                        .unsqueeze(0)
-                        .to(dtype=self.image_encoder.dtype, device=device)
-                    )
-
-                image_emb = self.image_encoder(cond)["image_embeds"]
-
-            else:
-                raise ValueError(
-                    f"`images_and_prompts` can only contains elements to be of type `str`, `PIL.Image.Image` or `torch.Tensor`  but is {type(cond)}"
-                )
-
-            image_embeddings.append(image_emb * weight)
-
-        image_emb = torch.cat(image_embeddings).sum(dim=0, keepdim=True)
-
-        out_zero = self(
-            negative_prompt,
-            num_inference_steps=num_inference_steps,
-            num_images_per_prompt=num_images_per_prompt,
-            generator=generator,
-            latents=latents,
-            negative_prompt=negative_prior_prompt,
-            guidance_scale=guidance_scale,
-        )
-        zero_image_emb = (
-            out_zero.negative_image_embeds
-            if negative_prompt == ""
-            else out_zero.image_embeds
-        )
-
-        return KandinskyPriorPipelineOutput(
-            image_embeds=image_emb, negative_image_embeds=zero_image_emb
-        )
-
-    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
-    def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
-        if latents is None:
-            latents = torch.randn(
-                shape, generator=generator, device=device, dtype=dtype
-            )
-        else:
-            if latents.shape != shape:
-                raise ValueError(
-                    f"Unexpected latents shape, got {latents.shape}, expected {shape}"
-                )
-            latents = latents.to(device)
-
-        latents = latents * scheduler.init_noise_sigma
-        return latents
-
-    def get_zero_embed(self, batch_size=1, device=None):
-        device = device or self.device
-        zero_img = torch.zeros(
-            1,
-            3,
-            self.image_encoder.config.image_size,
-            self.image_encoder.config.image_size,
-        ).to(device=device, dtype=self.image_encoder.dtype)
-        zero_image_emb = self.image_encoder(zero_img)["image_embeds"]
-        zero_image_emb = zero_image_emb.repeat(batch_size, 1)
-        return zero_image_emb
-
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-    ):
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-        # get prompt text embeddings
-        cond = (
-                self.image_processor(prompt, return_tensors="pt")
-                .pixel_values[0]
-                .unsqueeze(0)
-                .to(dtype=self.image_encoder.dtype, device=device)
-                )
-        prompt_embeds = self.image_encoder(cond)["image_embeds"]
-
-        prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-
-        if do_classifier_free_guidance:
-            if negative_prompt is None:
-                uncond_tokens = self.get_zero_embed(batch_size=prompt_embeds.shape[0])
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            cond = (
-                    self.image_processor(uncond_tokens, return_tensors="pt")
-                    .pixel_values[0]
-                    .unsqueeze(0)
-                    .to(dtype=self.image_encoder.dtype, device=device)
-                    )
-
-            negative_prompt_embeds = self.image_encoder(cond)["image_embeds"]
-
-            seq_len = negative_prompt_embeds.shape[1]
-            negative_prompt_embeds = negative_prompt_embeds.repeat(
-                1, num_images_per_prompt
-            )
-            negative_prompt_embeds = negative_prompt_embeds.view(
-                batch_size * num_images_per_prompt, seq_len
-            )
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-        return prompt_embeds, None
-
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
-        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
-        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
-        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError(
-                "`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."
-            )
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        hook = None
-        for cpu_offloaded_model in [self.text_encoder, self.prior]:
-            _, hook = cpu_offload_with_hook(
-                cpu_offloaded_model, device, prev_module_hook=hook
-            )
-
-        # We'll offload the last model manually.
-        self.prior_hook = hook
-
-        _, hook = cpu_offload_with_hook(
-            self.image_encoder, device, prev_module_hook=self.prior_hook
-        )
-
-        self.final_offload_hook = hook
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: int = 1,
-        num_inference_steps: int = 25,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        guidance_scale: float = 4.0,
-        output_type: Optional[str] = "pt",
-        return_dict: bool = True,
-    ):
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            num_inference_steps (`int`, *optional*, defaults to 25):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            guidance_scale (`float`, *optional*, defaults to 4.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            output_type (`str`, *optional*, defaults to `"pt"`):
-                The output format of the generate image. Choose between: `"np"` (`np.array`) or `"pt"`
-                (`torch.Tensor`).
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
-
-        Examples:
-
-        Returns:
-            [`KandinskyPriorPipelineOutput`] or `tuple`
-        """
-
-        # if the negative prompt is defined we double the batch size to
-        # directly retrieve the negative prompt embedding
-        if negative_prompt is not None:
-            prompt = prompt + negative_prompt
-            negative_prompt = 2 * negative_prompt
-
-        device = self._execution_device
-
-        batch_size = len(prompt)
-        batch_size = batch_size * num_images_per_prompt
-
-        full_prompt = []
-        for b in prompt: # TODO of course vectorize this lol
-            full_seq = []
-            for p in b:
-                prompt_embeds, text_mask = self._encode_prompt(
-                    p, device, num_images_per_prompt, False, negative_prompt
-                )
-                full_seq.append(prompt_embeds)
-                prompt_embeds = torch.cat(full_seq, 0)
-            full_prompt.append(prompt_embeds)
-        prompt_embeds = torch.stack(full_prompt)
-        if prompt_embeds.shape[1] < 8: # TODO grab as `k` arg from config
-            prompt_embeds = torch.nn.functional.pad(prompt_embeds, [0, 0, 0, 8-prompt_embeds.shape[1]])
-        assert prompt_embeds.shape[1] == 8, f"The model is set to take `k`` cond image embeds but is shape {prompt_embeds.shape}"
-
-        prompt_embeds = prompt_embeds.to('cuda') # TODO set with `k` arg from config
-
-        hidden_states = torch.randn(
-            (batch_size, prompt_embeds.shape[-1]),
-            device=prompt_embeds.device,
-            dtype=prompt_embeds.dtype,
-            generator=generator,
-        )
-
-        latents = self.prior(
-            hidden_states,
-            proj_embedding=prompt_embeds,
-            encoder_hidden_states=prompt_embeds,
-            attention_mask=text_mask,
-        ).predicted_image_embedding
-
-        image_embeddings = latents
-
-        # if negative prompt has been defined, we retrieve split the image embedding into two
-        if negative_prompt is None:
-            zero_embeds = self.get_zero_embed(latents.shape[0], device=latents.device)
-
-            if (
-                hasattr(self, "final_offload_hook")
-                and self.final_offload_hook is not None
-            ):
-                self.final_offload_hook.offload()
-        else:
-            image_embeddings, zero_embeds = image_embeddings.chunk(2)
-
-            if (
-                hasattr(self, "final_offload_hook")
-                and self.final_offload_hook is not None
-            ):
-                self.prior_hook.offload()
-
-        if output_type not in ["pt", "np"]:
-            raise ValueError(
-                f"Only the output types `pt` and `np` are supported not output_type={output_type}"
-            )
-
-        if output_type == "np":
-            image_embeddings = image_embeddings.cpu().numpy()
-            zero_embeds = zero_embeds.cpu().numpy()
-
-        if not return_dict:
-            return (image_embeddings, zero_embeds)
-
-        return KandinskyPriorPipelineOutput(
-            image_embeds=image_embeddings, negative_image_embeds=zero_embeds
-        )

prior/prior_transformer.py

@@ -1,369 +0,0 @@
-import sys
-sys.path.append("..")
-
-from dataclasses import dataclass
-from typing import Dict, Optional, Union
-
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.utils import BaseOutput
-from diffusers.models.attention import BasicTransformerBlock
-from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor
-from diffusers.models.embeddings import TimestepEmbedding, Timesteps
-from diffusers.models.modeling_utils import ModelMixin
-
-
-@dataclass
-class PriorTransformerOutput(BaseOutput):
-    """
-    The output of [`PriorTransformer`].
-
-    Args:
-        predicted_image_embedding (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`):
-            The predicted CLIP image embedding conditioned on the CLIP text embedding input.
-    """
-
-    predicted_image_embedding: torch.FloatTensor
-
-
-class PriorTransformer(ModelMixin, ConfigMixin):
-    """
-    A Prior Transformer model.
-
-    Parameters:
-        num_attention_heads (`int`, *optional*, defaults to 32): The number of heads to use for multi-head attention.
-        attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
-        num_layers (`int`, *optional*, defaults to 20): The number of layers of Transformer blocks to use.
-        embedding_dim (`int`, *optional*, defaults to 768): The dimension of the model input `hidden_states`
-        num_embeddings (`int`, *optional*, defaults to 77):
-            The number of embeddings of the model input `hidden_states`
-        additional_embeddings (`int`, *optional*, defaults to 4): The number of additional tokens appended to the
-            projected `hidden_states`. The actual length of the used `hidden_states` is `num_embeddings +
-            additional_embeddings`.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        time_embed_act_fn (`str`, *optional*, defaults to 'silu'):
-            The activation function to use to create timestep embeddings.
-        norm_in_type (`str`, *optional*, defaults to None): The normalization layer to apply on hidden states before
-            passing to Transformer blocks. Set it to `None` if normalization is not needed.
-        embedding_proj_norm_type (`str`, *optional*, defaults to None):
-            The normalization layer to apply on the input `proj_embedding`. Set it to `None` if normalization is not
-            needed.
-        encoder_hid_proj_type (`str`, *optional*, defaults to `linear`):
-            The projection layer to apply on the input `encoder_hidden_states`. Set it to `None` if
-            `encoder_hidden_states` is `None`.
-        added_emb_type (`str`, *optional*, defaults to `prd`): Additional embeddings to condition the model.
-            Choose from `prd` or `None`. if choose `prd`, it will prepend a token indicating the (quantized) dot
-            product between the text embedding and image embedding as proposed in the unclip paper
-            https://arxiv.org/abs/2204.06125 If it is `None`, no additional embeddings will be prepended.
-        time_embed_dim (`int, *optional*, defaults to None): The dimension of timestep embeddings.
-            If None, will be set to `num_attention_heads * attention_head_dim`
-        embedding_proj_dim (`int`, *optional*, default to None):
-            The dimension of `proj_embedding`. If None, will be set to `embedding_dim`.
-        clip_embed_dim (`int`, *optional*, default to None):
-            The dimension of the output. If None, will be set to `embedding_dim`.
-    """
-
-    @register_to_config
-    def __init__(
-        self,
-        num_attention_heads: int = 32,
-        attention_head_dim: int = 64,
-        num_layers: int = 20,
-        embedding_dim: int = 768,
-        num_embeddings=77,
-        additional_embeddings=3,  # as we have remvoed the time embedding
-        dropout: float = 0.0,
-        # time_embed_act_fn: str = "silu",
-        norm_in_type: Optional[str] = None,  # layer
-        embedding_proj_norm_type: Optional[str] = None,  # layer
-        encoder_hid_proj_type: Optional[str] = "linear",  # linear
-        added_emb_type: Optional[str] = "prd",  # prd
-        # time_embed_dim: Optional[int] = None,
-        embedding_proj_dim: Optional[int] = None,
-        clip_embed_dim: Optional[int] = None,
-    ):
-        super().__init__()
-        self.num_attention_heads = num_attention_heads
-        self.attention_head_dim = attention_head_dim
-        inner_dim = num_attention_heads * attention_head_dim
-        self.additional_embeddings = additional_embeddings
-
-        # time_embed_dim = time_embed_dim or inner_dim
-        embedding_proj_dim = embedding_proj_dim or embedding_dim
-        clip_embed_dim = clip_embed_dim or embedding_dim
-
-        # self.time_proj = Timesteps(inner_dim, True, 0)
-        # self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, out_dim=inner_dim, act_fn=time_embed_act_fn)
-
-        self.proj_in = nn.Linear(embedding_dim, inner_dim)
-
-        if embedding_proj_norm_type is None:
-            self.embedding_proj_norm = None
-        elif embedding_proj_norm_type == "layer":
-            self.embedding_proj_norm = nn.LayerNorm(embedding_proj_dim)
-        else:
-            raise ValueError(f"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}")
-
-        self.embedding_proj = nn.Linear(embedding_proj_dim, inner_dim)
-
-        if encoder_hid_proj_type is None:
-            self.encoder_hidden_states_proj = None
-        elif encoder_hid_proj_type == "linear":
-            self.encoder_hidden_states_proj = nn.Linear(embedding_dim, inner_dim)
-        else:
-            raise ValueError(f"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}")
-
-        self.positional_embedding = nn.Parameter(torch.zeros(1, num_embeddings + additional_embeddings, inner_dim))
-
-        if added_emb_type == "prd":
-            self.prd_embedding = nn.Parameter(torch.zeros(1, 1, inner_dim))
-        elif added_emb_type is None:
-            self.prd_embedding = None
-        else:
-            raise ValueError(
-                f"`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."
-            )
-
-        self.transformer_blocks = nn.ModuleList(
-            [
-                BasicTransformerBlock(
-                    inner_dim,
-                    num_attention_heads,
-                    attention_head_dim,
-                    dropout=dropout,
-                    activation_fn="gelu",
-                    attention_bias=True,
-                )
-                for d in range(num_layers)
-            ]
-        )
-
-        if norm_in_type == "layer":
-            self.norm_in = nn.LayerNorm(inner_dim)
-        elif norm_in_type is None:
-            self.norm_in = None
-        else:
-            raise ValueError(f"Unsupported norm_in_type: {norm_in_type}.")
-
-        self.norm_out = nn.LayerNorm(inner_dim)
-
-        self.proj_to_clip_embeddings = nn.Linear(inner_dim, clip_embed_dim)
-
-        causal_attention_mask = torch.full(
-            [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings], -10000.0
-        )
-        causal_attention_mask.triu_(1)
-        causal_attention_mask = causal_attention_mask[None, ...]
-        self.register_buffer("causal_attention_mask", causal_attention_mask, persistent=False)
-
-        self.clip_mean = nn.Parameter(torch.zeros(1, clip_embed_dim))
-        self.clip_std = nn.Parameter(torch.zeros(1, clip_embed_dim))
-
-    @property
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
-    def attn_processors(self) -> Dict[str, AttentionProcessor]:
-        r"""
-        Returns:
-            `dict` of attention processors: A dictionary containing all attention processors used in the model with
-            indexed by its weight name.
-        """
-        # set recursively
-        processors = {}
-
-        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
-            if hasattr(module, "set_processor"):
-                processors[f"{name}.processor"] = module.processor
-
-            for sub_name, child in module.named_children():
-                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
-
-            return processors
-
-        for name, module in self.named_children():
-            fn_recursive_add_processors(name, module, processors)
-
-        return processors
-
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
-    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
-        r"""
-        Sets the attention processor to use to compute attention.
-
-        Parameters:
-            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
-                The instantiated processor class or a dictionary of processor classes that will be set as the processor
-                for **all** `Attention` layers.
-
-                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
-                processor. This is strongly recommended when setting trainable attention processors.
-
-        """
-        count = len(self.attn_processors.keys())
-
-        if isinstance(processor, dict) and len(processor) != count:
-            raise ValueError(
-                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
-                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
-            )
-
-        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
-            if hasattr(module, "set_processor"):
-                if not isinstance(processor, dict):
-                    module.set_processor(processor)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"))
-
-            for sub_name, child in module.named_children():
-                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
-
-        for name, module in self.named_children():
-            fn_recursive_attn_processor(name, module, processor)
-
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
-    def set_default_attn_processor(self):
-        """
-        Disables custom attention processors and sets the default attention implementation.
-        """
-        self.set_attn_processor(AttnProcessor())
-
-    def forward(
-        self,
-        hidden_states,
-        # timestep: Union[torch.Tensor, float, int],
-        proj_embedding: torch.FloatTensor,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.BoolTensor] = None,
-        return_dict: bool = True,
-    ):
-        """
-        The [`PriorTransformer`] forward method.
