"
+ os.environ["MASTER_ADDR"] = split[0]
+ os.environ["MASTER_PORT"] = split[1]
+
+ # perform a dummy all-reduce to initialize the NCCL communicator
+ dist.all_reduce(torch.zeros(1).cuda())
+
+ suppress_output(is_master())
+ args.rank = dist.get_rank()
+ return args.rank
+
+
+def get_rank():
+ if not dist.is_available():
+ return 0
+ if not dist.is_nccl_available():
+ return 0
+ if not dist.is_initialized():
+ return 0
+ return dist.get_rank()
+
+
+def is_master():
+ return get_rank() == 0
+
+
+def synchronize():
+ if dist.is_initialized():
+ dist.barrier()
+
+
+def suppress_output(is_master):
+ """Suppress printing on the current device. Force printing with `force=True`."""
+ import builtins as __builtin__
+
+ builtin_print = __builtin__.print
+
+ def print(*args, **kwargs):
+ force = kwargs.pop("force", False)
+ if is_master or force:
+ builtin_print(*args, **kwargs)
+
+ __builtin__.print = print
+
+ import warnings
+
+ builtin_warn = warnings.warn
+
+ def warn(*args, **kwargs):
+ force = kwargs.pop("force", False)
+ if is_master or force:
+ builtin_warn(*args, **kwargs)
+
+ # Log warnings only once
+ warnings.warn = warn
+ warnings.simplefilter("once", UserWarning)
\ No newline at end of file
diff --git a/magicanimate/utils/util.py b/magicanimate/utils/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..76f2245cf13367694ad00faf3be721de70ff57bd
--- /dev/null
+++ b/magicanimate/utils/util.py
@@ -0,0 +1,138 @@
+# *************************************************************************
+# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
+# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
+# ytedance Inc..
+# *************************************************************************
+
+# Adapted from https://github.com/guoyww/AnimateDiff
+import os
+import imageio
+import numpy as np
+
+import torch
+import torchvision
+
+from PIL import Image
+from typing import Union
+from tqdm import tqdm
+from einops import rearrange
+
+
+def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=25):
+ videos = rearrange(videos, "b c t h w -> t b c h w")
+ outputs = []
+ for x in videos:
+ x = torchvision.utils.make_grid(x, nrow=n_rows)
+ x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
+ if rescale:
+ x = (x + 1.0) / 2.0 # -1,1 -> 0,1
+ x = (x * 255).numpy().astype(np.uint8)
+ outputs.append(x)
+
+ os.makedirs(os.path.dirname(path), exist_ok=True)
+ imageio.mimsave(path, outputs, fps=fps)
+
+def save_images_grid(images: torch.Tensor, path: str):
+ assert images.shape[2] == 1 # no time dimension
+ images = images.squeeze(2)
+ grid = torchvision.utils.make_grid(images)
+ grid = (grid * 255).numpy().transpose(1, 2, 0).astype(np.uint8)
+ os.makedirs(os.path.dirname(path), exist_ok=True)
+ Image.fromarray(grid).save(path)
+
+# DDIM Inversion
+@torch.no_grad()
+def init_prompt(prompt, pipeline):
+ uncond_input = pipeline.tokenizer(
+ [""], padding="max_length", max_length=pipeline.tokenizer.model_max_length,
+ return_tensors="pt"
+ )
+ uncond_embeddings = pipeline.text_encoder(uncond_input.input_ids.to(pipeline.device))[0]
+ text_input = pipeline.tokenizer(
+ [prompt],
+ padding="max_length",
+ max_length=pipeline.tokenizer.model_max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+ text_embeddings = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0]
+ context = torch.cat([uncond_embeddings, text_embeddings])
+
+ return context
+
+
+def next_step(model_output: Union[torch.FloatTensor, np.ndarray], timestep: int,
+ sample: Union[torch.FloatTensor, np.ndarray], ddim_scheduler):
+ timestep, next_timestep = min(
