latest

app.py

@@ -58,7 +58,7 @@ def generate_gpu(in_im_embs, prompt='the scene'):
             num_inference_steps=50,
             image_embeds=positive_image_embeds,
             negative_image_embeds=negative_image_embeds,
-            guidance_scale=11,
+            guidance_scale=15,
         ).images[0]
         cond = (
                     model.prior_pipe.image_processor(images, return_tensors="pt")
@@ -101,17 +101,17 @@ def sample_embs(prompt_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:
+    if len(positives) == 0:
         positives = torch.zeros_like(im_emb)[None]
     else:
+        embs = random.sample(positives, min(4, len(positives))) + positives[-4:]
         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:
+    if len(negs) == 0:
         negatives = torch.zeros_like(im_emb)[None]
     else:
+        negative_embs = random.sample(negs, min(4, len(negs))) + negs[-4:]
         negatives = torch.stack(negative_embs, 1)
 
     image_embeds = torch.stack([sample_embs(negatives), sample_embs(positives)])
@@ -186,12 +186,8 @@ def pluck_img(user_id):
         time.sleep(.1)
         # TODO optimize this lol
 
+    # NOTE could opt for only showing their own or prioritizing their own media.
     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():
@@ -390,7 +386,7 @@ Explore the latent space using binary feedback.
                  [b1, b2, b3, b4, b5, b6, img, calibrate_prompts, user_id, ]
                  )
     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>
+        html = gr.HTML('''<div style='text-align:center; font-size:20px'>You will calibrate for several images and then roam. When your media is generating, you may encounter others'.</ 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.

last_epoch_ckpt/diffusion_pytorch_model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:91ca25967a5dd1665b5fb8f1b4a45ba0f7ad0c23929daefb26b267816616e05d
+oid sha256:d65a902c101345526b244420a5e6f495a947909db28015840afa9bacd557936b
 size 136790920

prior/pipeline_kandinsky_prior.py

@@ -401,6 +401,7 @@ class KandinskyPriorPipeline(DiffusionPipeline):
     def __call__(
         self,
         prompt: Union[str, List[str]],
+        k,
         negative_prompt: Optional[Union[str, List[str]]] = None,
         num_images_per_prompt: int = 1,
         num_inference_steps: int = 25,
@@ -471,11 +472,11 @@ class KandinskyPriorPipeline(DiffusionPipeline):
                 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}"
+        if prompt_embeds.shape[1] < k:
+            prompt_embeds = torch.nn.functional.pad(prompt_embeds, [0, 0, 0, k-prompt_embeds.shape[1]])
+        assert prompt_embeds.shape[1] == k, 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
+        prompt_embeds = prompt_embeds.to('cuda')
 
         hidden_states = torch.randn(
             (batch_size, prompt_embeds.shape[-1]),
@@ -495,7 +496,25 @@ class KandinskyPriorPipeline(DiffusionPipeline):
 
         # 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)
+            # zero_embeds = self.get_zero_embed(latents.shape[0], device=latents.device)
+
+            # using the same hidden states or different hidden states?
+            
+            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=torch.zeros_like(prompt_embeds),
+                encoder_hidden_states=torch.zeros_like(prompt_embeds),
+                attention_mask=text_mask,
+            ).predicted_image_embedding
+
+            zero_embeds = latents
 
             if (
                 hasattr(self, "final_offload_hook")

train.py

@@ -1,94 +1 @@
-
-
-########################################
-# 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()
+### See prefererence prior github for training code here: https://github.com/rynmurdock/preference-prior

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
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-laion-clap==1.1.4
-langcodes==3.3.0
-lark==1.1.2
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-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
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-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
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-preshed==3.0.8
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-protobuf==4.25.3
-psutil==5.9.5
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-py-cpuinfo==9.0.0
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-pycosat @ file:///croot/pycosat_1696536503704/work
-pycparser==2.21
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-pyparsing==3.1.1
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-pyre-extensions==0.0.29
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-python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work
-python-docx==0.8.11
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-pytorch-lantern==0.12.7
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-pytz @ file:///croot/pytz_1695131579487/work
-pyu2f==0.1.5
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-QtPy @ file:///croot/qtpy_1700144840038/work
-randomname==0.2.1
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-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
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-resampy==0.4.3
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-rsa==4.7.2
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-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
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-sacremoses==0.0.53
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-salesforce-lavis @ git+https://github.com/salesforce/LAVIS.git@4a85b17846ee62f09c40f37cc955dd33c2abec68
-scikit-image==0.20.0
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-sentencepiece==0.1.99
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-sgm @ file:///home/ryn_mote/Misc/generative-models
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-six @ file:///tmp/build/80754af9/six_1644875935023/work
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-spaces==0.27.0
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-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
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-sympy @ file:///croot/sympy_1701397643339/work
-tables==3.9.2
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-torch==2.2.2+cu118
-torch-ema==0.3
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-torchaudio==2.0.2+cu118
-torchdiffeq==0.2.3
-torchio==0.19.0
-torchlibrosa==0.1.0
-torchmetrics==0.11.4
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-tqdm==4.66.5
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-traits==6.4.1
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-tzdata @ file:///croot/python-tzdata_1690578112552/work
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-undetected-chromedriver==3.5.5
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-zstandard @ file:///croot/zstandard_1677013143055/work