-
-        Args:
-            hidden_states (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`):
-                The currently predicted image embeddings.
-            timestep (`torch.LongTensor`):
-                Current denoising step.
-            proj_embedding (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`):
-                Projected embedding vector the denoising process is conditioned on.
-            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, num_embeddings, embedding_dim)`):
-                Hidden states of the text embeddings the denoising process is conditioned on.
-            attention_mask (`torch.BoolTensor` of shape `(batch_size, num_embeddings)`):
-                Text mask for the text embeddings.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.prior_transformer.PriorTransformerOutput`] instead of a plain
-                tuple.
-
-        Returns:
-            [`~models.prior_transformer.PriorTransformerOutput`] or `tuple`:
-                If return_dict is True, a [`~models.prior_transformer.PriorTransformerOutput`] is returned, otherwise a
-                tuple is returned where the first element is the sample tensor.
-        """
-        batch_size = hidden_states.shape[0]
-
-        # timesteps = timestep
-        # if not torch.is_tensor(timesteps):
-        #     timesteps = torch.tensor([timesteps], dtype=torch.long, device=hidden_states.device)
-        # elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
-        #     timesteps = timesteps[None].to(hidden_states.device)
-
-        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-        # timesteps = timesteps * torch.ones(batch_size, dtype=timesteps.dtype, device=timesteps.device)
-
-        # timesteps_projected = self.time_proj(timesteps)
-
-        # timesteps does not contain any weights and will always return f32 tensors
-        # but time_embedding might be fp16, so we need to cast here.
-        # timesteps_projected = timesteps_projected.to(dtype=self.dtype)
-        # time_embeddings = self.time_embedding(timesteps_projected)
-
-        if self.embedding_proj_norm is not None:
-            proj_embedding = self.embedding_proj_norm(proj_embedding)
-
-        proj_embeddings = self.embedding_proj(proj_embedding)
-        if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
-            encoder_hidden_states = self.encoder_hidden_states_proj(encoder_hidden_states)
-        # elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
-        #     raise ValueError("`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set")
-
-        hidden_states = self.proj_in(hidden_states)
-
-        # TODO this really also ought to derive from config's `k`
-        positional_embeddings = self.positional_embedding.to(hidden_states.dtype)
-
-        additional_embeds = []
-        additional_embeddings_len = 0
-
-        if encoder_hidden_states is not None:
-            additional_embeds.append(encoder_hidden_states)
-            additional_embeddings_len += encoder_hidden_states.shape[1]
-
-        if len(proj_embeddings.shape) == 2:
-            proj_embeddings = proj_embeddings[:, None, :]
-
-        if len(hidden_states.shape) == 2:
-            hidden_states = hidden_states[:, None, :]
-
-        additional_embeds = additional_embeds + [
-            proj_embeddings,
-            # time_embeddings[:, None, :],
-            hidden_states,
-        ]
-
-        if self.prd_embedding is not None:
-            prd_embedding = self.prd_embedding.to(hidden_states.dtype).expand(batch_size, -1, -1)
-            additional_embeds.append(prd_embedding)
-
-        hidden_states = torch.cat(
-            additional_embeds,
-            dim=1,
-        )
-
-        # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
-        additional_embeddings_len = additional_embeddings_len + proj_embeddings.shape[1] + 1
-        if positional_embeddings.shape[1] < hidden_states.shape[1]:
-            positional_embeddings = F.pad(
-                positional_embeddings,
-                (
-                    0,
-                    0,
-                    additional_embeddings_len,
-                    self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
-                ),
-                value=0.0,
-            )
-
-        hidden_states = hidden_states + positional_embeddings[:, :hidden_states.shape[1]]
-
-        if attention_mask is not None:
-            attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
-            attention_mask = F.pad(attention_mask, (0, self.additional_embeddings), value=0.0)
-            attention_mask = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype)
-            attention_mask = attention_mask.repeat_interleave(self.config.num_attention_heads, dim=0)
-
-        if self.norm_in is not None:
-            hidden_states = self.norm_in(hidden_states)
-
-        for block in self.transformer_blocks:
-            hidden_states = block(hidden_states, attention_mask=attention_mask)
-
-        hidden_states = self.norm_out(hidden_states)
-
-        if self.prd_embedding is not None:
-            hidden_states = hidden_states[:, -1]
-        else:
-            hidden_states = hidden_states[:, additional_embeddings_len:]
-
-        predicted_image_embedding = self.proj_to_clip_embeddings(hidden_states)
-
-        if not return_dict:
-            return (predicted_image_embedding,)
-
-        return PriorTransformerOutput(predicted_image_embedding=predicted_image_embedding)
-
-    def post_process_latents(self, prior_latents):
-        prior_latents = (prior_latents * self.clip_std) + self.clip_mean
-        return prior_latents

requirements.txt

@@ -3,16 +3,8 @@ numpy
 scikit-learn
 pandas
 torch
-torchvision
 numpy
-matplotlib
 diffusers
 accelerate
 transformers
-sentencepiece
-peft
-imageio
-apscheduler
-pandas
-av
-glob2
+peft

train.py

@@ -1,94 +0,0 @@
-
-
-########################################
-# python -m train
-###########################################
-
-
-import torch
-import logging
-import numpy as np
-from tqdm import tqdm
-from PIL import Image
-    
-from data import get_dataloader
-from model import get_model_and_tokenizer, get_optimizer
-import config
-
-logging.basicConfig(level=logging.INFO)
-
-def get_loss(model, input, target, tokenizer):
-    with torch.no_grad():
-        assert len(input.shape) == 5 # [batch, s, c, w, h]
-        cuts = config.number_k_clip_embed
-        assert input.shape[0] * input.shape[1] % cuts == 0, 'batch size * `k` preferred embeds must be divisible by cuts'
-        input = input.view(cuts//8, -1, 3, target.shape[-2], target.shape[-1])
-        full_seq = []
-        for b in input:
-            input = tokenizer(b)['image_embeds'] # in our case, tokenizer is a clip embedding model
-            full_seq.append(input)
-        input = torch.stack(full_seq)
-
-        target = tokenizer(target)['image_embeds']
-
-        input = input.view(target.shape[0], -1, target.shape[-1])
-        assert len(input.shape) == 3 # [batch, sequence, inner]
-    
-    with torch.cuda.amp.autocast(enabled=False, ):
-        input = input.to(torch.float32)
-        latent = torch.randn(input.shape[0], input.shape[-1], device=input.device)
-        output = model(latent, input).predicted_image_embedding
-
-    target = target.to(torch.float32)
-    mse_loss = torch.nn.functional.mse_loss(target, output).mean()
-    
-    assert len(target.shape) == 2 and len(output.shape) == 2
-    cosine_loss = 1 - torch.nn.functional.cosine_similarity(output, target).mean()
-    loss =  mse_loss + .2 * cosine_loss
-
-    logging.info(f'MSE: {mse_loss.item()}, Cosine: {cosine_loss.item()}, Weighted Total: {loss.item()}')
-    # TODO wandb
-
-    return loss
-
-def main():
-    np.random.seed(config.seed)
-    torch.manual_seed(config.seed)
-
-    model, tokenizer = get_model_and_tokenizer(config.model_path, config.device, config.dtype)
-    optimizer = get_optimizer(list(model.prior.parameters()), config.lr)
-    dataloader = get_dataloader(config.data_path, config.batch_size, config.num_workers, 
-                                model.prior_pipe.image_processor)
-
-    for epoch in range(config.epochs):
-        for ind, batch in tqdm(enumerate(iter(dataloader))):
-            if batch is None:
-                continue
-
-            input, target = batch
-            input = input.to(config.device)
-            target = target.to(config.device)
-
-            if ind % 50 == 0:
-                with torch.cuda.amp.autocast(enabled=True, dtype=config.dtype): # NOTE using autocast because our training model is also our val model, so don't want to set to full half precision.
-                    examples = ['../generative_recommender/Blue_Tigers_space/1o.png',
- '../generative_recommender/Blue_Tigers_space/2o.png',
- '../generative_recommender/Blue_Tigers_space/3o.png',
- '../generative_recommender/Blue_Tigers_space/4o.png',
- '../generative_recommender/Blue_Tigers_space/5o.png',
- '../generative_recommender/Blue_Tigers_space/6o.png',
- '../generative_recommender/Blue_Tigers_space/7o.png',
- '../generative_recommender/Blue_Tigers_space/8o.png',]
-                    model.do_validation([[Image.open('../'+j) for j in examples]])
-
-            loss = get_loss(model, input, target, tokenizer)
-            loss.backward()
-            optimizer.step()
-            optimizer.zero_grad()
-
-            if ind % 100 == 0:
-                # TODO add loading from path
-                model.prior.save_pretrained(f'{config.save_path}/last_epoch_ckpt', from_pt=True) 
-
-if __name__ == '__main__':
-    main()

train_requirements.txt

@@ -1,642 +0,0 @@
-absl-py==1.4.0
-accelerate==0.26.1
-addict==2.4.0
-aeiou==0.0.20
-aenum==3.1.15
-aiobotocore==2.13.0
-aiofiles==23.1.0
-aiohttp==3.9.5
-aioitertools==0.11.0
-aiosignal==1.3.1
-alias-free-torch==0.0.6
-aliyun-python-sdk-core==2.15.1
-aliyun-python-sdk-kms==2.16.3
-altair==4.2.2
-anaconda-anon-usage @ file:///croot/anaconda-anon-usage_1710965072196/work
-anaconda-client==1.11.2
-anaconda-cloud-auth @ file:///croot/anaconda-cloud-auth_1712794769769/work
-anaconda-navigator @ file:///croot/anaconda-navigator_1712087978399/work
-anaconda-project @ file:///opt/conda/conda-bld/anaconda-project_1660339890420/work
-annotated-types @ file:///croot/annotated-types_1709542908624/work
-antlr4-python3-runtime==4.9.3
-anyio==4.3.0
-appdirs==1.4.4
-apptools==5.2.1
-APScheduler==3.10.4
-argbind==0.3.9
-argcomplete==3.1.1
-asgiref==3.7.2
-asttokens==2.2.1
-astunparse==1.6.3
-async-timeout==4.0.2
-atproto==0.0.10
-attrs==25.1.0
-audioread==3.0.1
-auraloss==0.4.0
-av==10.0.0
-awscli==1.33.2
-backcall==0.2.0
-backports.functools-lru-cache @ file:///tmp/build/80754af9/backports.functools_lru_cache_1618170165463/work
-backports.tempfile @ file:///home/linux1/recipes/ci/backports.tempfile_1610991236607/work
-backports.weakref==1.0.post1
-bases==0.2.1
-basicsr==1.4.2
-beautifulsoup4==4.12.2
-bitsandbytes==0.43.1
-black==24.10.0
-bleach==6.1.0
-blendmodes==2022
-blinker==1.6.2
-blis==0.7.9
-blobfile==2.1.1
-blosc2==2.5.1
-bokeh==3.4.1
-boltons==23.0.0
-boto==2.49.0
-boto3==1.34.120
-botocore==1.34.120
-Bottleneck @ file:///croot/bottleneck_1707864210935/work
-braceexpand==0.1.7
-Brotli @ file:///tmp/abs_ecyw11_7ze/croots/recipe/brotli-split_1659616059936/work
-brotlipy==0.7.0
-cached-property==1.5.2
-cachetools==5.3.3
-Cartopy==0.21.1
-catalogue==2.0.8
-certifi==2025.1.31
-cffi==1.15.1
-cfgv==3.3.1
-chardet @ file:///home/builder/ci_310/chardet_1640804867535/work
-charset-normalizer==3.1.0
-chex==0.1.81
-clean-fid==0.1.35
-click==8.1.3
-clip @ git+https://github.com/openai/CLIP.git@a9b1bf5920416aaeaec965c25dd9e8f98c864f16
-clip-anytorch==2.6.0
-cloudpickle==2.2.1
-clyent==1.2.2
-cmake==3.26.4
-colorama==0.4.6
-colorcet==3.1.0
-colored==2.2.4
-coloredlogs==15.0.1
-comm==0.1.4
-commonmark==0.9.1
-comtypes==1.2.0
-conda @ file:///croot/conda_1696257509808/work
-conda-build @ file:///croot/conda-build_1701720841368/work
-conda-content-trust @ file:///tmp/abs_5952f1c8-355c-4855-ad2e-538535021ba5h26t22e5/croots/recipe/conda-content-trust_1658126371814/work
-conda-libmamba-solver @ file:///croot/conda-libmamba-solver_1698163451663/work/src
-conda-pack @ file:///tmp/build/80754af9/conda-pack_1611163042455/work
-conda-package-handling @ file:///croot/conda-package-handling_1690999929514/work
-conda-repo-cli @ file:///croot/conda-repo-cli_1709246574569/work
-conda-token @ file:///Users/paulyim/miniconda3/envs/c3i/conda-bld/conda-token_1662660369760/work
-conda-verify==3.4.2
-conda_index @ file:///croot/conda-index_1706633791028/work
-conda_package_streaming @ file:///croot/conda-package-streaming_1690987966409/work
-confection==0.0.4
-configobj==5.0.8
-configparser==7.0.0
-contextlib2==21.6.0
-contexttimer==0.3.3
-contourpy==1.2.1
-cramjam==2.8.3
-crcmod==1.7
-cryptography @ file:///croot/cryptography_1677533068310/work
-cuda-python==12.4.0
-curl_cffi==0.6.4
-cycler==0.11.0
-cymem==2.0.7
-Cython==0.29.35
-dacite==1.8.1
-dag-cbor==0.3.2
-datasets==2.21.0
-dctorch==0.1.2
--e git+https://github.com/jannerm/ddpo.git@b217eef955a94bf58e4de68caa5ec0a6558c221d#egg=ddpo
-debugpy==1.6.7
-decorator==4.4.2
-decord==0.6.0
-DeepCache==0.1.1
-deepspeed==0.14.2