+ timestep - ddim_scheduler.config.num_train_timesteps // ddim_scheduler.num_inference_steps, 999), timestep
+ alpha_prod_t = ddim_scheduler.alphas_cumprod[timestep] if timestep >= 0 else ddim_scheduler.final_alpha_cumprod
+ alpha_prod_t_next = ddim_scheduler.alphas_cumprod[next_timestep]
+ beta_prod_t = 1 - alpha_prod_t
+ next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
+ next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
+ next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction
+ return next_sample
+
+
+def get_noise_pred_single(latents, t, context, unet):
+ noise_pred = unet(latents, t, encoder_hidden_states=context)["sample"]
+ return noise_pred
+
+
+@torch.no_grad()
+def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt):
+ context = init_prompt(prompt, pipeline)
+ uncond_embeddings, cond_embeddings = context.chunk(2)
+ all_latent = [latent]
+ latent = latent.clone().detach()
+ for i in tqdm(range(num_inv_steps)):
+ t = ddim_scheduler.timesteps[len(ddim_scheduler.timesteps) - i - 1]
+ noise_pred = get_noise_pred_single(latent, t, cond_embeddings, pipeline.unet)
+ latent = next_step(noise_pred, t, latent, ddim_scheduler)
+ all_latent.append(latent)
+ return all_latent
+
+
+@torch.no_grad()
+def ddim_inversion(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt=""):
+ ddim_latents = ddim_loop(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt)
+ return ddim_latents
+
+
+def video2images(path, step=4, length=16, start=0):
+ reader = imageio.get_reader(path)
+ frames = []
+ for frame in reader:
+ frames.append(np.array(frame))
+ frames = frames[start::step][:length]
+ return frames
+
+
+def images2video(video, path, fps=8):
+ imageio.mimsave(path, video, fps=fps)
+ return
+
+
+tensor_interpolation = None
+
+def get_tensor_interpolation_method():
+ return tensor_interpolation
+
+def set_tensor_interpolation_method(is_slerp):
+ global tensor_interpolation
+ tensor_interpolation = slerp if is_slerp else linear
+
+def linear(v1, v2, t):
+ return (1.0 - t) * v1 + t * v2
+
+def slerp(
+ v0: torch.Tensor, v1: torch.Tensor, t: float, DOT_THRESHOLD: float = 0.9995
+) -> torch.Tensor:
+ u0 = v0 / v0.norm()
+ u1 = v1 / v1.norm()
+ dot = (u0 * u1).sum()
+ if dot.abs() > DOT_THRESHOLD:
+ #logger.info(f'warning: v0 and v1 close to parallel, using linear interpolation instead.')
+ return (1.0 - t) * v0 + t * v1
+ omega = dot.acos()
+ return (((1.0 - t) * omega).sin() * v0 + (t * omega).sin() * v1) / omega.sin()
\ No newline at end of file
diff --git a/magicanimate/utils/videoreader.py b/magicanimate/utils/videoreader.py
new file mode 100644
index 0000000000000000000000000000000000000000..e26a37e01a1deb223e0ca8d24beb306d1703d01e
--- /dev/null
+++ b/magicanimate/utils/videoreader.py
@@ -0,0 +1,157 @@
+# *************************************************************************
+# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
+# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
+# ytedance Inc..
+# *************************************************************************
+
+# Copyright 2022 ByteDance and/or its affiliates.
+#
+# Copyright (2022) PV3D Authors
+#
+# ByteDance, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from ByteDance or
+# its affiliates is strictly prohibited.
+import av, gc
+import torch
+import warnings
+import numpy as np
+
+
+_CALLED_TIMES = 0
+_GC_COLLECTION_INTERVAL = 20
+
+
+# remove warnings
+av.logging.set_level(av.logging.ERROR)
+
+
+class VideoReader():
+ """
+ Simple wrapper around PyAV that exposes a few useful functions for
+ dealing with video reading. PyAV is a pythonic binding for the ffmpeg libraries.
+ Acknowledgement: Codes are borrowed from Bruno Korbar
+ """
+ def __init__(self, video, num_frames=float("inf"), decode_lossy=False, audio_resample_rate=None, bi_frame=False):
+ """
+ Arguments:
+ video_path (str): path or byte of the video to be loaded
+ """
+ self.container = av.open(video)
+ self.num_frames = num_frames
+ self.bi_frame = bi_frame
+
+ self.resampler = None
+ if audio_resample_rate is not None:
+ self.resampler = av.AudioResampler(rate=audio_resample_rate)
+
+ if self.container.streams.video:
+ # enable multi-threaded video decoding
+ if decode_lossy:
+ warnings.warn('VideoReader| thread_type==AUTO can yield potential frame dropping!', RuntimeWarning)
+ self.container.streams.video[0].thread_type = 'AUTO'
+ self.video_stream = self.container.streams.video[0]
+ else:
+ self.video_stream = None
+
+ self.fps = self._get_video_frame_rate()
+
+ def seek(self, pts, backward=True, any_frame=False):
+ stream = self.video_stream
+ self.container.seek(pts, any_frame=any_frame, backward=backward, stream=stream)
+
+ def _occasional_gc(self):
+ # there are a lot of reference cycles in PyAV, so need to manually call
+ # the garbage collector from time to time
+ global _CALLED_TIMES, _GC_COLLECTION_INTERVAL
+ _CALLED_TIMES += 1
+ if _CALLED_TIMES % _GC_COLLECTION_INTERVAL == _GC_COLLECTION_INTERVAL - 1:
+ gc.collect()
+
+ def _read_video(self, offset):
+ self._occasional_gc()
+
+ pts = self.container.duration * offset
+ time_ = pts / float(av.time_base)
+ self.container.seek(int(pts))
+
+ video_frames = []
+ count = 0
+ for _, frame in enumerate(self._iter_frames()):
+ if frame.pts * frame.time_base >= time_:
+ video_frames.append(frame)
+ if count >= self.num_frames - 1:
+ break
+ count += 1
+ return video_frames
+
+ def _iter_frames(self):
+ for packet in self.container.demux(self.video_stream):
+ for frame in packet.decode():
+ yield frame
+
+ def _compute_video_stats(self):
+ if self.video_stream is None or self.container is None:
+ return 0
+ num_of_frames = self.container.streams.video[0].frames
+ if num_of_frames == 0:
+ num_of_frames = self.fps * float(self.container.streams.video[0].duration*self.video_stream.time_base)
+ self.seek(0, backward=False)
+ count = 0
+ time_base = 512
+ for p in self.container.decode(video=0):
+ count = count + 1
+ if count == 1:
+ start_pts = p.pts
+ elif count == 2:
+ time_base = p.pts - start_pts
+ break
+ return start_pts, time_base, num_of_frames
+
+ def _get_video_frame_rate(self):
+ return float(self.container.streams.video[0].guessed_rate)
+
+ def sample(self, debug=False):
+
+ if self.container is None:
+ raise RuntimeError('video stream not found')
+ sample = dict()
+ _, _, total_num_frames = self._compute_video_stats()
+ offset = torch.randint(max(1, total_num_frames-self.num_frames-1), [1]).item()
+ video_frames = self._read_video(offset/total_num_frames)
+ video_frames = np.array([np.uint8(f.to_rgb().to_ndarray()) for f in video_frames])
+ sample["frames"] = video_frames
+ sample["frame_idx"] = [offset]
+
+ if self.bi_frame:
+ frames = [np.random.beta(2, 1, size=1), np.random.beta(1, 2, size=1)]
+ frames = [int(frames[0] * self.num_frames), int(frames[1] * self.num_frames)]
+ frames.sort()
+ video_frames = np.array([video_frames[min(frames)], video_frames[max(frames)]])
+ Ts= [min(frames) / (self.num_frames - 1), max(frames) / (self.num_frames - 1)]
+ sample["frames"] = video_frames
+ sample["real_t"] = torch.tensor(Ts, dtype=torch.float32)
+ sample["frame_idx"] = [offset+min(frames), offset+max(frames)]
+ return sample
+
+ return sample
+
+ def read_frames(self, frame_indices):
+ self.num_frames = frame_indices[1] - frame_indices[0]
+ video_frames = self._read_video(frame_indices[0]/self.get_num_frames())
+ video_frames = np.array([
+ np.uint8(video_frames[0].to_rgb().to_ndarray()),
+ np.uint8(video_frames[-1].to_rgb().to_ndarray())
+ ])
+ return video_frames
+
+ def read(self):
+ video_frames = self._read_video(0)
+ video_frames = np.array([np.uint8(f.to_rgb().to_ndarray()) for f in video_frames])
+ return video_frames
+
+ def get_num_frames(self):
+ _, _, total_num_frames = self._compute_video_stats()
+ return total_num_frames
\ No newline at end of file
diff --git a/packages.txt b/packages.txt
new file mode 100644
index 0000000000000000000000000000000000000000..20645e641240cb419f5fc66c14c1447e91daf669
--- /dev/null
+++ b/packages.txt
@@ -0,0 +1 @@
+ffmpeg
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..82f59c87c395ea9454a2578f8b5e84cd361f4342
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,127 @@
+absl-py==1.4.0
+accelerate==0.22.0
+aiofiles==23.2.1
+aiohttp==3.8.5
+aiosignal==1.3.1