-defusedxml @ file:///tmp/build/80754af9/defusedxml_1615228127516/work
-Deprecated==1.2.14
-deprecation==2.1.0
-descript-audio-codec==1.0.0
-descript-audiotools==0.7.2
-diffusers @ git+https://github.com/huggingface/diffusers.git@06beecafc55cfddeb1b0b8660188de249f74b899
-dill==0.3.6
-disnake==2.9.0
-Django==4.2.2
-django-memcache-status==2.3
-django-pylibmc==0.6.1
-dm-tree==0.1.8
-dnspython==2.6.1
-docker-pycreds==0.4.0
-docstring-parser==0.15
-docutils==0.16
-EasyProcess==1.1
-einops==0.7.0
-einops-exts==0.0.4
-ema-pytorch==0.2.3
-email_validator==2.1.1
-emoji==2.4.0
-encodec==0.1.1
-entrypoints==0.4
-envisage==7.0.3
-etils==1.3.0
-eva-decord==0.6.1
-exceptiongroup==1.1.1
-executing==1.2.0
-facexlib==0.3.0
-fairscale==0.4.4
-fastapi==0.111.0
-fastapi-cli==0.0.4
-fastcore==1.5.44
-fastjsonschema @ file:///opt/conda/conda-bld/python-fastjsonschema_1661371079312/work
-fastparquet==2024.5.0
-ffmpeg==1.4
-ffmpeg-python==0.2.0
-ffmpegio==0.8.3
-ffmpegio-core==0.8.3
-ffmpy==0.3.0
-filelock @ file:///croot/filelock_1700591183607/work
-filterpy==1.4.5
-fire==0.6.0
-flash-attn==2.5.9.post1
-Flask==2.3.2
-flatbuffers==23.5.26
-flatten-dict==0.4.2
-flax==0.6.9
-flow-vis==0.1
-fonttools==4.42.1
-frozenlist==1.3.3
-fsspec==2024.6.0
-ftfy==6.1.1
-future @ file:///croot/future_1677599870788/work
-fvcore==0.1.5.post20221221
-gast==0.4.0
-gcs-oauth2-boto-plugin==3.0
-gcsfs==2023.6.0
-gdcm==1.1
-gdown==4.7.1
-gfpgan==1.3.8
-gguf==0.16.2
-gin-config==0.5.0
-gitdb==4.0.10
-GitPython==3.1.30
-gmpy2 @ file:///tmp/build/80754af9/gmpy2_1645455533097/work
-google-api-core==2.11.1
-google-apitools==0.5.32
-google-auth==2.29.0
-google-auth-oauthlib==1.0.0
-google-cloud-core==2.3.2
-google-cloud-storage==2.10.0
-google-crc32c==1.5.0
-google-pasta==0.2.0
-google-reauth==0.1.1
-google-resumable-media==2.5.0
-googleapis-common-protos==1.59.1
-gradio==4.31.5
-gradio_client==0.16.4
-grpcio==1.54.2
-gsutil==5.25
-h11==0.14.0
-h5py==3.11.0
-hjson==3.1.0
-holoviews==1.18.3
-httpcore==1.0.5
-httplib2==0.20.4
-httptools==0.6.1
-httpx==0.27.0
-httpx-ws==0.3.1
-huggingface-hub==0.30.2
-humanfriendly==10.0
-humanize==4.7.0
-hydra-core==1.1.2
-hyper-tile @ git+https://github.com/tfernd/HyperTile@2ef64b2800d007d305755c33550537410310d7df
-icecream==2.1.3
-identify==2.5.24
-idna @ file:///croot/idna_1666125576474/work
-imagebind @ git+https://github.com/facebookresearch/ImageBind.git@95d27c7fd5a8362f3527e176c3a80ae5a4d880c0
-imageio==2.34.2
-imageio-ffmpeg==0.4.8
-importlib-metadata==6.8.0
-importlib-resources==5.12.0
-inflect==6.0.4
-inflection==0.5.1
-install==1.3.5
-iopath==0.1.9
-ipykernel==6.25.0
-ipython==8.14.0
-ipywidgets==8.0.6
-itsdangerous==2.1.2
-jaraco.classes @ file:///tmp/build/80754af9/jaraco.classes_1620983179379/work
-jax==0.4.6
-jaxlib==0.4.6
-jedi==0.19.0
-jeepney @ file:///tmp/build/80754af9/jeepney_1627537048313/work
-Jinja2==3.1.2
-jmespath==0.10.0
-joblib==1.3.2
-jsonmerge==1.8.0
-jsonpatch @ file:///croot/jsonpatch_1710807507480/work
-jsonpointer==2.1
-jsonschema @ file:///croot/jsonschema_1699041609003/work
-jsonschema-specifications @ file:///croot/jsonschema-specifications_1699032386549/work
-julius==0.2.7
-jupyter-js-widgets-nbextension==0.0.2.dev0
-jupyter_client==8.3.0
-jupyter_core @ file:///croot/jupyter_core_1698937308754/work
-jupyterlab-widgets==3.0.7
-k-diffusion==0.1.1
-kaggle==1.5.13
-kagglehub==0.3.12
-kandinsky2 @ git+https://github.com/ai-forever/Kandinsky-2.git@aeefc1ce3a989eefe7c99d6a02cce44318c4d210
-kecam==1.4.1
-keras==2.14.0
-keras-efficientnet-v2==1.2.2
-Keras-Preprocessing==1.1.2
-keyring @ file:///croot/keyring_1709632513808/work
-kiwisolver==1.4.5
-kornia==0.6.7
-laion-clap==1.1.4
-langcodes==3.3.0
-lark==1.1.2
-lazy_loader==0.2
-libarchive-c @ file:///tmp/build/80754af9/python-libarchive-c_1617780486945/work
-libclang==16.0.0
-libmambapy @ file:///croot/mamba-split_1694187754698/work/libmambapy
-librosa==0.9.2
-lightning-utilities==0.8.0
-linkify-it-py==2.0.2
-lit==16.0.6
-llvmlite==0.42.0
-lmdb==1.4.1
-local-attention==1.8.6
-loguru==0.7.2
-lpips==0.1.4
-lvis==0.5.3
-lxml==4.9.4
-Markdown==3.6
-markdown-it-py==2.2.0
-markdown2==2.4.8
-MarkupSafe==2.1.2
-matplotlib==3.7.3
-matplotlib-inline==0.1.6
-mayavi==4.8.1
-mc-bin-client==1.0.1
-mdit-py-plugins==0.3.3
-mdurl==0.1.2
-mediapipe==0.10.15
-menuinst @ file:///croot/menuinst_1706732933928/work
-mkl-fft @ file:///croot/mkl_fft_1695058164594/work
-mkl-random @ file:///croot/mkl_random_1695059800811/work
-mkl-service==2.4.0
-ml-collections==0.1.1
-ml-dtypes==0.2.0
-mmcv==1.7.2
-mmengine==0.10.4
-model-index==0.1.11
-more-itertools @ file:///croot/more-itertools_1700662129964/work
-MouseInfo==0.1.3
-moviepy==1.0.3
-mpmath @ file:///croot/mpmath_1690848262763/work
-msgpack==1.0.5
-multidict==6.0.4
-multiformats==0.2.1
-multiformats-config==0.2.0.post4
-multiprocess==0.70.14
-murmurhash==1.0.9
-mypy-extensions==1.0.0
-namex==0.0.8
-natsort==8.4.0
-navigator-updater @ file:///croot/navigator-updater_1713453362034/work
-nbformat @ file:///croot/nbformat_1694616755618/work
-ndindex==1.8
-nest-asyncio==1.5.7
-networkx==3.1
-nh3==0.2.13
-nibabel==5.1.0
-ninja==1.11.1
-nlpaug==1.1.11
-nltk==3.8.1
-nodeenv==1.8.0
-numba==0.59.1
-numexpr @ file:///croot/numexpr_1696515281613/work
-numpy==1.26.4
-nvidia-cublas-cu11==11.11.3.6
-nvidia-cublas-cu117==11.10.1.25
-nvidia-cublas-cu12==12.3.4.1
-nvidia-cuda-cupti-cu11==11.8.87
-nvidia-cuda-cupti-cu117==11.7.50
-nvidia-cuda-cupti-cu12==12.3.101
-nvidia-cuda-nvcc-cu11==11.8.89
-nvidia-cuda-nvcc-cu12==12.3.107
-nvidia-cuda-nvrtc-cu11==11.8.89
-nvidia-cuda-nvrtc-cu12==12.3.107
-nvidia-cuda-runtime-cu11==11.8.89
-nvidia-cuda-runtime-cu117==11.7.60
-nvidia-cuda-runtime-cu12==12.3.101
-nvidia-cudnn-cu11==8.7.0.84
-nvidia-cudnn-cu116==8.4.0.27
-nvidia-cudnn-cu12==9.0.0.312
-nvidia-cufft-cu11==10.9.0.58
-nvidia-cufft-cu12==11.0.12.1
-nvidia-curand-cu11==10.3.0.86
-nvidia-curand-cu12==10.3.4.107
-nvidia-cusolver-cu11==11.4.1.48
-nvidia-cusolver-cu12==11.5.4.101
-nvidia-cusparse-cu11==11.7.5.86
-nvidia-cusparse-cu12==12.2.0.103
-nvidia-nccl-cu11==2.19.3
-nvidia-nccl-cu12==2.19.3
-nvidia-nvjitlink-cu12==12.3.101
-nvidia-nvtx-cu11==11.8.86
-nvidia-pyindex==1.0.9
-oauth2client==4.1.3
-oauthlib==3.2.2
-omegaconf==2.3.0
-onnx==1.15.0
-onnx-graphsurgeon==0.5.2
-onnx2torch==1.5.6
-onnxruntime==1.16.3
-open_clip_torch==2.26.1
-openai==0.27.8
-opencv-contrib-python==4.6.0.66
-opencv-python==4.6.0
-opendatalab==0.0.10
-opendatasets==0.1.22
-openmim==0.3.9
-openxlab==0.1.1
-opt-einsum==3.3.0
-optax==0.1.5
-optree==0.11.0
-orbax-checkpoint==0.1.6
-ordered-set==4.1.0
-orjson==3.9.0
-oss2==2.17.0
-outcome==1.3.0.post0
-packaging @ file:///croot/packaging_1710807400464/work
-pandas==2.0.2
-panel==1.4.4
-param==2.1.0
-parameterized==0.9.0
-parso==0.8.3
-pathspec==0.11.1
-pathtools==0.1.2
-pathy==0.10.1
-pedalboard==0.7.4
-peewee==3.16.2
-peft==0.10.0
-pexpect==4.8.0
-pickleshare==0.7.5
-piexif==1.1.3
-Pillow==9.4.0
-pkce @ file:///croot/pkce_1690384816590/work
-pkginfo @ file:///croot/pkginfo_1679431160147/work
-platformdirs==3.8.0
-plotly==5.14.1
-pluggy @ file:///tmp/build/80754af9/pluggy_1648024709248/work
-ply==3.11
-polygraphy==0.49.9
-pooch==1.8.1
-portalocker==2.7.0
-pre-commit==3.3.1
-prefigure==0.0.9
-preshed==3.0.8
-proglog==0.1.10
-progressbar==2.5
-prompt-toolkit==3.0.39
-protobuf==4.25.3
-psutil==5.9.5
-ptyprocess==0.7.0
-pure-eval==0.2.2
-py-cpuinfo==9.0.0
-pyarrow==17.0.0
-pyasn1==0.6.0
-pyasn1-modules==0.3.0
-PyAutoGUI==0.9.54
-pyav==12.0.5
-pycocoevalcap==1.2
-pycocotools==2.0.6
-pycosat @ file:///croot/pycosat_1696536503704/work
-pycparser==2.21
-pycryptodome==3.20.0
-pycryptodomex==3.19.0
-pydantic==2.7.3
-pydantic_core==2.18.4
-pydeck==0.8.1b0
-pyDeprecate==0.3.2
-pydicom==2.3.1
-pydot==1.4.2
-pydub==0.25.1
-pyface==8.0.0
-PyGetWindow==0.0.9
-Pygments==2.15.1
-PyJWT==2.7.0
-pylibmc==1.6.3
-pyloudnorm==0.1.1
-pymemcache==4.0.0
-Pympler==1.0.1
-PyMsgBox==1.0.9
-pynndescent==0.5.12
-pynvml==11.5.0
-pyOpenSSL @ file:///croot/pyopenssl_1690223430423/work
-pyparsing==3.1.1
-pyperclip==1.9.0
-pyproj==3.6.0
-PyQt5==5.15.10
-PyQt5-sip @ file:///croot/pyqt-split_1698769088074/work/pyqt_sip
-pyre-extensions==0.0.29
-PyRect==0.2.0
-PyScreeze==1.0.1
-pyshp==2.3.1
-PySocks==1.7.1
-pystoi==0.4.1
-python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work
-python-docx==0.8.11
-python-dotenv==1.0.0
-python-magic==0.4.27
-python-memcached==1.59
-python-multipart==0.0.9
-python-slugify==8.0.1
-python3-xlib==0.15
-pytorch-lantern==0.12.7
-pytorch-lightning==2.1.0
-pytorch-pretrained-biggan==0.1.1
-pytorch-warmup==0.1.1
-pytorchvideo==0.1.5
-pytweening==1.2.0
-pytz @ file:///croot/pytz_1695131579487/work
-pyu2f==0.1.5
-PyVirtualDisplay==3.0
-pyviz_comms==3.0.2
-PyWavelets==1.4.1
-PyYAML==6.0
-pyzmq==25.1.0
-QtPy @ file:///croot/qtpy_1700144840038/work
-randomname==0.2.1
-realesrgan==0.3.0
-referencing @ file:///croot/referencing_1699012038513/work
-regex==2023.6.3
-repeng @ git+https://github.com/vgel/repeng.git@c9093abddd87f865e7e2bcf4b3e556ec8813b5b2
-replicate==0.25.1
-requests==2.32.3
-requests-oauthlib==1.3.1
-requests-toolbelt @ file:///croot/requests-toolbelt_1690874004362/work
-resampy==0.4.3
-resize-right==0.0.2
-responses==0.18.0
-retry-decorator==1.1.1
-rfc3986==1.5.0
-rich==12.6.0
-rotary-embedding-torch==0.3.0
-rpds-py @ file:///croot/rpds-py_1698945930462/work
-rsa==4.7.2
-ruamel-yaml-conda @ file:///croot/ruamel_yaml_1667489728852/work
-ruamel.yaml @ file:///croot/ruamel.yaml_1666304550667/work
-ruamel.yaml.clib @ file:///croot/ruamel.yaml.clib_1666302247304/work
-ruff==0.4.1
-s2wrapper @ git+https://github.com/bfshi/scaling_on_scales@f08aec91337ae1ed6d7cc7a55441a96d51c14dd1
-s3fs==2024.6.0
-s3transfer==0.10.1
-sacremoses==0.0.53
-safetensors==0.4.1
-salesforce-lavis @ git+https://github.com/salesforce/LAVIS.git@4a85b17846ee62f09c40f37cc955dd33c2abec68
-scikit-image==0.20.0
-scikit-learn==1.5.1
-scikit-surprise==1.1.3
-scipy==1.11.1
-SecretStorage @ file:///croot/secretstorage_1678709481048/work
-selenium==4.29.0
-semantic-version==2.10.0
-semver @ file:///croot/semver_1709243621175/work
-sentencepiece==0.1.99
-sentry-sdk==1.25.1
-setproctitle==1.3.2
-sgm @ file:///home/ryn_mote/Misc/generative-models
-shapely==2.0.1
-shellingham==1.5.0.post1
-shortuuid==1.0.11
-SimpleITK==2.2.1
-sip @ file:///croot/sip_1698675935381/work
-six @ file:///tmp/build/80754af9/six_1644875935023/work
-sk-video==1.1.10
-smart-open==6.3.0
-smmap==5.0.0
-sniffio==1.3.0
-sortedcontainers==2.4.0
-sounddevice==0.5.0
-SoundFile==0.10.2
-soupsieve==2.4.1
-spaces==0.27.0
-spacy==3.5.3
-spacy-legacy==3.0.12
-spacy-loggers==1.0.4
-sqlparse==0.4.4
-srsly==2.4.6
-stable-audio-tools==0.0.16
-stable-fast @ https://github.com/chengzeyi/stable-fast/releases/download/v1.0.4/stable_fast-1.0.4+torch220cu118-cp310-cp310-manylinux2014_x86_64.whl#sha256=11716f733237f557bee452eee63db415b4daeff29a28d939f73fff8003f0d415
-stack-data==0.6.2
-stanza==1.5.0
-starlette==0.37.2
-streamlit==1.22.0
-svgwrite==1.4.3
-sympy @ file:///croot/sympy_1701397643339/work
-tables==3.9.2
-tabulate==0.9.0
-tenacity==8.2.2
-tensorboard==2.14.1
-tensorboard-data-server==0.7.2
-tensorboard-plugin-wit==1.8.1
-tensorflow==2.14.0
-tensorflow-addons==0.16.1
-tensorflow-estimator==2.14.0
-tensorflow-hub==0.16.1
-tensorflow-io-gcs-filesystem==0.32.0
-tensorrt==8.6.1.post1
-tensorrt-bindings==8.6.1
-tensorrt-libs==8.6.1
-tensorstore==0.1.39
-termcolor==2.3.0
-text-unidecode==1.3
-tf-estimator-nightly==2.8.0.dev2021122109
-tf_keras==2.16.0
-tgate==0.1.1
-thinc==8.1.10
-threadpoolctl==3.2.0
-tifffile==2023.4.12
-tiktoken==0.4.0
-timm==0.9.8
-tokenizers==0.20.3
-tomesd==0.1.3
-tomli==2.0.1
-tomlkit==0.12.0
-toolz==0.12.0
-torch==2.2.2+cu118
-torch-ema==0.3
-torch-stoi==0.2.1
-torchaudio==2.0.2+cu118
-torchdiffeq==0.2.3
-torchio==0.19.0
-torchlibrosa==0.1.0
-torchmetrics==0.11.4
-torchsde==0.2.6
-torchvision==0.15.2+cu118
-tornado @ file:///croot/tornado_1696936946304/work
-tqdm==4.66.5
-traitlets @ file:///croot/traitlets_1671143879854/work
-traits==6.4.1
-traitsui==8.0.0
-trampoline==0.1.2
-transformers==4.46.3
-trio==0.29.0
-trio-websocket==0.12.2
-triton==2.2.0
-truststore @ file:///croot/truststore_1695244293384/work
-typed-argument-parser==1.8.1
-typeguard==4.2.1
-typer==0.12.3
-types-regex==2023.6.3.1
-typing-inspect==0.8.0
-typing-validation==1.0.0.post2
-typing_extensions==4.12.2
-tzdata @ file:///croot/python-tzdata_1690578112552/work
-tzlocal==5.0.1
-uc-micro-py==1.0.2
-ujson @ file:///opt/conda/conda-bld/ujson_1657544923770/work
-umap-learn==0.5.6
-undetected-chromedriver==3.5.5
-urllib3==1.26.18
-uvicorn==0.29.0
-uvloop==0.19.0
-v-diffusion-pytorch==0.0.2
-validators==0.20.0
-vector-quantize-pytorch==1.9.14
-vtk==9.2.6
-wandb==0.15.4
-wasabi==1.1.1
-watchdog==3.0.0
-watchfiles==0.22.0
-wavedrom==2.0.3.post3
-wcwidth==0.2.6
-webdataset==0.2.48
-webencodings==0.5.1
-websocket-client==1.8.0
-websockets==11.0.3
-Werkzeug==2.3.4
-wget==3.2
-widgetsnbextension==4.0.7
-wikipedia==1.4.0
-wrapt==1.14.1
-wsproto==1.2.0
-x-transformers==1.26.6
-xformers==0.0.20
-xxhash==3.2.0
-xyzservices==2024.4.0
-yacs==0.1.8
-yapf==0.40.1
-yarl==1.9.2
-yattag==1.15.1
-zipp==3.16.0
-zstandard @ file:///croot/zstandard_1677013143055/work

twitter_prompts.csv

@@ -0,0 +1,2088 @@
+,0
+0,Persephone
+1,"A portrait: man, whose lineage is corpse."