+altair==5.0.1
+annotated-types==0.5.0
+antlr4-python3-runtime==4.9.3
+anyio==3.7.1
+async-timeout==4.0.3
+attrs==23.1.0
+cachetools==5.3.1
+certifi==2023.7.22
+charset-normalizer==3.2.0
+click==8.1.7
+cmake==3.27.2
+contourpy==1.1.0
+cycler==0.11.0
+datasets==2.14.4
+dill==0.3.7
+einops==0.6.1
+exceptiongroup==1.1.3
+fastapi==0.103.0
+ffmpy==0.3.1
+filelock==3.12.2
+fonttools==4.42.1
+frozenlist==1.4.0
+fsspec==2023.6.0
+google-auth==2.22.0
+google-auth-oauthlib==1.0.0
+gradio==3.41.2
+gradio-client==0.5.0
+grpcio==1.57.0
+h11==0.14.0
+httpcore==0.17.3
+httpx==0.24.1
+huggingface-hub==0.16.4
+idna==3.4
+importlib-metadata==6.8.0
+importlib-resources==6.0.1
+jinja2==3.1.2
+joblib==1.3.2
+jsonschema==4.19.0
+jsonschema-specifications==2023.7.1
+kiwisolver==1.4.5
+lightning-utilities==0.9.0
+lit==16.0.6
+markdown==3.4.4
+markupsafe==2.1.3
+matplotlib==3.7.2
+mpmath==1.3.0
+multidict==6.0.4
+multiprocess==0.70.15
+networkx==3.1
+numpy==1.24.4
+nvidia-cublas-cu11==11.10.3.66
+nvidia-cuda-cupti-cu11==11.7.101
+nvidia-cuda-nvrtc-cu11==11.7.99
+nvidia-cuda-runtime-cu11==11.7.99
+nvidia-cudnn-cu11==8.5.0.96
+nvidia-cufft-cu11==10.9.0.58
+nvidia-curand-cu11==10.2.10.91
+nvidia-cusolver-cu11==11.4.0.1
+nvidia-cusparse-cu11==11.7.4.91
+nvidia-nccl-cu11==2.14.3
+nvidia-nvtx-cu11==11.7.91
+oauthlib==3.2.2
+omegaconf==2.3.0
+opencv-python==4.8.0.76
+orjson==3.9.5
+pandas==2.0.3
+pillow==9.5.0
+pkgutil-resolve-name==1.3.10
+protobuf==4.24.2
+psutil==5.9.5
+pyarrow==13.0.0
+pyasn1==0.5.0
+pyasn1-modules==0.3.0
+pydantic==2.3.0
+pydantic-core==2.6.3
+pydub==0.25.1
+pyparsing==3.0.9
+python-multipart==0.0.6
+pytorch-lightning==2.0.7
+pytz==2023.3
+pyyaml==6.0.1
+referencing==0.30.2
+regex==2023.8.8
+requests==2.31.0
+requests-oauthlib==1.3.1
+rpds-py==0.9.2
+rsa==4.9
+safetensors==0.3.3
+semantic-version==2.10.0
+sniffio==1.3.0
+starlette==0.27.0
+sympy==1.12
+tensorboard==2.14.0
+tensorboard-data-server==0.7.1
+tokenizers==0.13.3
+toolz==0.12.0
+torchmetrics==1.1.0
+tqdm==4.66.1
+transformers==4.32.0
+triton==2.0.0
+tzdata==2023.3
+urllib3==1.26.16
+uvicorn==0.23.2
+websockets==11.0.3
+werkzeug==2.3.7
+xxhash==3.3.0
+yarl==1.9.2
+zipp==3.16.2
+decord
+imageio==2.9.0
+imageio-ffmpeg==0.4.3
+timm
+scipy
+scikit-image
+av
+imgaug
+lpips
+ffmpeg-python
+torch==2.0.1
+torchvision==0.15.2
+xformers==0.0.22
+diffusers==0.21.4
diff --git a/samples/animation-2023-12-05T00-24-12/videos/0002_demo4.mp4 b/samples/animation-2023-12-05T00-24-12/videos/0002_demo4.mp4
new file mode 100644
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new file mode 100644
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new file mode 100644
index 0000000000000000000000000000000000000000..8f84ada3cbf1efa43b23138505387260ad642c83
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new file mode 100644
index 0000000000000000000000000000000000000000..462f7a7a5675834ed62e590b5102b3e78b563bee
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new file mode 100644
index 0000000000000000000000000000000000000000..4080a0155411670dc3839a7b8fd76517e3212e49
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new file mode 100644
index 0000000000000000000000000000000000000000..665b46f9551b4f4c8cb6253662ae7b5546d11f51
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new file mode 100644
index 0000000000000000000000000000000000000000..98e8cf53ecfefda176bb6ce86bab59fa484f141f
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new file mode 100644
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diff --git a/sd-vae-ft-mse/.gitattributes b/sd-vae-ft-mse/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..99f243ff7947dfc5afddaf3d818da3d0124d5ca2
--- /dev/null
+++ b/sd-vae-ft-mse/.gitattributes
@@ -0,0 +1,33 @@
+*.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
+*.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
+saved_model/**/* 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
+diffusion_pytorch_model.safetensors filter=lfs diff=lfs merge=lfs -text
diff --git a/sd-vae-ft-mse/README.md b/sd-vae-ft-mse/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..61d3c7cf571c574a406544612b857ac1b26f4439
--- /dev/null
+++ b/sd-vae-ft-mse/README.md
@@ -0,0 +1,83 @@
+---
+license: mit
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+inference: false
+---
+# Improved Autoencoders
+
+## Utilizing
+These weights are intended to be used with the [🧨 diffusers library](https://github.com/huggingface/diffusers). If you are looking for the model to use with the original [CompVis Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion), [come here](https://huggingface.co/stabilityai/sd-vae-ft-mse-original).
+
+#### How to use with 🧨 diffusers
+You can integrate this fine-tuned VAE decoder to your existing `diffusers` workflows, by including a `vae` argument to the `StableDiffusionPipeline`
+```py
+from diffusers.models import AutoencoderKL
+from diffusers import StableDiffusionPipeline
+
+model = "CompVis/stable-diffusion-v1-4"
+vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
+pipe = StableDiffusionPipeline.from_pretrained(model, vae=vae)
+```
+
+## Decoder Finetuning
+We publish two kl-f8 autoencoder versions, finetuned from the original [kl-f8 autoencoder](https://github.com/CompVis/latent-diffusion#pretrained-autoencoding-models) on a 1:1 ratio of [LAION-Aesthetics](https://laion.ai/blog/laion-aesthetics/) and LAION-Humans, an unreleased subset containing only SFW images of humans. The intent was to fine-tune on the Stable Diffusion training set (the autoencoder was originally trained on OpenImages) but also enrich the dataset with images of humans to improve the reconstruction of faces.
+The first, _ft-EMA_, was resumed from the original checkpoint, trained for 313198 steps and uses EMA weights. It uses the same loss configuration as the original checkpoint (L1 + LPIPS).
+The second, _ft-MSE_, was resumed from _ft-EMA_ and uses EMA weights and was trained for another 280k steps using a different loss, with more emphasis
+on MSE reconstruction (MSE + 0.1 * LPIPS). It produces somewhat ``smoother'' outputs. The batch size for both versions was 192 (16 A100s, batch size 12 per GPU).
+To keep compatibility with existing models, only the decoder part was finetuned; the checkpoints can be used as a drop-in replacement for the existing autoencoder.
+
+_Original kl-f8 VAE vs f8-ft-EMA vs f8-ft-MSE_
+
+## Evaluation
+### COCO 2017 (256x256, val, 5000 images)
+| Model | train steps | rFID | PSNR | SSIM | PSIM | Link | Comments
+|----------|---------|------|--------------|---------------|---------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|
+| | | | | | | | |
+| original | 246803 | 4.99 | 23.4 +/- 3.8 | 0.69 +/- 0.14 | 1.01 +/- 0.28 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD |
+| ft-EMA | 560001 | 4.42 | 23.8 +/- 3.9 | 0.69 +/- 0.13 | 0.96 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt | slightly better overall, with EMA |
+| ft-MSE | 840001 | 4.70 | 24.5 +/- 3.7 | 0.71 +/- 0.13 | 0.92 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt | resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs |
+
+
+### LAION-Aesthetics 5+ (256x256, subset, 10000 images)
+| Model | train steps | rFID | PSNR | SSIM | PSIM | Link | Comments
+|----------|-----------|------|--------------|---------------|---------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|
+| | | | | | | | |
+| original | 246803 | 2.61 | 26.0 +/- 4.4 | 0.81 +/- 0.12 | 0.75 +/- 0.36 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD |
+| ft-EMA | 560001 | 1.77 | 26.7 +/- 4.8 | 0.82 +/- 0.12 | 0.67 +/- 0.34 | https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt | slightly better overall, with EMA |
+| ft-MSE | 840001 | 1.88 | 27.3 +/- 4.7 | 0.83 +/- 0.11 | 0.65 +/- 0.34 | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt | resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs |
+
+
+### Visual
+_Visualization of reconstructions on 256x256 images from the COCO2017 validation dataset._
+
+
+
+
+256x256: ft-EMA (left), ft-MSE (middle), original (right)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/sd-vae-ft-mse/config.json b/sd-vae-ft-mse/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..0db26717579be63eb0ddbf15b43faa43700dfe5a
--- /dev/null
+++ b/sd-vae-ft-mse/config.json
@@ -0,0 +1,29 @@
+{
+ "_class_name": "AutoencoderKL",
+ "_diffusers_version": "0.4.2",
+ "act_fn": "silu",
+ "block_out_channels": [
+ 128,
+ 256,
+ 512,
+ 512
+ ],
+ "down_block_types": [
+ "DownEncoderBlock2D",
+ "DownEncoderBlock2D",
+ "DownEncoderBlock2D",
+ "DownEncoderBlock2D"
+ ],
+ "in_channels": 3,
+ "latent_channels": 4,
+ "layers_per_block": 2,
+ "norm_num_groups": 32,
+ "out_channels": 3,
+ "sample_size": 256,
+ "up_block_types": [
+ "UpDecoderBlock2D",
+ "UpDecoderBlock2D",
+ "UpDecoderBlock2D",
+ "UpDecoderBlock2D"
+ ]
+}
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new file mode 100644
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+oid sha256:1b4889b6b1d4ce7ae320a02dedaeff1780ad77d415ea0d744b476155c6377ddc
+size 334707217
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new file mode 100644
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+*.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
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+*.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
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+*.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
+v1-5-pruned-emaonly.ckpt filter=lfs diff=lfs merge=lfs -text
+v1-5-pruned.ckpt filter=lfs diff=lfs merge=lfs -text
diff --git a/stable-diffusion-v1-5/README.md b/stable-diffusion-v1-5/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..103bad8a83037abcdb9d24f2b21eba8792b33a5d