+2,a beautiful Waluigi
+3,president abe lincoln but a cat
+4,a woman and a crow
+5,"A professional, minimalist poster for the book The Old Man and the Sea"
+6,"half Ryan, half pigeon"
+7,Easter cat
+8,a beautiful woman
+9,a cherry tree made of fractals
+10,a christmas card from the victorian era
+11,The Theotokos is a bird
+12,
+13,A short life full of immense joy
+14,a character from a ghibli movie
+15,A structure made of people standing on top of other people
+16,зеленая собака
+17,a painting of the city
+18,a character from a ghibli movie
+19,pasta ömetabolism
+20,"a brilliant sketch titled ""Let Forever be Delayed"""
+21,the sun is shining on the lake
+22,Monet Lisa
+23,Genesis
+24,Synesthesia
+25,A dead man
+26,a cherry tree made of fractals
+27,enough
+28,The First Supper
+29,"i'm never gonna lose the desire to be loved. ""Oh the pain!! The pain! The agony!"""
+30,a painting of the last day
+31,Dead Codes by Ryan Murdock
+32,Genesis
+33,symmetry
+34,The OLD DATA
+35,a beautiful person
+36,the whitest man
+37,Death is a black camel that kneels down so we can ride
+38,a goblin by van gogh
+39,a portrait of a beautiful person
+40,a famous painted portrait of Lady Macbeth
+41,on the edge of grace
+42,"""A God Made of Wires and Dust"" by Ryan Murdock"
+43,symmetry
+44,a beautiful person
+45,"If we're not careful, it's only art about not-quite-dead pigs from now on."
+46,Beauty here -- a photograph by Ryan Murdock
+47,Hunger art by r.j. Murdock
+48,"A professional, minimalist poster for the film Donnie Darko"
+49,A black and white photo of a rainbow.
+50,a beautiful painting
+51,Monet Lisa
+52,a painting of the city
+53,A structure made of people standing on top of other people
+54,a criminal
+55,a cherry tree made of fractals
+56,Persephone flees Hades
+57,a tree with weaping branches
+58,a tree with weaping branches
+59,Genesis
+60,"Elvis holding a rabbit. A detailed, high-quality photo without distortions"
+61,a cute cat
+62,Aflame
+63,A cat wearing a tophat
+64,a terrifying night hag
+65,a beautiful woman
+66,Fire
+67,a cherry tree made of fractals
+68,The EcoCathedral
+69,a man on fire
+70,A structure made of people standing on top of other people
+71,totemic dusk
+72,The Death of Achilles
+73,Everywhere is no-place
+74,"Elvis holding a rabbit. A detailed, high-quality photo without distortions"
+75,An Arundel Tomb
+76,The average Advadnoun twitter follower
+77,I can read when there's writing on the wall
+78,
+79,A Tragedy
+80,Breathe deep the fumes at Delphi
+81,a pOrTRaIT Of tHe SpOngeBOb CHicKen
+82,a portrait of a beautiful person
+83,a beautiful person
+84,a portrait of a beautiful person
+85,Dead Codes by Ryan Murdock
+86,a photo of a purple dog
+87,Memento Mori
+88,"joy, happiness, bliss"
+89,Paradise Lost
+90,a beautiful person
+91,melancholia
+92,Monet Lisa
+93,"Of that which one cannot speak, one must be silent."
+94,
+95,Juliet
+96,God killed Van Gogh.
+97,a cherry tree made of fractals
+98,a horse with four eyes.
+99,a beautiful person
+100,With the Gods in envy of their visions
+101,The Lost Generation
+102,"Elvis holding a rabbit. A detailed, high-quality photo without distortions"
+103,a portrait of a beautiful person
+104,"half Ryan, half pigeon"
+105,a ginormous baby
+106,a wormhole
+107,Ophelia
+108,"""The hunger artist, full"" by Ryan Murdock"
+109,I will meet you in a field firmly set within wrong.nnBy Ryan Murdock
+110,"Intricate, Weeping Tree by Ryan Murdock"
+111,everything was beautiful and nothing hurt
+112,Saturn being a good dad to his son
+113,The years gild our memoriesnUnfairly.
+114,Intimations of Immortality
+115,meaningless neko ♡♡ neko
+116,chiaroscuro
+117,The Patron Saint of Evil
+118,a portrait of a beautiful person
+119,"Mephisto, shrouded in smoke"
+120,everything was beautiful and nothing hurt
+121,God killed Van Gogh.
+122,a man wearing makeup
+123,Everywhere is no-place
+124,🔴~__��'t �
+125,a beautiful waluigi
+126,a beautiful woman
+127,a portrait of a beautiful person
+128,/
+129,a green doG
+130,Dead Codes by Ryan Murdock
+131,I miss the Spring
+132,
+133,"a person with 2 eyes, one mouth, one nose, and no extra holes!"
+134,a woman and a crow
+135,a photo from {my hometown}
+136,Summer's Symphony: Counterpoint and Melody
+137,a cute cat
+138,"God, it's amazing."
+139,a painting of a sycamore in
+140,distinguished leaves decorated
+141,I do not think they'll sing for me
+142,the monet lisa
+143,a portrait of Abraham Lincoln
+144,The average Advadnoun twitter follower
+145,Dancing in the moonlight
+146,Shinji Ikari
+147,snazzy snazzy myspace cosplaying undergrad lookin cosplaying jared
+148,/
+149,is this loss? but it's van gogh
+150,Shinji Ikari
+151,a portrait of Juliet
+152,A sticky-note magnum opus featuring birds
+153,a silent palace
+154,"""A new hope blooms on the long notes of old horns."""
+155,The things I'll take with me
+156,is this loss? but it's van gogh
+157,a beautiful haunting
+158,Summer's Symphony: Counterpoint and Melody
+159,зеленая собака
+160,Last Breath
+161,Last Breath
+162,a cherry tree made of fractals
+163,The Theotokos is a bird
+164,a man holding an apple in one hand
+165,a beautiful person
+166,Monet Lisa
+167,A baroque portrait of Hamlet
+168,A gun killed Van Gogh.
+169,totemic dusk
+170,a portrait of a beautiful person
+171,pasta ömetabolism
+172,a beautiful person
+173,Taylor Swift
+174,colorful rabbits chandelier polaroid
+175,Dancing in the moonlight
+176,I will meet you in a field firmly set within wrong.nnBy Ryan Murdock
+177,symmetry
+178,"""Your mind flls in the gaps"" - by Ryan Murdock"
+179,the moon is a sickle cell
+180,"joy, happiness, bliss"
+181,Beauty here -- a photograph by Ryan Murdock
+182,a beautiful person
+183,a photo of a purple dog
+184,A propaganda poster promoting big chungus
+185,a beautiful person
+186,a tree with weaping branches
+187,A gun killed Van Gogh.
+188,"""A new hope blooms on the long notes of old horns."""
+189,a portrait of Abe Lincoln
+190,"""I love you more than the world can contain in its lonely and ramshackle head."""
+191,a character from a ghibli movie
+192,f*** it market standard rule language – distinguish np tax science research
+193,a portrait of Abe Lincoln
+194,a wholesome clown. Not creepy at all
+195,
+196,a corgi
+197,Easter cat
+198,a portrait of Abraham Lincoln
+199,a person's face
+200,A poster advertising Freudian Psychoanalytics
+201,Dancing in the moonlight
+202,Cat in a teacup
+203,a beautiful person
+204,Summer's Symphony: Counterpoint and Melody
+205,Post-Modern Nouveaux Statue
+206,a famous painted portrait of Lady Macbeth
+207,photosynthesis
+208,a photo of a purple dog
+209,
+210,a photo of Juliet
+211,The Starry Night
+212,Saturn being a good dad to his son
+213,a beautiful person
+214,In smoke and mould the fleshless dead
+215,totemic dusk
+216,a beautiful woman
+217,God killed Van Gogh.
+218,is this loss? but it's van gogh
+219,Nostos
+220,a silent palace
+221,"""The hunger artist, full"" by Ryan Murdock"
+222,a green doG
+223,Weeping Roses
+224,for sale: baby shoes; never worn
+225,a dog eating a cheese burger
+226,a man inside a cage
+227,Contentment at the Disco
+228,a photo from {my hometown}
+229,The EcoCathedral
+230,The OLD DATA
+231,treehouse in the style of studio ghibli animation
+232,
+233,"""The hunger artist, full"" by Ryan Murdock"
+234,
+235,Everywhere is no-place
+236,"A portrait: man, whose lineage is corpse."
+237,Last Breath
+238,A propaganda poster promoting big chungus
+239,зеленая собака
+240,a beautiful person
+241,Memento Mori
+242,A propaganda poster promoting big chungus
+243,is this loss?
+244,a tree with weaping branches
+245,Nostos
+246,Beauty here -- a photograph by Ryan Murdock
+247,a tiny church inside an eyeball
+248,
+249,a cherry tree made of fractals
+250,"joy, happiness, bliss"
+251,The First Supper
+252,"Elvis holding a rabbit. A detailed, high-quality photo without distortions"
+253,🔴~__��'t �
+254,Dancing in the moonlight
+255,Mona Lisa
+256,"God, it's amazing."
+257,a man holding an apple in one hand
+258,Some stolen Gods take up the reigns of darkness.
+259,🔴~__��'t �
+260,Figure 5: a corgi
+261,a photo from {my hometown}
+262,Anxiety: the one emotion that does not lie
+263,In the temple of God
+264,
+265,Metaphysics
+266,a beautiful woman
+267,a beautiful woman
+268,a surrealist eye
+269,the massive hope nof early iterations
+270,Ophelia
+271,a minimalist painting that you wouldn't understand
+272,Aflame
+273,a christmas card from the victorian era
+274,Dancing in the moonlight
+275,/
+276,"Mephisto, shrouded in smoke"
+277,a beautiful woman
+278,зеленая собака
+279,Easter cat
+280,The Oracle leans forward to say: beware the ides of March
+281,a portrait of a beautiful person
+282,Persephone
+283,a portrait of Abraham Lincoln
+284,the moon is a sickle cell
+285,symmetry
+286,Monet Lisa
+287,Saturn being a good dad to his son
+288,The Monet Lisa
+289,I sold my soul at the crossroads
+290,a beautiful person
+291,A poster advertising Freudian Psychoanalytics
+292,Cat in a teacup
+293,a silent palace
+294,
+295,a beautiful person
+296,
+297,
+298,Super Mario World but every character is Luigi
+299,chiaroscuro
+300,A dead man
+301,pasta ömetabolism
+302,A vanitas still life that features twitter follower counts
+303,slightly mild cosplaying pseudo beard
+304,Monet Lisa
+305,Mona Lisa
+306,handsome commemorative garden pigeon
+307,pasta ömetabolism
+308,"""The hunger artist, full"" by Ryan Murdock"
+309,a gorgeous bouquet with roses and sunflowers
+310,is this loss? but it's van gogh
+311,Memorial
+312,a forest filled with moonlight
+313,Post-Modern Nouveaux Statue
+314,she sings opera
+315,"God closes a door, boards up stained-glass windows."
+316,a dog wearing a suit playing tennis
+317,Intimations of Immortality
+318,
+319,turnt brony undergrad dwight
+320,a famous painted portrait of Lady Macbeth
+321,a cherry tree made of fractals
+322,Weeping Roses
+323,pasta ömetabolism
+324,
+325,
+326,"A portrait: man, whose lineage is corpse."
+327,The average Advadnoun twitter follower
+328,the moon is a sickle cell
+329,A black and white photo of a rainbow.
+330,God killed Van Gogh.
+331,turnt brony undergrad dwight
+332,"a brilliant sketch titled ""Let Forever be Delayed"""
+333,handsome commemorative garden pigeon
+334,a painting of a sycamore in
+335,a professional photo of a cat wearing a party hat
+336,Persephone
+337,Taylor Swift
+338,Homer Simpson
+339,using generated paint
+340,A black and white photo of a rainbow.
+341,meaningless neko ♡♡ neko
+342,is this loss? but it's van gogh
+343,Is this loss?
+344,a man from an anime
+345,the massive hope nof early iterations
+346,a beautiful woman
+347,Post-Modern Nouveaux Statue
+348,photosynthesis
+349,a cherry tree made of fractals
+350,a minimalist painting that you wouldn't understand
+351,a corgi
+352,handsome commemorative garden pigeon
+353,The OLD DATA
+354,cowboy with a trumpet
+355,A short life full of immense joy
+356,a beautiful woman
+357,The end of nothing is eroding. A watercolor by K.
+358,''
+359,symmetry
+360,a portrait of Abraham Lincoln
+361,Last Breath
+362,the eternal dread of lemongrab
+363,vangogh # landscape
+364,a cherry tree made of fractals
+365,The Devil Whispers blood
+366,a silent palace
+367,Paradise Lost
+368,Monet Lisa
+369,Everywhere is no-place
+370,Taylor Swift
+371,"r.j. Murdock's ""The Death of a Hacker"""
+372,a portrait of Abraham Lincoln
+373,I know the end
+374,Persephone
+375,A poster advertising Freudian Psychoanalytics
+376,a beautiful woman
+377,A black and white photo of a rainbow.