--- /dev/null
+++ b/stable-diffusion-v1-5/README.md
@@ -0,0 +1,207 @@
+---
+license: creativeml-openrail-m
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+inference: true
+extra_gated_prompt: |-
+ This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
+ The CreativeML OpenRAIL License specifies:
+
+ 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
+ 2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
+ 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
+ Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
+
+extra_gated_heading: Please read the LICENSE to access this model
+---
+
+# Stable Diffusion v1-5 Model Card
+
+Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
+For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion).
+
+The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2)
+checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
+
+You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion).
+
+### Diffusers
+```py
+from diffusers import StableDiffusionPipeline
+import torch
+
+model_id = "runwayml/stable-diffusion-v1-5"
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+prompt = "a photo of an astronaut riding a horse on mars"
+image = pipe(prompt).images[0]
+
+image.save("astronaut_rides_horse.png")
+```
+For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion)
+
+### Original GitHub Repository
+
+1. Download the weights
+ - [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference
+ - [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning
+
+2. Follow instructions [here](https://github.com/runwayml/stable-diffusion).
+
+## Model Details
+- **Developed by:** Robin Rombach, Patrick Esser
+- **Model type:** Diffusion-based text-to-image generation model
+- **Language(s):** English
+- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
+- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
+- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
+- **Cite as:**
+
+ @InProceedings{Rombach_2022_CVPR,
+ author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
+ title = {High-Resolution Image Synthesis With Latent Diffusion Models},
+ booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+ month = {June},
+ year = {2022},
+ pages = {10684-10695}
+ }
+
+# Uses
+
+## Direct Use
+The model is intended for research purposes only. Possible research areas and
+tasks include
+
+- Safe deployment of models which have the potential to generate harmful content.
+- Probing and understanding the limitations and biases of generative models.
+- Generation of artworks and use in design and other artistic processes.
+- Applications in educational or creative tools.
+- Research on generative models.
+
+Excluded uses are described below.
+
+ ### Misuse, Malicious Use, and Out-of-Scope Use
+_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
+
+
+The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
+
+#### Out-of-Scope Use
+The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
+
+#### Misuse and Malicious Use
+Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
+
+- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
+- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
+- Impersonating individuals without their consent.
+- Sexual content without consent of the people who might see it.
+- Mis- and disinformation
+- Representations of egregious violence and gore
+- Sharing of copyrighted or licensed material in violation of its terms of use.
+- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
+
+## Limitations and Bias
+
+### Limitations
+
+- The model does not achieve perfect photorealism
+- The model cannot render legible text
+- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
+- Faces and people in general may not be generated properly.
+- The model was trained mainly with English captions and will not work as well in other languages.
+- The autoencoding part of the model is lossy
+- The model was trained on a large-scale dataset
+ [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
+ and is not fit for product use without additional safety mechanisms and
+ considerations.
+- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
+ The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
+
+### Bias
+
+While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
+Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
+which consists of images that are primarily limited to English descriptions.
+Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
+This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
+ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
+
+### Safety Module
+
+The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers.
+This checker works by checking model outputs against known hard-coded NSFW concepts.
+The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter.
+Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images.
+The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept.
+
+
+## Training
+
+**Training Data**
+The model developers used the following dataset for training the model:
+
+- LAION-2B (en) and subsets thereof (see next section)
+
+**Training Procedure**
+Stable Diffusion v1-5 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
+
+- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
+- Text prompts are encoded through a ViT-L/14 text-encoder.
+- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
+- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet.
+
+Currently six Stable Diffusion checkpoints are provided, which were trained as follows.
+- [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
+ 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
+- [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`.
+ 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
+filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
+- [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2` - 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
+- [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2` - 225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
+- [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
+- [`stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything.
+
+- **Hardware:** 32 x 8 x A100 GPUs
+- **Optimizer:** AdamW
+- **Gradient Accumulations**: 2
+- **Batch:** 32 x 8 x 2 x 4 = 2048
+- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
+
+## Evaluation Results
+Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
+5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling
+steps show the relative improvements of the checkpoints:
+
+
+
+Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
+## Environmental Impact
+
+**Stable Diffusion v1** **Estimated Emissions**
+Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
+
+- **Hardware Type:** A100 PCIe 40GB
+- **Hours used:** 150000
+- **Cloud Provider:** AWS
+- **Compute Region:** US-east
+- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
+
+
+## Citation
+
+```bibtex
+ @InProceedings{Rombach_2022_CVPR,
+ author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
+ title = {High-Resolution Image Synthesis With Latent Diffusion Models},
+ booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+ month = {June},
+ year = {2022},
+ pages = {10684-10695}
+ }
+```
+
+*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
\ No newline at end of file
diff --git a/stable-diffusion-v1-5/feature_extractor/preprocessor_config.json b/stable-diffusion-v1-5/feature_extractor/preprocessor_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..5294955ff7801083f720b34b55d0f1f51313c5c5
--- /dev/null
+++ b/stable-diffusion-v1-5/feature_extractor/preprocessor_config.json
@@ -0,0 +1,20 @@
+{
+ "crop_size": 224,
+ "do_center_crop": true,
+ "do_convert_rgb": true,
+ "do_normalize": true,
+ "do_resize": true,
+ "feature_extractor_type": "CLIPFeatureExtractor",
+ "image_mean": [
+ 0.48145466,
+ 0.4578275,
+ 0.40821073
+ ],
+ "image_std": [
+ 0.26862954,
+ 0.26130258,
+ 0.27577711
+ ],
+ "resample": 3,
+ "size": 224
+}
diff --git a/stable-diffusion-v1-5/model_index.json b/stable-diffusion-v1-5/model_index.json
new file mode 100644
index 0000000000000000000000000000000000000000..daf7e2e2dfc64fb437a2b44525667111b00cb9fc
--- /dev/null
+++ b/stable-diffusion-v1-5/model_index.json
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+{
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+ "stable_diffusion",
+ "StableDiffusionSafetyChecker"
+ ],
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+ "diffusers",
+ "PNDMScheduler"
+ ],
+ "text_encoder": [
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+ "CLIPTextModel"
+ ],
+ "tokenizer": [
+ "transformers",
+ "CLIPTokenizer"
+ ],
+ "unet": [
+ "diffusers",
+ "UNet2DConditionModel"
+ ],
+ "vae": [
+ "diffusers",
+ "AutoencoderKL"
+ ]
+}
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diff --git a/stable-diffusion-v1-5/tokenizer/special_tokens_map.json b/stable-diffusion-v1-5/tokenizer/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..2c2130b544c0c5a72d5d00da071ba130a9800fb2
--- /dev/null
+++ b/stable-diffusion-v1-5/tokenizer/special_tokens_map.json
@@ -0,0 +1,24 @@
+{
+ "bos_token": {
+ "content": "<|startoftext|>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ },
+ "eos_token": {
+ "content": "<|endoftext|>",
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+ "normalized": true,
+ "rstrip": false,
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+ "unk_token": {