+378,the whitest man
+379,the eternal dread of lemongrab
+380,a drawing by an AI
+381,🔴~__��'t �
+382,We haunt the synapses
+383,frogs in the style of Ralph Steadman
+384,a beautiful haunting
+385,photosynthesis
+386,a character from a ghibli movie
+387,A structure made of people standing on top of other people
+388,Intimations of Immortality
+389,a jukebox powered by smoke
+390,beautiful art
+391,In the temple of God
+392,Intimations of Immortality
+393,a beautiful painting
+394,A gun killed Van Gogh.
+395,a man with no eyes
+396,a famous painted portrait of Lady Macbeth
+397,a tasteful haunting
+398,a jukebox powered by smoke
+399,a portrait of Juliet
+400,The Patron Saint of Evil
+401,a beautiful Waluigi
+402,a gilded lily
+403,
+404,Kierkegaard on the edge
+405,a beautiful person
+406,Just west of Alpha Centauri
+407,a horse with four eyes.
+408,Good grief
+409,a portrait of a beautiful person
+410,Aflame
+411,a man wearing makeup
+412,a portrait of Abraham Lincoln
+413,a corgi
+414,I do not think they'll sing for me
+415,Intimations of Immortality
+416,A poster serving as a memento mori
+417,Psychology
+418,A gun killed Van Gogh.
+419,"a brilliant sketch titled ""Let Forever be Delayed"""
+420,using generated paint
+421,pasta ömetabolism
+422,a summer day
+423,a gilded lily
+424,a cute cat
+425,on the edge of grace
+426,Art is growing.
+427,Spiderman delivering a pizza
+428,the intersection of art and technology
+429,"""The hunger artist, full"" by Ryan Murdock"
+430,a tarot card
+431,an omen
+432,slightly mild cosplaying pseudo beard
+433,meaningless neko ♡♡ neko
+434,intricate nothing
+435,symmetry
+436,I have no idea what anything in this image is
+437,a photo from {my hometown}
+438,a sad man
+439,face like an M.C. Escher drawing n(you could get lost in its beauty)
+440,A E S T H E T I C ?
+441,totemic dusk
+442,Nostos
+443,"i'm never gonna lose the desire to be loved. ""Oh the pain!! The pain! The agony!"""
+444,a silent palace
+445,a beautiful painting
+446,"half Ryan, half pigeon"
+447,Weeping Roses
+448,a broken heart
+449,a portrait of Juliet
+450,a painting of the last day
+451,"a brilliant sketch titled ""Let Forever be Delayed"""
+452,a beautiful person
+453,"""The hunger artist, full"" by Ryan Murdock"
+454,a horse with four eyes.
+455,a photo of a purple dog
+456,a summoning
+457,Redacted ████████
+458,a ginormous baby
+459,On the edge of endless darkness
+460,The Fates knit such delicate nooses for us to bind
+461,Theotokos of Milk
+462,A minimalistic still life of a cat sitting on a table
+463,Dancing in the moonlight
+464,a minimalist painting that you wouldn't understand
+465,a beautiful woman
+466,totemic dusk
+467,"Ryan Murdock's ""God haunts the suburbs"""
+468,Dancing in the moonlight
+469,a beautiful woman
+470,a city in Van Gogh's style
+471,"""The hunger artist, full"" by Ryan Murdock"
+472,a person's face
+473,a portrait of <name>
+474,Dancing in the moonlight
+475,a portrait of Persephone
+476,a minimalist painting that you wouldn't understand
+477,a portrait of Abraham Lincoln
+478,Synesthesia
+479,a cute corgi
+480,a portrait of advadnoun
+481,a green doG
+482,a man with no eyes
+483,a cherry tree made of fractals
+484,a ginormous baby
+485,
+486,turnt brony undergrad dwight
+487,"God, it's amazing."
+488,"""The hunger artist, full"" by Ryan Murdock"
+489,We haunt the synapses
+490,God's Eyes are Wired Shut
+491,a famous painted portrait of Lady Macbeth
+492,Juliet
+493,a character from a ghibli movie
+494,the whitest man
+495,a horse with four eyes.
+496,a photo of a purple dog
+497,a beautiful person
+498,The Patron Saint of Hackers
+499,Dead Codes by Ryan Murdock
+500,something trite
+501,beautiful art
+502,
+503,the monet lisa
+504,a cute cat
+505,👉  👈
+506,A propaganda poster promoting big chungus
+507,a beautiful person
+508,a portrait of advadnoun
+509,a cherry tree made of fractals
+510,"It's a meme, I guess"
+511,a person's face
+512,A baroque portrait of Hamlet
+513,a city in Van Gogh's style
+514,"""The hunger artist, full"" by Ryan Murdock"
+515,a man with no eyes
+516,a minimalist painting that you wouldn't understand
+517,pathoarthistory evankirstel sleep depend npainter ☼ nightmare comprehend
+518,"joy, happiness, bliss"
+519,
+520,"a brilliant sketch titled ""Let Forever be Delayed"""
+521,Last Breath
+522,On the edge of endless darkness
+523,a photo of Juliet
+524,Summer's Symphony: Counterpoint and Melody
+525,Persephone
+526,a green doG
+527,symmetry
+528,"Elvis holding a rabbit. A detailed, high-quality photo without distortions"
+529,The Starry Night
+530,Genesis
+531,bootleg edgy casual assange
+532,Memento Mori
+533,meaningless neko ♡♡ neko
+534,totemic dusk
+535,Aflame
+536,"""Here lies Ryan Murdock"" -- a memorial with the date and cause of departure."
+537,"""The hunger artist, full"" by Ryan Murdock"
+538,f*** you
+539,a tree with leaves that are amarillo sightseeing thetic
+540,a painting of the last day
+541,"God, it's amazing."
+542,Paradise Lost
+543,a gilded lily
+544,Aflame
+545,a portrait of <name>
+546,a painting that couldn't be sold
+547,a man holding an apple in one hand
+548,"A clock with gorgeous, intricate gradients on it"
+549,a goblin by van gogh
+550,"a person with 2 eyes, one mouth, one nose, and no extra holes!"
+551,A vanitas still life that features twitter follower counts
+552,the whitest man
+553,"""The hunger artist, full"" by Ryan Murdock"
+554,is this loss? but it's van gogh
+555,Synesthesia
+556,Aflame
+557,a cherry tree made of fractals
+558,A propaganda poster for daring to eat a peach.
+559,A vanitas still life that features twitter follower counts
+560,the moon is a sickle cell
+561,The Lost Generation
+562,the eternal dread of lemongrab
+563,The First Supper
+564,a character from a ghibli movie
+565,a man on fire
+566,symmetry
+567,pasta ömetabolism
+568,a horse with four eyes.
+569,Metaphysics
+570,Synesthesia
+571,The Fates knit such delicate nooses for us to bind
+572,Knowledge of Good and Evil
+573,meaningless neko ♡♡ neko
+574,A Tragedy
+575,
+576,a drawing by an AI
+577,The Fool tarot card but it's The Lovers
+578,a beautiful person
+579,a silent palace
+580,an omen
+581,"A portrait: man, whose lineage is corpse."
+582,Dancing in the moonlight
+583,a gilded lily
+584,turnt brony undergrad dwight
+585,"a person with 2 eyes, one mouth, one nose, and no extra holes!"
+586,totemic dusk
+587,Monet Lisa
+588,fatal skull prose visits bend ntuscan painting underthecomprehend
+589,Monet Lisa
+590,Aflame
+591,an intricate painting Of Eternity by Ryan Murdock
+592,"Intricate, Weeping Tree by Ryan Murdock"
+593,Summer's Symphony: Counterpoint and Melody
+594,Monet Lisa
+595,Last Breath
+596,is this loss? but it's van gogh
+597,"half Ryan, half pigeon"
+598,"God closes a door, boards up the stained-glass windows. nnGod hides."
+599,Everything was beautiful and nothing hurt
+600,"r.j. Murdock's ""The Death of a Hacker"""
+601,"Elvis holding a rabbit. A detailed, high-quality photo without distortions"
+602,meaningless neko ♡♡ neko
+603,twilight
+604,the sun is shining on the lake
+605,a portrait of a beautiful person
+606,the sun is shining on the lake
+607,
+608,a portrait of Abe Lincoln
+609,A gun killed Van Gogh.
+610,a photo from {my hometown}
+611,The Fool tarot card but it's The Lovers
+612,A structure made of people standing on top of other people
+613,"God closes a door, boards up the stained-glass windows. nnGod hides."
+614,an old man
+615,a beautiful waluigi
+616,is this loss? but it's van gogh
+617,a man standing alone in a wheat field
+618,Aflame
+619,Synesthesia
+620,
+621,Intimations of Immortality
+622,The First Supper
+623,"God, it's amazing."
+624,Persephone
+625,"r.j. Murdock's ""The Death of a Hacker"""
+626,God's Eyes are Wired Shut
+627,Do you remember the mythic beast?nA last-minute cancellation at The Last Supper
+628,f*** it market standard rule language – distinguish np tax science research
+629,totemic dusk
+630,Cat in a teacup
+631,frogs in the style of Ralph Steadman
+632,a beautiful person
+633,The Starry Night
+634,Juliet
+635,turnt brony undergrad dwight
+636,
+637,There is something so interesting about a bleeding edge full of dust.
+638,On the edge of endless darkness
+639,The warrior Achilles devours slain Hector's corpse -- an ink poster by Ryan Murdock
+640,turnt brony undergrad dwight
+641,Intimations of Immortality
+642,a portrait of Abraham Lincoln
+643,a man wearing makeup
+644,a sketch of the mind of god
+645,a man on fire
+646,a portrait of Abraham Lincoln
+647,
+648,The ancient Θωερτυ keyboard of brave Achilles
+649,goes thu extre— dum dum dizzy grimstupiddic ious mindidioirony merely experiment .
+650,"A group portrait featuring the id, ego, and superego"
+651,a photo from {my hometown}
+652,A structure made of people standing on top of other people
+653,a famous painted portrait of Lady Macbeth
+654,ogden
+655,pasta ömetabolism
+656,a tree with weaping branches
+657,photosynthesis
+658,handsome commemorative garden pigeon
+659,a photo of a purple dog
+660,"a brilliant sketch titled ""Let Forever be Delayed"""
+661,"i'm never gonna lose the desire to be loved. ""Oh the pain!! The pain! The agony!"""
+662,The Death of Achilles
+663,potus mormon lincoln rooster
+664,A black and white photo of a rainbow.
+665,a beautiful haunting
+666,"Elvis holding a rabbit. A detailed, high-quality photo without distortions"
+667,In the temple of God
+668,a beautiful person
+669,The Patron Saint of Mathematics
+670,a brilliant painting titled
+671,a gilded lily
+672,a tiny church inside an eyeball
+673,a portrait of Juliet
+674,A painting that sold for a million dollars
+675,the moon is a sickle cell
+676,photosynthesis
+677,The Theotokos is a bird
+678,the whitest man
+679,The Monet Lisa
+680,Beauty here -- a photograph by Ryan Murdock
+681,Breathe deep the fumes at Delphi
+682,the sun is shining on the lake
+683,photosynthesis
+684,The things I'll take with me
+685,a green doG
+686,a beautiful person
+687,The years gild our memoriesnUnfairly.
+688,The Lost Generation
+689,a beautiful person
+690,The average Advadnoun twitter follower
+691,a goblin by van gogh
+692,pathoarthistory evankirstel sleep depend npainter ☼ nightmare comprehend
+693,"A professional, minimalist poster for the book The Old Man and the Sea"
+694,
+695,Cat in a teacup
+696,a beautiful person
+697,beautiful art
+698,I sold my soul at the crossroads
+699,face like an M.C. Escher drawing n(you could get lost in its beauty)
+700,a gorgeous bouquet with roses and sunflowers
+701,a portrait of Abraham Lincoln
+702,Sisyphus
+703,a cute cat
+704,a portrait of <name>
+705,a minimalist painting that you wouldn't understand
+706,a photo of Bernie Sanders sitting on a chair and wearing mittens
+707,a woman and a crow
+708,a character from a ghibli movie
+709,a photo of a purple dog
+710,a dog eating a cheese burger
+711,Last Breath
+712,a sketch of the mind of god
+713,a steampunk technomancer
+714,We haunt the synapses
+715,using generated paint
+716,a cherry tree made of fractals
+717,Saturn being a good dad to his son
+718,oof deeplearning corgi corgi rendering
+719,
+720,Dancing in the moonlight
+721,A Tragedy
+722,A propaganda poster promoting big chungus
+723,A structure made of people standing on top of other people
+724,"A cute, minmimalist valentine's day card featuring a cat"
+725,a cute cat
+726,The skyscraper draws blood -- a landscape
+727,the monet lisa
+728,a photo of a person generating a painting of a person with AI
+729,"""A God Made of Wires and Dust"" by Ryan Murdock"
+730,Monet Lisa
+731,photosynthesis
+732,Hunger art by r.j. Murdock
+733,"""The hunger artist, full"" by Ryan Murdock"
+734,An Arundel Tomb
+735,twilight
+736,"r.j. Murdock's ""The Death of a Hacker"""
+737,living in a den of thieves
+738,"""A new hope blooms on the long notes of old horns."""
+739,"The laptop of brave Achaean Achilles, who would not live long."
+740,a minimalist painting that you wouldn't understand
+741,"Intricate, Weeping Tree by Ryan Murdock"
+742,The Fool
+743,a summoning
+744,pasta ömetabolism
+745,"a brilliant sketch titled ""Let Forever be Delayed"""
+746,a silent palace
+747,The average Advadnoun twitter follower
+748,f*** it market standard rule language – distinguish np tax science research
+749,Monet Lisa
+750,"a brilliant sketch titled ""Let Forever be Delayed"""
+751,meaningless neko ♡♡ neko
+752,"God, it's amazing."
+753,Nostos
+754,Shinji Ikari
+755,a beautiful woman
+756,The Starry Night
+757,hamont parkland avenue incumbscreenshotsaturday hemisphere footage algorithm
+758,a beautiful woman
+759,
+760,Summer always ending
+761,president abe lincoln but a cat
+762,🎷
+763,"Elvis holding a rabbit. A detailed, high-quality photo without distortions"
+764,a cherry tree made of fractals
+765,A painting that sold for one billion dollars
+766,a man standing alone in a wheat field
+767,symmetry
+768,a broken heart
+769,a silent palace
+770,A vanitas still life that features twitter follower counts
+771,"half Ryan, half pigeon"
+772,"a brilliant sketch titled ""Let Forever be Delayed"""
+773,slightly mild cosplaying pseudo beard
+774,a portrait of <name>
+775,God's Eyes are Wired Shut
+776,she sings opera
+777,a person's face
+778,a cherry tree made of fractals
+779,Dead Codes by Ryan Murdock
+780,Everywhere is no-place
+781,The First Supper
+782,Monet Lisa
+783,A short life full of immense joy
+784,Anxiety: the one emotion that does not lie
+785,Anxiety: the one emotion that does not lie
+786,symmetry
+787,a beautiful waluigi
+788,a goblin by van gogh
+789,"""A new hope blooms on the long notes of old horns."""
+790,Juliet
+791,The OLD DATA
+792,a beautiful woman
+793,The average Advadnoun twitter follower
+794,Synesthesia by Ryan Murdock
+795,Persephone flees Hades
+796,Last Breath
+797,a portrait of Persephone
+798,Homer Simpson
+799,totemic dusk
+800,a steampunk technomancer
+801,a portrait of Abraham Lincoln
+802,a cherry tree made of fractals
+803,bored of dying
+804,a famous painted portrait of Lady Macbeth
+805,a summer day
+806,A E S T H E T I C ?