+ "content": "<|endoftext|>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ }
+}
diff --git a/stable-diffusion-v1-5/tokenizer/tokenizer_config.json b/stable-diffusion-v1-5/tokenizer/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..5ba7bf706515bc60487ad0e1816b4929b82542d6
--- /dev/null
+++ b/stable-diffusion-v1-5/tokenizer/tokenizer_config.json
@@ -0,0 +1,34 @@
+{
+ "add_prefix_space": false,
+ "bos_token": {
+ "__type": "AddedToken",
+ "content": "<|startoftext|>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ },
+ "do_lower_case": true,
+ "eos_token": {
+ "__type": "AddedToken",
+ "content": "<|endoftext|>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ },
+ "errors": "replace",
+ "model_max_length": 77,
+ "name_or_path": "openai/clip-vit-large-patch14",
+ "pad_token": "<|endoftext|>",
+ "special_tokens_map_file": "./special_tokens_map.json",
+ "tokenizer_class": "CLIPTokenizer",
+ "unk_token": {
+ "__type": "AddedToken",
+ "content": "<|endoftext|>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ }
+}
diff --git a/stable-diffusion-v1-5/tokenizer/vocab.json b/stable-diffusion-v1-5/tokenizer/vocab.json
new file mode 100644
index 0000000000000000000000000000000000000000..469be27c5c010538f845f518c4f5e8574c78f7c8
--- /dev/null
+++ b/stable-diffusion-v1-5/tokenizer/vocab.json
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diff --git a/stable-diffusion-v1-5/unet/config.json b/stable-diffusion-v1-5/unet/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..1a02ee8abc93e840ffbcb2d68b66ccbcb74b3ab3
--- /dev/null
+++ b/stable-diffusion-v1-5/unet/config.json
@@ -0,0 +1,36 @@
+{
+ "_class_name": "UNet2DConditionModel",
+ "_diffusers_version": "0.6.0",
+ "act_fn": "silu",
+ "attention_head_dim": 8,
+ "block_out_channels": [
+ 320,
+ 640,
+ 1280,
+ 1280
+ ],
+ "center_input_sample": false,
+ "cross_attention_dim": 768,
+ "down_block_types": [
+ "CrossAttnDownBlock2D",
+ "CrossAttnDownBlock2D",
+ "CrossAttnDownBlock2D",
+ "DownBlock2D"
+ ],
+ "downsample_padding": 1,
+ "flip_sin_to_cos": true,
+ "freq_shift": 0,
+ "in_channels": 4,
+ "layers_per_block": 2,
+ "mid_block_scale_factor": 1,
+ "norm_eps": 1e-05,
+ "norm_num_groups": 32,
+ "out_channels": 4,
+ "sample_size": 64,
+ "up_block_types": [
+ "UpBlock2D",
+ "CrossAttnUpBlock2D",
+ "CrossAttnUpBlock2D",
+ "CrossAttnUpBlock2D"
+ ]
+}
diff --git a/stable-diffusion-v1-5/unet/diffusion_pytorch_model.bin b/stable-diffusion-v1-5/unet/diffusion_pytorch_model.bin
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diff --git a/stable-diffusion-v1-5/v1-inference.yaml b/stable-diffusion-v1-5/v1-inference.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d4effe569e897369918625f9d8be5603a0e6a0d6
--- /dev/null
+++ b/stable-diffusion-v1-5/v1-inference.yaml
@@ -0,0 +1,70 @@
+model:
+ base_learning_rate: 1.0e-04
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: "jpg"
+ cond_stage_key: "txt"
+ image_size: 64
+ channels: 4
+ cond_stage_trainable: false # Note: different from the one we trained before
+ conditioning_key: crossattn
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+
+ scheduler_config: # 10000 warmup steps
+ target: ldm.lr_scheduler.LambdaLinearScheduler
+ params:
+ warm_up_steps: [ 10000 ]
+ cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+ f_start: [ 1.e-6 ]
+ f_max: [ 1. ]
+ f_min: [ 1. ]
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 32 # unused
+ in_channels: 4
+ out_channels: 4
+ model_channels: 320
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_heads: 8
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 768
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+
+ cond_stage_config:
+ target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
diff --git a/stable-diffusion-v1-5/vae/config.json b/stable-diffusion-v1-5/vae/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..55d78924fee13e4220f24320127c5f16284e13b9
--- /dev/null
+++ b/stable-diffusion-v1-5/vae/config.json
@@ -0,0 +1,29 @@
+{
+ "_class_name": "AutoencoderKL",
+ "_diffusers_version": "0.6.0",
+ "act_fn": "silu",
+ "block_out_channels": [
+ 128,
+ 256,
+ 512,
+ 512
+ ],
+ "down_block_types": [
+ "DownEncoderBlock2D",
+ "DownEncoderBlock2D",
+ "DownEncoderBlock2D",
+ "DownEncoderBlock2D"
+ ],
+ "in_channels": 3,
+ "latent_channels": 4,
+ "layers_per_block": 2,
+ "norm_num_groups": 32,
+ "out_channels": 3,
+ "sample_size": 512,
+ "up_block_types": [
+ "UpDecoderBlock2D",
+ "UpDecoderBlock2D",
+ "UpDecoderBlock2D",
+ "UpDecoderBlock2D"
+ ]
+}
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