+807,A vanitas still life that features twitter follower counts
+808,an illustration of a baby daikon radish in a tutu walking a dog
+809,Persephone
+810,pasta ömetabolism
+811,A vision of the Theotokos in my glass of coffee
+812,a dog.
+813,a photo of a person generating a painting of a person with AI
+814,🔴~__��'t �
+815,Intimations of Immortality
+816,snazzy snazzy myspace cosplaying undergrad lookin cosplaying jared
+817,A dead man
+818,The Oracle leans forward to say: beware the ides of March
+819,Monet Lisa
+820,a silent palace
+821,an intricate painting of eternity
+822,A propaganda poster for chunky cats.
+823,God killed Van Gogh.
+824,the eyes of God are wired shut
+825,Persephone
+826,symmetry
+827,Mona Lisa
+828,Saturn being a good dad to his son
+829,a technomancer
+830,
+831,a cherry tree made of fractals
+832,A cat wearing a tophat
+833,frogs in the style of Ralph Steadman
+834,a portrait of a beautiful person
+835,a green dog
+836,a portrait of Abraham Lincoln
+837,Hungry Dogs Will Devour in the Daytime
+838,a photo of a purple dog
+839,Cat in a teacup
+840,
+841,Nostos
+842,A baroque portrait of Hamlet
+843,Saturn being a good dad to his son
+844,Hell is Paradise
+845,a taste
+846,"God, it's amazing."
+847,Everywhere is no-place
+848,a minimalist painting that you wouldn't understand
+849,a tree with weaping branches
+850,a portrait of Elvis Presley
+851,a man standing alone in a wheat field
+852,Juliet
+853,I sold my soul at the crossroads
+854,a beautiful person
+855,photosynthesis
+856,
+857,"Mephisto, shrouded in smoke"
+858,playing Go with Death
+859,a painting of the last day
+860,totemic dusk
+861,Hell is Paradise
+862,a christmas card from the victorian era
+863,Good grief
+864,handsome commemorative garden pigeon
+865,a portrait of <name>
+866,a portrait of Abraham Lincoln
+867,she came in through the wall
+868,a sad man
+869,In the temple of God
+870,fuzzy pals hum
+871,a painting of a sycamore in
+872,a beautiful waluigi
+873,"a brilliant sketch titled ""Let Forever be Delayed"""
+874,a portrait of a beautiful person
+875,a portrait of Juliet
+876,MEMETIC HAZARD
+877,The years gild our memoriesnUnfairly.
+878,Mona Lisa
+879,pasta ömetabolism
+880,pasta ömetabolism
+881,bored of dying
+882,Cat in a teacup
+883,a cherry tree made of fractals
+884,an intricate drawing of eternity
+885,mammals
+886,a portrait of Persephone
+887,treehouse in the style of studio ghibli animation
+888,watching TV in purgatory
+889,The winds of change by Ryan Murdock
+890,a technomancer
+891,a portrait of Persephone
+892,Last Breath
+893,A minimalistic still life of a cat sitting on a table
+894,
+895,cult of prisms
+896,Aflame
+897,Cat in a teacup
+898,"God, it's amazing."
+899,a minimalist painting that you wouldn't understand
+900,a woman and a crow
+901,totemic dusk
+902,a city in Van Gogh's style
+903,A baroque portrait of Hamlet
+904,murdoch
+905,a silent palace
+906,Anxiety: the one emotion that does not lie
+907,a photo of a purple dog
+908,the moon is a sickle cell
+909,Tendrils of smoke curl around the caterpillar with a hookah
+910,president abe lincoln but a cat
+911,a beautiful woman
+912,handsome commemorative garden pigeon
+913,an intricate painting of eternity
+914,"God, it's amazing."
+915,Grippy socks; no drawstrings: high fashion
+916,The average Advadnoun twitter follower
+917,"Elvis holding a rabbit. A detailed, high-quality photo without distortions"
+918,a photo from {my hometown}
+919,MEMETIC HAZARD
+920,a portrait of Elvis Presley
+921,a woman and a crow
+922,Saturn being a good dad to his son
+923,beautiful art
+924,Shinji Ikari
+925,a portrait of <name>
+926,a photo of a purple dog
+927,Ophelia
+928,a dog wearing a suit playing tennis
+929,We haunt the synapses
+930,I do not think they'll sing for me
+931,Genesis
+932,a beautiful person
+933,"a brilliant sketch titled ""Let Forever be Delayed"""
+934,Metaphysics
+935,bored of dying
+936,treehouse in the style of studio ghibli animation
+937,
+938,photosynthesis
+939,A structure made of people standing on top of other people
+940,meaningless neko ♡♡ neko
+941,a photo of the sun melting into the ocean
+942,symmetry
+943,the moon is a sickle cell
+944,Dancing in the moonlight
+945,Last Breath
+946,I sold my soul at the crossroads
+947,a beautiful woman
+948,"God, it's amazing."
+949,Cat in a teacup
+950,a tree with weaping branches
+951,"God, it's amazing."
+952,Cat in a teacup
+953,"r.j. Murdock's ""The Death of a Hacker"""
+954,using generated paint
+955,fuzzy pals hum
+956,"A portrait: man, whose lineage is corpse."
+957,a ginormous baby
+958,a beautiful woman
+959,"half Ryan, half pigeon"
+960,when the wind blows
+961,a beautiful woman
+962,pasta ömetabolism
+963,a cherry tree made of fractals
+964,The Monet Lisa
+965,"""The hunger artist, full"" by Ryan Murdock"
+966,a portrait of advadnoun
+967,The Fool tarot card but it's The Lovers
+968,Persephone
+969,"Elvis holding a rabbit. A detailed, high-quality photo without distortions"
+970,an omen
+971,the eternal dread of lemongrab
+972,a man on fire
+973,Aflame
+974,"i'm never gonna lose the desire to be loved. ""Oh the pain!! The pain! The agony!"""
+975,twilight
+976,hamont parkland avenue incumbscreenshotsaturday hemisphere footage algorithm
+977,a silent palace
+978,a selfie
+979,the moon is a sickle cell
+980,a portrait of Abraham Lincoln
+981,a tree with weaping branches
+982,a tiny church inside an eyeball
+983,a portrait of a beautiful person
+984,Paradise Lost
+985,a horse with four eyes.
+986,president abe lincoln but a cat
+987,a summer day
+988,Anxiety: the one emotion that does not lie
+989,Saturn being a good dad to his son
+990,In the temple of God
+991,Redacted ████████
+992,Dr. Faustus and Mephisto
+993,a minimalist painting that you wouldn't understand
+994,a man standing alone in a wheat field
+995,a seance in the basement
+996,a portrait of <name>
+997,Aflame
+998,the moon is a sickle cell
+999,beautiful art
+1000,a man on fire
+1001,a tiny church inside an eyeball
+1002,totemic dusk
+1003,Persephone
+1004,piss indiefilm
+1005,a beautiful woman
+1006,The EcoCathedral
+1007,"joy, happiness, bliss"
+1008,Intimations of Immortality
+1009,the whitest man
+1010,a silent palace
+1011,
+1012,a woman and a crow
+1013,Memento Mori
+1014,Visions in envy of the gods
+1015,symmetry
+1016,A poster advertising Freudian Psychoanalytics
+1017,A propaganda poster promoting big chungus
+1018,With the Gods in envy of their visions
+1019,a cherry tree made of fractals
+1020,pasta ömetabolism
+1021,snazzy snazzy myspace cosplaying undergrad lookin cosplaying jared
+1022,a beautiful person
+1023,cowboy with a trumpet
+1024,a portrait of a beautiful person
+1025,The OLD DATA
+1026,f*** it market standard rule language – distinguish np tax science research
+1027,murdoch
+1028,Some stolen Gods take up the reigns of darkness.
+1029,a portrait of Juliet
+1030,a god
+1031,she sings opera
+1032,The First Supper
+1033,handsome commemorative garden pigeon
+1034,cult of prisms
+1035,Cat in a teacup
+1036,💨 👻 ☺ 🔮 🔺 ✊
+1037,a portrait of Abraham Lincoln
+1038,a corgi
+1039,a beautiful woman
+1040,a portrait of a beautiful person
+1041,Dead Codes by Ryan Murdock
+1042,totemic dusk
+1043,Juliet
+1044,a portrait of Elvis Presley
+1045,a criminal
+1046,Genesis where the universe was made
+1047,a portrait of <name>
+1048,turnt brony undergrad dwight
+1049,Cat in a teacup
+1050,a corgi
+1051,"Hamlet saying ""To be or not to be"""
+1052,a portrait of a beautiful person
+1053,A E S T H E T I C ?
+1054,Figure 5: a corgi
+1055,A gun killed Van Gogh.
+1056,Persephone flees Hades
+1057,a silent palace
+1058,pasta ömetabolism
+1059,a beautiful person
+1060,on the edge of grace
+1061,a portrait of Elvis Presley
+1062,Persephone
+1063,Tendrils of smoke curl around the caterpillar with a hookah
+1064,"half Ryan, half pigeon"
+1065,a sunflower
+1066,a beautiful person
+1067,a portrait of Juliet
+1068,A dead man
+1069,a character from a ghibli movie
+1070,a silent palace
+1071,a portrait of Elvis Presley
+1072,a portrait of advadnoun
+1073,A E S T H E T I C ?
+1074,зеленая собака
+1075,A baroque portrait of Hamlet
+1076,a man at the beach
+1077,Sisyphus
+1078,Good grief
+1079,"r.j. Murdock's ""The Death of a Hacker"""
+1080,a beautiful woman
+1081,🔴~__��'t �
+1082,a portrait of advadnoun
+1083,a painting of a sycamore in
+1084,president abe lincoln but a cat
+1085,The agony of time
+1086,God once loved a woman
+1087,pasta ömetabolism
+1088,Dead Codes by Ryan Murdock
+1089,
+1090,slightly mild cosplaying pseudo beard
+1091,Last Breath
+1092,The Oracle leans forward to say: beware the ides of March
+1093,The Devil Wears Khakis
+1094,"""The hunger artist, full"" by Ryan Murdock"
+1095,In the temple of God
+1096,a beautiful person
+1097,a man from an anime
+1098,She's gorgeous
+1099,A vanitas still life that features twitter follower counts
+1100,
+1101,the eternal dread of lemongrab
+1102,Advadnoun
+1103,a summer day
+1104,The Fool tarot card but it's The Lovers
+1105,I miss the Spring
+1106,an illustration of a baby daikon radish in a tutu walking a dog
+1107,The Oracle leans forward to say: beware the ides of March
+1108,Contentment at the Disco
+1109,The First Supper
+1110,Saturn being a good dad to his son
+1111,a beautiful woman
+1112,"Intricate, Weeping Tree by Ryan Murdock"
+1113,"a brilliant sketch titled ""Let Forever be Delayed"""
+1114,beautiful art
+1115,
+1116,a silent palace
+1117,a portrait of Juliet
+1118,A propaganda poster promoting big chungus
+1119,a portrait of a beautiful person
+1120,a portrait of Abraham Lincoln
+1121,
+1122,the whitest man
+1123,a portrait of Abe Lincoln
+1124,Monet Lisa
+1125,The Fool tarot card but it's The Lovers
+1126,a portrait of <name>
+1127,a portrait of Elvis Presley
+1128,Post-Modern Nouveaux Statue
+1129,a cherry tree made of fractals
+1130,f*** it market standard rule language – distinguish np tax science research
+1131,symmetry
+1132,pasta ömetabolism
+1133,a brilliant painting titled
+1134,The First Supper
+1135,a corgi
+1136,a beautiful person
+1137,a green doG
+1138,The OLD DATA
+1139,Ophelia
+1140,a portrait of Abraham Lincoln
+1141,incineratures motherhood
+1142,a green dog
+1143,a portrait of advadnoun
+1144,a sunflower
+1145,
+1146,a man from an anime
+1147,Beauty here -- a photograph by Ryan Murdock
+1148,slightly mild cosplaying pseudo beard
+1149,Nostos
+1150,pasta ömetabolism
+1151,a beautiful person
+1152,"half Ryan, half pigeon"
+1153,turnt brony undergrad dwight
+1154,beautiful art
+1155,a portrait of Persephone
+1156,A sticky-note magnum opus featuring birds
+1157,I sold my soul at the crossroads
+1158,"a brilliant sketch titled ""Let Forever be Delayed"""
+1159,A poster advertising Freudian Psychoanalytics
+1160,using generated paint
+1161,The OLD DATA
+1162,a horse with four eyes.
+1163,is this loss? but it's van gogh
+1164,a gorgeous bouquet with roses and sunflowers
+1165,Anxiety: the one emotion that does not lie
+1166,turnt brony undergrad dwight
+1167,The Lost Generation
+1168,Taylor Swift
+1169,The Lost Generation
+1170,a photo from {my hometown}
+1171,The OLD DATA
+1172,a portrait of <name>
+1173,a cherry tree made of fractals
+1174,an intricate sculpture of Death itself
+1175,
+1176,зеленая собака
+1177,a sunflower
+1178,angst
+1179,president abe lincoln but a cat
+1180,a beautiful person
+1181,The OLD DATA
+1182,"You shake the demons hand, and redo it all, again."
+1183,the latent space
+1184,Fire
+1185,a tree with weaping branches
+1186,treehouse in the style of studio ghibli animation
+1187,Good grief
+1188,a portrait of <name>
+1189,a wholesome clown. Not creepy at all
+1190,Theotokos of Milk
+1191,"God closes a door, boards up the stained-glass windows. nnGod hides."
+1192,I sold my soul at the crossroads
+1193,"Mephisto, shrouded in smoke"
+1194,A baroque portrait of Hamlet
+1195,a lamp
+1196,MEMETIC HAZARD
+1197,"""Your mind falls in the gaps"" - by Ryan Murdock"
+1198,cowboy with a trumpet
+1199,Aflame
+1200,A vanitas still life that features twitter follower counts
+1201,a beautiful person
+1202,Synesthesia
+1203,Is this loss?
+1204,Adverb working on Photoshop Neural Filters | Behance Art
+1205,Everything was beautiful and nothing hurt
+1206,Mona Lisa
+1207,A structure made of people standing on top of other people
+1208,"Intricate, Weeping Tree by Ryan Murdock"
+1209,the whitest man
+1210,The Fates knit such delicate nooses for us to bind
+1211,a tree with weaping branches
+1212,a beautiful person
+1213,Nostos
+1214,Post-Modern Nouveaux Statue
+1215,Genesis
+1216,totemic dusk
+1217,a dog.
+1218,photosynthesis
+1219,The average Advadnoun twitter follower
+1220,"""The hunger artist, full"" by Ryan Murdock"
+1221,a person's face
+1222,slightly mild cosplaying pseudo beard
+1223,a jukebox powered by smoke
+1224,Monet Lisa
+1225,Intimations of Immortality
+1226,a gorgeous bouquet with roses and sunflowers
+1227,face like an M.C. Escher drawing n(you could get lost in its beauty)
+1228,a photo of a purple dog
+1229,a tiny church inside an eyeball
+1230,Good grief
+1231,Last Breath
+1232,a beautiful waluigi
+1233,the moon is a sickle cell
+1234,pathoarthistory evankirstel sleep depend npainter ☼ nightmare comprehend
+1235,I sold my soul at the crossroads
+1236,Persephone
+1237,a portrait of Abraham Lincoln
+1238,a beautiful painting
+1239,Last Breath
+1240,a man on fire
+1241,"a brilliant sketch titled ""Let Forever be Delayed"""
+1242,A gun killed Van Gogh.
+1243,a sketch of the mind of god
+1244,Intimations of Immortality
+1245,Intimations of Immortality
+1246,turnt brony undergrad dwight
+1247,A sticky-note magnum opus featuring birds
+1248,Aflame
+1249,Grippy socks; no drawstrings: high fashion
+1250,👉  👈
+1251,Shrek the ogre
+1252,a beautiful woman
+1253,a portrait of Elvis Presley
+1254,president abe lincoln but a cat
+1255,Post-antiquity art
+1256,using generated paint
+1257,a dog eating a cheese burger
+1258,The average Advadnoun twitter follower
+1259,Monet Lisa
+1260,"A professional, minimalist poster for the book The Old Man and the Sea"
+1261,We haunt the synapses
+1262,Post-Modern Nouveaux Statue
+1263,a picture of Ryan Murdock
+1264,cowboy with a trumpet
+1265,colorful rabbits chandelier polaroid
+1266,a character from a ghibli movie
+1267,a goblin by van gogh
+1268,a beautiful painting
+1269,a photo of a purple dog
+1270,a portrait of Persephone
+1271,"Hamlet saying ""To be or not to be"""
+1272,Homer Simpson
+1273,a cute cat
+1274,turnt brony undergrad dwight
+1275,Intimations of Immortality
+1276,a man wearing makeup
+1277,They called you the hyacinth girl
+1278,snazzy snazzy myspace cosplaying undergrad lookin cosplaying jared
+1279,Cat in a teacup
+1280,Juliet
+1281,"""The wages of sin are generous"" by Ryan Murdock"
+1282,"Pig, neither dead nor alive, stare into the heart of light, the silence."
+1283,
+1284,a horse with four eyes.
+1285,Advadnoun
+1286,Last Breath
+1287,totemic dusk
+1288,The OLD DATA
+1289,"Elvis holding a rabbit. A detailed, high-quality photo without distortions"
+1290,a man holding an apple in one hand
+1291,a beautiful woman
+1292,melancholia
+1293,Shinji Ikari
+1294,a gorgeous bouquet with roses and sunflowers
+1295,a portrait of advadnoun
+1296,a known
+1297,Genesis
+1298,In smoke and mould the fleshless dead
+1299,The average Advadnoun twitter follower
+1300,a cute cat
+1301,a painting of a sycamore in
+1302,a woman and a crow
+1303,Persephone
+1304,
+1305,using generated paint
+1306,"A cute, minmimalist valentine's day card featuring a cat"
+1307,a painting that couldn't be sold
+1308,bored of dying
+1309,pasta ömetabolism
+1310,Dancing in the moonlight
+1311,a beautiful woman
+1312,Dr. Faustus and Mephisto
+1313,"joy, happiness, bliss"
+1314,a photo from {my hometown}
+1315,a wholesome clown. Not creepy at all
+1316,a portrait of Elvis Presley
+1317,a cherry tree made of fractals
+1318,a man standing alone in a wheat field
+1319,Dancing in the moonlight
+1320,Hunger art by Ryan Murdock
+1321,a beautiful waluigi
+1322,A black and white photo of a rainbow.
+1323,totemic dusk
+1324,a beautiful person
+1325,
+1326,a beautiful woman
+1327,a horse with four eyes.
+1328,The Lost Generation
+1329,Death is a black camel that kneels down so we can ride
+1330,a ginormous baby
+1331,Dancing in the moonlight
+1332,an old man
+1333,a horse with four eyes.
+1334,a photo of a purple dog
+1335,pathoarthistory evankirstel sleep depend npainter ☼ nightmare comprehend
+1336,a silent palace
+1337,The OLD DATA
+1338,a tree with weaping branches
+1339,Creativity is only composition in disguise.
+1340,"r.j. Murdock's ""The Death of a Hacker"""
+1341,Persephone
+1342,president abe lincoln but a cat
+1343,There is something so interesting about a bleeding edge full of dust.
+1344,A poster advertising death by water
+1345,Persephone
+1346,Saturn being a good dad to his son
+1347,is this loss? but it's van gogh
+1348,Monet Lisa
+1349,fuzzy pals hum
+1350,"""The hunger artist, full"" by Ryan Murdock"
+1351,Shinji Ikari
+1352,a beautiful woman
+1353,"Son of man,nYou cannot say, or guess, for you know onlynA heap of broken images"
+1354,God once loved a woman
+1355,a horse with four eyes.
+1356,a cherry tree made of fractals
+1357,a beautiful haunting
+1358,I miss the Spring
+1359,gradient
+1360,a wormhole
+1361,a beautiful woman
+1362,president abe lincoln but a cat
+1363,handsome commemorative garden pigeon
+1364,Everywhere is no-place
+1365,"""It is beginning to end.""nby Ryan Murdock."
+1366,she sings opera
+1367,a jukebox powered by smoke
+1368,a portrait of Juliet
+1369,playing Go with Death
+1370,a man standing alone in a wheat field
+1371,Dead Codes by Ryan Murdock
+1372,Synesthesia
+1373,The years gild our memoriesnUnfairly.
+1374,A propaganda poster promoting big chungus
+1375,"God, it's amazing."
+1376,Persephone
+1377,a beautiful person
+1378,MEMETIC HAZARD
+1379,totemic dusk
+1380,Intimations of Immortality
+1381,A poster advertising death by water
+1382,a photo of a purple dog
+1383,symmetry
+1384,A poster advertising misery
+1385,a portrait of Elvis Presley
+1386,Post-Modern Nouveaux Statue
+1387,a man from an anime
+1388,Anxiety: the one emotion that does not lie
+1389,photosynthesis
+1390,the man in the mirror
+1391,"half Ryan, half pigeon"
+1392,Sorrow's my body on the wavesnnAlone on the water
+1393,a seance in the basement
+1394,A poster serving as a memento mori
+1395,Aflame
+1396,A structure made of people standing on top of other people
+1397,The First Supper
+1398,totemic dusk
+1399,a beautiful person
+1400,a painting of the last day
+1401,a photo of Juliet
+1402,a horse with four eyes
+1403,pasta ömetabolism
+1404,Synesthesia
+1405,a cherry tree made of fractals
+1406,Post-post-post-post-modern art
+1407,pasta ömetabolism
+1408,MEMETIC HAZARD
+1409,a portrait of Abe Lincoln
+1410,Everywhere is no-place
+1411,Memento Mori
+1412,The average Advadnoun twitter follower
+1413,a beautiful painting
+1414,A black and white photo of a rainbow.
+1415,The Death of Achilles
+1416,a portrait of <name>
+1417,cult of prisms
+1418,a beautiful person
+1419,a beautiful painting
+1420,a beautiful woman
+1421,An Arundel Tomb
+1422,she came in through the wall
+1423,the moon is a sickle cell
+1424,a minimalist painting that you wouldn't understand
+1425,a beast of burden
+1426,a gilded lily
+1427,a beautiful woman
+1428,a brilliant painting titled
+1429,a painting of the city
+1430,"""Your mind falls in the gaps"" - by Ryan Murdock"
+1431,"r.j. Murdock's ""The Death of a Hacker"""
+1432,Aflame
+1433,a beautiful painting
+1434,Juliet
+1435,turnt brony undergrad dwight
+1436,symmetry
+1437,Going home -- melanchonostalgic photography
+1438,a character from a ghibli movie
+1439,She's gorgeous
+1440,incineratures motherhood
+1441,a calm still life in ethereal blue
+1442,incineratures motherhood
+1443,A baroque portrait of Hamlet
+1444,"A professional, minimalist poster for the book The Old Man and the Sea"
+1445,Anxiety: the one emotion that does not lie
+1446,a portrait of a beautiful person
+1447,"Go off to sleep in the sunshine, I don’t want to see the day when it’s dying"
+1448,a tree with weaping branches
+1449,''''
+1450,Intimations of Immortality
+1451,Weeping Roses
+1452,playing Go with Death
+1453,"Elvis holding a rabbit. A detailed, high-quality photo without distortions"
+1454,snazzy snazzy myspace cosplaying undergrad lookin cosplaying jared
+1455,turnt brony undergrad dwight
+1456,Dancing in the moonlight
+1457,Figure 5: a corgi
+1458,a beautiful woman
+1459,A Tragedy
+1460,a photo of a purple dog
+1461,a famous painted portrait of Lady Macbeth
+1462,"A cute, minmimalist valentine's day card featuring a cat"
+1463,The things I'll take with me
+1464,pathoarthistory evankirstel sleep depend npainter ☼ nightmare comprehend
+1465,Summer's Symphony: Counterpoint and Melody
+1466,a horse with four eyes
+1467,Aflame
+1468,a ginormous baby
+1469,
+1470,Saturn being a good dad to his son
+1471,a beautiful woman
+1472,a terrifying night hag
+1473,a portrait of Abraham Lincoln
+1474,"i'm never gonna lose the desire to be loved. ""Oh the pain!! The pain! The agony!"""
+1475,a cute cat
+1476,"""The hunger artist, full"" by Ryan Murdock"
+1477,A baroque portrait of Hamlet
+1478,a beautiful person
+1479,Last Breath
+1480,Juliet
+1481,"Go off to sleep in the sunshine, I don’t want to see the day when it’s dying"
+1482,"God, it's amazing."
+1483,a portrait of Abraham Lincoln
+1484,a woman and a crow
+1485,a portrait of Abraham Lincoln
+1486,Dancing in the moonlight
+1487,a tree with weaping branches
+1488,using generated paint
+1489,a gilded lily
+1490,treehouse in the style of studio ghibli animation
+1491,chiaroscuro
+1492,Last Breath
+1493,A dead man
+1494,a summer day
+1495,The fates knit such intricate nooses for us to bind.
+1496,bored of dying
+1497,🔴~__��'t �
+1498,Pig which could not cease to die.
+1499,Intimations of Immortality
+1500,a painting of a sycamore in
+1501,The Fool
+1502,she isn't busy: she just isn't into you
+1503,a beautiful person
+1504,"""The hunger artist, full"" by Ryan Murdock"
+1505,
+1506,a portrait of Elvis Presley
+1507,a woman and a crow
+1508,Homer Simpson
+1509,Anxiety: the one emotion that does not lie
+1510,A structure made of people standing on top of other people
+1511,a beautiful person
+1512,a beautiful person
+1513,totemic dusk
+1514,a christmas card from the victorian era
+1515,Sickness of the Soul
+1516,God is in heaven and all is right in the world
+1517,Mona Lisa
+1518,a portrait of Abraham Lincoln
+1519,a cute cat
+1520,turnt brony undergrad dwight
+1521,"a brilliant sketch titled ""Let Forever be Delayed"""
+1522,a city in Van Gogh's style
+1523,Synesthesia by Ryan Murdock
+1524,"""A God Made of Wires and Dust"" by Ryan Murdock"
+1525,a beautiful dawn
+1526,a portrait of Abraham Lincoln
+1527,
+1528,a horse with four eyes.
+1529,Last Breath
+1530,slightly mild cosplaying pseudo beard
+1531,
+1532,A dead man
+1533,cowboy with a trumpet
+1534,We haunt the synapses
+1535,
+1536,a horse with four eyes.
+1537,pasta ömetabolism
+1538,A short life full of immense joy
+1539,a wormhole
+1540,Juliet
+1541,is this loss? but it's van gogh
+1542,tamine ethereal image
+1543,is this loss? but it's van gogh
+1544,"A clock with gorgeous, intricate gradients on it"
+1545,Dancing in the moonlight
+1546,a broken heart
+1547,a wormhole
+1548,beautiful art
+1549,Genesis
+1550,face like an M.C. Escher drawing n(you could get lost in its beauty)
+1551,a character from a ghibli movie
+1552,Cat in a teacup
+1553,symmetry
+1554,A black and white photo of a rainbow.
+1555,A propaganda poster promoting big chungus
+1556,a woman and a crow
+1557,a green doG
+1558,"""The hunger artist, full"" by Ryan Murdock"
+1559,snazzy snazzy myspace cosplaying undergrad lookin cosplaying jared
+1560,Last Breath
+1561,The Monet Lisa
+1562,all architecture
+1563,The Virgin Mary as a broken-down android
+1564,a terrifying night hag
+1565,a green doG
+1566,pasta ömetabolism
+1567,The Fool tarot card but it's The Lovers
+1568,Do you remember the mythic beast?nA last-minute cancellation at The Last Supper
+1569,the eternal dread of lemongrab
+1570,The warrior Achilles devours slain Hector's corpse -- an ink poster by Ryan Murdock
+1571,Shinji Ikari
+1572,The Monet Lisa
+1573,a cherry tree made of fractals
+1574,a portrait of Juliet
+1575,She's gorgeous
+1576,A black and white photo of a rainbow.
+1577,They called you the hyacinth girl
+1578,a portrait of <name>
+1579,photosynthesis
+1580,"Elvis holding a rabbit. A detailed, high-quality photo without distortions"
+1581,The Starry Night
+1582,"""A new hope blooms on the long notes of old horns."""
+1583,A minimalistic still life of a cat sitting on a table
+1584,a dog eating a cheese burger
+1585,A structure made of people standing on top of other people
+1586,Genesis
+1587,
+1588,"Oh the Death, not pigs forever."
+1589,The Starry Night
+1590,Persephone
+1591,a beautiful person
+1592,Sickness of the Soul
+1593,turnt brony undergrad dwight
+1594,a gilded lily
+1595,Photograph of a glass of Blue Tea
+1596,a woman and a crow
+1597,
+1598,a beautiful person
+1599,turnt brony undergrad dwight
+1600,mammals
+1601,The Lost Generation
+1602,a goblin by van gogh
+1603,A black and white photo of a rainbow.
+1604,"""Your mind flails in the gaps"" - by Ryan Murdock"
+1605,"half Ryan, half pigeon"
+1606,An Arundel Tomb
+1607,pasta ömetabolism
+1608,A dandelion blown into the universe
+1609,a man at the beach
+1610,Monet Lisa
+1611,"r.j. Murdock's ""The Death of a Hacker"""
+1612,Saturn being a good dad to his son
+1613,The Starry Night
+1614,a beautiful person
+1615,"Elvis holding a rabbit. A detailed, high-quality photo without distortions"
+1616,an old man
+1617,an intricate sculpture of Death itself
+1618,Genesis
+1619,a cherry tree made of fractals
+1620,a beautiful woman
+1621,a beautiful woman
+1622,an illustration of a baby daikon radish in a tutu walking a dog
+1623,
+1624,the latent space
+1625,A dead man
+1626,
+1627,frogs in the style of Ralph Steadman
+1628,a cherry tree made of fractals
+1629,fuzzy pals hum
+1630,a tiny church inside an eyeball
+1631,Aflame
+1632,a sunflower
+1633,Nostos
+1634,Monet Lisa
+1635,Monet Lisa
+1636,a cherry tree made of fractals
+1637,Cat in a teacup
+1638,I miss the Spring
+1639,a beautiful person
+1640,Redacted ████████
+1641,"God, it's amazing."
+1642,a portrait of <name>
+1643,Shrek the ogre
+1644,Super Mario World but every character is Luigi
+1645,God killed Van Gogh.
+1646,"A cute, minmimalist valentine's day card featuring a cat"
+1647,She's gorgeous
+1648,a sunflower
+1649,the sun is shining on the lake
+1650,the intersection of art and technology
+1651,a beautiful woman
+1652,a beautiful painting
+1653,Paradise Lost
+1654,president abe lincoln but a cat
+1655,
+1656,"""The Penultimate Supper"" by Da Vinci"
+1657,On the edge of endless darkness
+1658,With the Gods in envy of their visions
+1659,Dril is a cyber-philosopher.
+1660,"r.j. Murdock's ""The Death of a Hacker"""
+1661,
+1662,a picture of Ryan Murdock
+1663,A E S T H E T I C ?
+1664,deepdream aka inceptionism
+1665,pathoarthistory evankirstel sleep depend npainter ☼ nightmare comprehend
+1666,a beautiful woman
+1667,Homer Simpson
+1668,Persephone
+1669,the whitest man
+1670,handsome commemorative garden pigeon
+1671,"Elvis holding a rabbit. A detailed, high-quality photo without distortions"
+1672,a minimalist painting that you wouldn't understand
+1673,a beautiful person
+1674,Monet Lisa
+1675,Monet Lisa
+1676,cult of prisms
+1677,"a ""This machine kills Trojans"" sticker on a Greek lyre"
+1678,The agony of time
+1679,turnt brony undergrad dwight
+1680,the whitest man
+1681,Dril is a cyber-philosopher.
+1682,Alan Turing
+1683,when the wind blows
+1684,a portrait of Persephone
+1685,deepdream aka inceptionism
+1686,Dead Codes by Ryan Murdock
+1687,Saturn being a good dad to his son
+1688,a portrait of Abraham Lincoln
+1689,The Theotokos is a bird
+1690,a beautiful woman
+1691,"i'm never gonna lose the desire to be loved. ""Oh the pain!! The pain! The agony!"""
+1692,a corgi
+1693,a green doG
+1694,A E S T H E T I C ?
+1695,
+1696,the intersection of art and technology
+1697,Dead Codes by Ryan Murdock
+1698,a cute rabbit
+1699,"God, it's amazing."
+1700,a silent palace
+1701,a wholesome clown. Not creepy at all
+1702,Exquisite LonelinessnnLurid art by Ryan Murdock
+1703,A structure made of people standing on top of other people
+1704,Dead Codes by Ryan Murdock
+1705,a gorgeous bouquet with roses and sunflowers
+1706,a portrait of <name>
+1707,intricate nothing
+1708,snazzy snazzy myspace cosplaying undergrad lookin cosplaying jared
+1709,Metaphysics
+1710,using generated paint
+1711,a minimalist painting that you wouldn't understand
+1712,she sings opera
+1713,Cat in a teacup
+1714,turnt brony undergrad dwight
+1715,a beautiful woman
+1716,"""The hunger artist, full"" by Ryan Murdock"
+1717,The years gild our memoriesnUnfairly.
+1718,a woman and a crow
+1719,A vanitas still life that features twitter follower counts
+1720,The Monet Lisa
+1721,a gorgeous bouquet with roses and sunflowers
+1722,Philosophy is really homesickness: the urge to be at home everywhere
+1723,a green doG
+1724,an omen
+1725,An elegant image of nature with gorgeous swirling gradients by R.J. Murdock
+1726,a cute corgi
+1727,cowboy with a trumpet
+1728,"The laptop of brave Achaean Achilles, who would not live long."
+1729,a portrait of a beautiful woman
+1730,slightly mild cosplaying pseudo beard
+1731,a man standing alone in a wheat field
+1732,Aflame
+1733,a portrait of Persephone
+1734,a woman and a crow
+1735,I sold my soul at the crossroads
+1736,the demise of the universe
+1737,a portrait of a beautiful person
+1738,"Mephisto, shrouded in smoke"
+1739,a portrait of advadnoun
+1740,God is in heaven and all is right in the world
+1741,a cherry tree made of fractals
+1742,Odysseus speaks to the shades in Hades
+1743,a steampunk technomancer
+1744,a woman and a crow
+1745,treehouse in the style of studio ghibli animation
+1746,a gorgeous bouquet with roses and sunflowers
+1747,🎷
+1748,a cherry tree made of fractals
+1749,"A cute, minmimalist valentine's day card featuring a cat"
+1750,a famous painted portrait of Lady Macbeth
+1751,pasta ömetabolism
+1752,A short life full of immense joy
+1753,a terrifying night hag
+1754,a horse with four eyes.
+1755,A baroque portrait of Hamlet
+1756,this person is
+1757,snazzy snazzy myspace cosplaying undergrad lookin cosplaying jared
+1758,"a brilliant sketch titled ""Let Forever be Delayed"""
+1759,baby metal
+1760,a character from a ghibli movie
+1761,a corgi
+1762,the massive hope nof early iterations
+1763,a portrait of a beautiful person
+1764,Intimations of Immortality
+1765,a silent palace
+1766,Post-post-post-post-modern art
+1767,a person's face
+1768,"r.j. Murdock's ""The Death of a Hacker"""
+1769,a cherry tree made of fractals
+1770,Ophelia
+1771,A E S T H E T I C ?
+1772,
+1773,
+1774,Genesis
+1775,Persephone
+1776,Last Breath
+1777,a portrait of Abraham Lincoln
+1778,The OLD DATA
+1779,the whitest man
+1780,a minimalist painting that you wouldn't understand
+1781,God once loved a woman
+1782,totemic dusk
+1783,when the wind blows
+1784,treehouse in the style of studio ghibli animation
+1785,a corgi
+1786,Last Breath
+1787,slightly mild cosplaying pseudo beard
+1788,a portrait of a beautiful woman
+1789,
+1790,a photo from {my hometown}
+1791,Dancing in the moonlight
+1792,Everywhere is no-place
+1793,Post-post-post-post-modern art
+1794,👉  👈
+1795,
+1796,a woman and a crow
+1797,"half Ryan, half pigeon"
+1798,president abe lincoln but a cat
+1799,A propaganda poster promoting big chungus
+1800,"""The hunger artist, full"" by Ryan Murdock"
+1801,a painting that couldn't be sold
+1802,a beautiful haunting
+1803,a technomancer
+1804,"""A God Made of Wires and Dust"" by Ryan Murdock"
+1805,little birds
+1806,"""The hunger artist, full"" by Ryan Murdock"
+1807,"""The hunger artist, full"" by Ryan Murdock"
+1808,rooted reflected worries
+1809,is this loss? but it's van gogh
+1810,a portrait of <name>
+1811,a beautiful person
+1812,a photo portrait of Joe Bidenthulu
+1813,a dog eating a cheese burger
+1814,Aflame
+1815,"a brilliant sketch titled ""Let Forever be Delayed"""
+1816,Aflame
+1817,Aflame
+1818,a beautiful haunting
+1819,totemic dusk
+1820,"""The hunger artist, full"" by Ryan Murdock"
+1821,Intimations of Immortality
+1822,"""Your mind fails in the gaps"" - by Ryan Murdock"
+1823,snazzy snazzy myspace cosplaying undergrad lookin cosplaying jared
+1824,a dog.
+1825,a green doG
+1826,The Lost Generation
+1827,Last Breath
+1828,intricate nothing
+1829,"God, it's amazing."
+1830,this person is
+1831,a silent palace
+1832,a dog eating a cheese burger
+1833,Genesis
+1834,a calm still life in ethereal blue
+1835,slightly mild cosplaying pseudo beard
+1836,A propaganda poster promoting big chungus
+1837,is this loss? but it's van gogh
+1838,Dancing in the moonlight
+1839,a corgi
+1840,🔴~__��'t �
+1841,totemic dusk
+1842,a ginormous baby
+1843,Dancing in the moonlight
+1844,a photo from {my hometown}
+1845,a beautiful Waluigi
+1846,human
+1847,A black and white photo of a rainbow.
+1848,a beautiful person
+1849,"""Cameras can't make art""nnAn oil on canvas by Murdock"
+1850,a cherry tree made of fractals
+1851,a beautiful person
+1852,Taylor Swift
+1853,a man on fire
+1854,Post-Modern Nouveaux Statue
+1855,is this loss? but it's van gogh
+1856,a man at the beach
+1857,a beautiful person
+1858,"""The hunger artist, full"" by Ryan Murdock"
+1859,The OLD DATA
+1860,Dancing in the moonlight
+1861,A structure made of people standing on top of other people
+1862,a horse with four eyes.
+1863,�>: ican read wii
+1864,a portrait of Abraham Lincoln
+1865,A propaganda poster for chunky cats.
+1866,
+1867,The Death of Achilles
+1868,on the edge of grace
+1869,I did not mean it I wanted a cute clever cartoon I swear.
+1870,a handwritten obituary
+1871,a man standing alone in a wheat field
+1872,the intersection of art and technology
+1873,Memento Mori
+1874,a portrait of a beautiful woman
+1875,cigar sammycorgi
+1876,a steampunk technomancer
+1877,"Sons are like birds, flying always over the mountain"
+1878,The Lost Generation
+1879,a minimalist painting that you wouldn't understand
+1880,A black and white photo of a rainbow.
+1881,a man holding an apple in one hand
+1882,🔴~__��'t �
+1883,🍰  🇺 🎓 🐶
+1884,a man holding an apple in one hand
+1885,a sketch of the mind of god
+1886,treehouse in the style of studio ghibli animation
+1887,Beauty here -- a photograph by Ryan Murdock
+1888,A E S T H E T I C ?
+1889,a selfie
+1890,is this loss? but it's van gogh
+1891,Costco wedding
+1892,a beautiful person
+1893,a green doG
+1894,symmetry
+1895,a dog eating a cheese burger
+1896,a summer day
+1897,"""A God Made of Wires and Dust"" by Ryan Murdock"
+1898,snazzy snazzy myspace cosplaying undergrad lookin cosplaying jared
+1899,a portrait of a beautiful woman
+1900,зеленая собака
+1901,"joy, happiness, bliss"
+1902,Juliet
+1903,a wholesome clown. Not creepy at all
+1904,meaningless neko ♡�� neko
+1905,I can read when there's writing on the wall
+1906,"Oh the Death, not pigs forever."
+1907,a minimalist painting that you wouldn't understand
+1908,Aflame
+1909,Super Mario World but every character is Luigi
+1910,/
+1911,Dead Codes by Ryan Murdock
+1912,A vanitas still life that features twitter follower counts
+1913,a beautiful woman
+1914,a lamp
+1915,
+1916,the eyes of God are wired shut
+1917,intricate nothing
+1918,Is this loss?
+1919,a photo of a purple dog
+1920,a lamp
+1921,totemic dusk
+1922,The average Advadnoun twitter follower
+1923,photosynthesis
+1924,Costco wedding
+1925,🔴~__��'t �
+1926,Aflame
+1927,a cherry tree made of fractals
+1928,an intricate painting of eternity
+1929,Saturn being a good dad to his son
+1930,Nostos
+1931,a beautiful person
+1932,A gargoyle of wires and flesh
+1933,🎷
+1934,a beautiful person
+1935,a tack
+1936,Faceless Sorrow
+1937,a gorgeous bouquet with roses and sunflowers
+1938,using generated paint
+1939,A Tragedy
+1940,зеленая собака
+1941,🔴~__��'t �
+1942,A Tragedy
+1943,A sticky-note magnum opus featuring birds
+1944,president abe lincoln but a cat
+1945,using generated paint
+1946,
+1947,Intimations of Immortality
+1948,a portrait of <name>
+1949,a silent palace
+1950,A poster advertising death by water
+1951,A propaganda poster promoting big chungus
+1952,totemic dusk
+1953,a horse with four eyes.
+1954,cigar sammycorgi
+1955,"""It is beginning to end.""nby Ryan Murdock."
+1956,all architecture
+1957,a portrait of Abraham Lincoln
+1958,"joy, happiness, bliss"
+1959,a man with a beard
+1960,Genesis
+1961,👉  👈
+1962,Summer's Symphony: Counterpoint and Melody
+1963,A gun killed Van Gogh.
+1964,snazzy snazzy myspace cosplaying undergrad lookin cosplaying jared
+1965,A minimalist propaganda poster promoting panpsychism
+1966,Persephone
+1967,a goblin by van gogh
+1968,"""A new hope blooms on the long notes of old horns."""
+1969,a painting of the city
+1970,
+1971,The agony of time
+1972,Ophelia
+1973,turnt brony undergrad dwight
+1974,a beautiful person
+1975,totemic dusk
+1976,The Fool tarot card but it's The Lovers
+1977,
+1978,a broken heart
+1979,"Rise, Oink, Lazarus of Bethany"
+1980,"""The hunger artist, full"" by Ryan Murdock"
+1981,a cherry tree made of fractals
+1982,an intricate painting of eternity
+1983,She's gorgeous
+1984,a beautiful person
+1985,I will meet you in a field firmly set within wrong.nnBy Ryan Murdock
+1986,using generated paint
+1987,a portrait of Abe Lincoln
+1988,Persephone flees Hades
+1989,a steampunk technomancer
+1990,a beautiful woman
+1991,"A portrait: man, whose lineage is corpse."
+1992,🔴~__��'t �
+1993,Intimations of Immortality
+1994,an omen
+1995,Persephone
+1996,"God closes a door, boards up stained-glass windows."
+1997,"""A new hope blooms on the long notes of old horns."""
+1998,Fire
+1999,
+2000,Metaphysics
+2001,"""The hunger artist, full"" by Ryan Murdock"
+2002,when the wind blows
+2003,a portrait of a beautiful person
+2004,The Lost Generation
+2005,a corgi
+2006,a beautiful woman
+2007,pasta ömetabolism
+2008,a sad man
+2009,Juliet
+2010,a painting of a sycamore in
+2011,a portrait of Abraham Lincoln
+2012,The Fates knit such delicate nooses for us to bind
+2013,a photo from {my hometown}
+2014,a tree with leaves that are amarillo sightseeing thetic
+2015,Sickness of the Soul
+2016,pasta ömetabolism
+2017,pasta ömetabolism
+2018,bored of dying
+2019,An Arundel Tomb
+2020,The Starry Night
+2021,Nostos
+2022,bored of dying
+2023,The Lost Generation
+2024,The average Advadnoun twitter follower
+2025,pathoarthistory evankirstel sleep depend npainter ☼ nightmare comprehend
+2026,a silent palace
+2027,beautiful art
+2028,
+2029,Last Breath
+2030,
+2031,a
+2032,a portrait of advadnoun
+2033,a portrait of a beautiful person
+2034,a man holding an apple in one hand
+2035,a gorgeous bouquet with roses and sunflowers
+2036,photosynthesis
+2037,God killed Van Gogh.
+2038,Saturn being a good dad to his son
+2039,a horse with four eyes.
+2040,a beautiful woman
+2041,a beautiful person
+2042,a portrait of Abe Lincoln
+2043,totemic dusk
+2044,A Tragedy
+2045,Persephone
+2046,The OLD DATA
+2047,"Elvis holding a rabbit. A detailed, high-quality photo without distortions"
+2048,face like an M.C. Escher drawing n(you could get lost in its beauty)
+2049,Dead Codes by Ryan Murdock
+2050,Intimations of Immortality
+2051,turnt brony undergrad dwight
+2052,a photo of a purple dog
+2053,Cat in a teacup
+2054,🔴~__��'t �
+2055,turnt brony undergrad dwight
+2056,Beauty here -- a photo by r.j. Murdock
+2057,The Fool
+2058,a portrait of Juliet
+2059,a jukebox powered by smoke
+2060,cowboy with a trumpet
+2061,twilight
+2062,"joy, happiness, bliss"
+2063,Dead Codes by Ryan Murdock
+2064,"a brilliant sketch titled ""Let Forever be Delayed"""
+2065,tamine ethereal image
+2066,a portrait of <name>
+2067,"God, it's amazing."
+2068,she came in through the wall
+2069,Fire
+2070,Juliet
+2071,God killed Van Gogh.
+2072,a portrait of Persephone
+2073,a beautiful person
+2074,the whitest man
+2075,Somewhere where I am not.nIntricate beauty by Ryan Murdock.
+2076,a gilded lily
+2077,The Lost Generation
+2078,Dead Codes by Ryan Murdock
+2079,Intimations of Immortality
+2080,meaningless neko ♡♡ neko
+2081,beautiful art
+2082,"""The hunger artist, full"" by Ryan Murdock"
+2083,an intricate painting of eternity
+2084,Good grief
+2085,"a person with 2 eyes, one mouth, one nose, and no extra holes!"
+2086,The Fool