Can you help build such a notebook?

from fresco.

Sure once I can figure out how to get run_fresco.py to work -- see separate issue

from fresco.

A notebook would be great Im trying to run it on google colab now and getting multiple errors.

Edit: ok got it working out the box on colab just had to change a few things..

The style transfer is super crisp!

Batch size 4 in config

Requirements

!pip install diffusers
!pip install gradio
!pip install numba
!pip install imageio-ffmpeg
!pip install transformers
!pip install torchvision
!pip install opencv-python
!pip install einops
!pip install matplotlib
!pip install timm
!pip install av
!pip install basicsr
!pip install transformers
!pip install opencv-contrib-python
!pip install einops
!pip install matplotlib
!pip install accelerate

!python /content/FRESCO/install.py

To use webui.py change line 358 to this
#source='upload',

Change to this in src/freelunchutils

from typing import Any, Dict, Optional, Tuple

import torch
import torch.fft as fft
from diffusers.utils import is_torch_version
#from diffusers.models.unet_2d_condition import logger as logger2d
from diffusers.models.unets.unet_2d_condition import UNet2DConditionOutput, UNet2DConditionModel
from diffusers.models.unets.unet_3d_condition import UNet3DConditionOutput, UNet3DConditionModel
#from diffusers.models import unet_3d_condition
#from unet_3d_condition import logger as logger3d

Change /usr/local/lib/python3.10/dist-packages/diffusers/models/init.py to this

#@title replace above with

Copyright 2024 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");

you may not use this file except in compliance with the License.

You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software

distributed under the License is distributed on an "AS IS" BASIS,

WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

See the License for the specific language governing permissions and

limitations under the License.

from typing import TYPE_CHECKING

from ..utils import (
DIFFUSERS_SLOW_IMPORT,
_LazyModule,
is_flax_available,
is_torch_available,
)

_import_structure = {}

if is_torch_available():
_import_structure["adapter"] = ["MultiAdapter", "T2IAdapter"]
_import_structure["autoencoders.autoencoder_asym_kl"] = ["AsymmetricAutoencoderKL"]
_import_structure["autoencoders.autoencoder_kl"] = ["AutoencoderKL"]
_import_structure["autoencoders.autoencoder_kl_temporal_decoder"] = ["AutoencoderKLTemporalDecoder"]
_import_structure["autoencoders.autoencoder_tiny"] = ["AutoencoderTiny"]
_import_structure["autoencoders.consistency_decoder_vae"] = ["ConsistencyDecoderVAE"]
_import_structure["controlnet"] = ["ControlNetModel"]
_import_structure["dual_transformer_2d"] = ["DualTransformer2DModel"]
_import_structure["embeddings"] = ["ImageProjection"]
_import_structure["modeling_utils"] = ["ModelMixin"]
_import_structure["transformers.prior_transformer"] = ["PriorTransformer"]
_import_structure["transformers.t5_film_transformer"] = ["T5FilmDecoder"]
_import_structure["transformers.transformer_2d"] = ["Transformer2DModel"]
_import_structure["transformers.transformer_temporal"] = ["TransformerTemporalModel"]
_import_structure["unets.unet_1d"] = ["UNet1DModel"]
_import_structure["unets.unet_2d"] = ["UNet2DModel"]
_import_structure["unets.unet_2d_condition"] = ["UNet2DConditionModel"]
_import_structure["unets.unet_3d_condition"] = ["UNet3DConditionModel"]
_import_structure["unets.unet_i2vgen_xl"] = ["I2VGenXLUNet"]
_import_structure["unets.unet_kandinsky3"] = ["Kandinsky3UNet"]
_import_structure["unets.unet_motion_model"] = ["MotionAdapter", "UNetMotionModel"]
_import_structure["unets.unet_spatio_temporal_condition"] = ["UNetSpatioTemporalConditionModel"]
_import_structure["unets.unet_stable_cascade"] = ["StableCascadeUNet"]
_import_structure["unets.uvit_2d"] = ["UVit2DModel"]
_import_structure["vq_model"] = ["VQModel"]

if is_flax_available():
_import_structure["controlnet_flax"] = ["FlaxControlNetModel"]
_import_structure["unets.unet_2d_condition_flax"] = ["FlaxUNet2DConditionModel"]
_import_structure["vae_flax"] = ["FlaxAutoencoderKL"]

if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
if is_torch_available():
from .adapter import MultiAdapter, T2IAdapter
from .autoencoders import (
AsymmetricAutoencoderKL,
AutoencoderKL,
AutoencoderKLTemporalDecoder,
AutoencoderTiny,
ConsistencyDecoderVAE,
)
from .controlnet import ControlNetModel
from .embeddings import ImageProjection
from .modeling_utils import ModelMixin
from .transformers import (
DualTransformer2DModel,
PriorTransformer,
T5FilmDecoder,
Transformer2DModel,
TransformerTemporalModel,
)
from .unets import (
I2VGenXLUNet,
Kandinsky3UNet,
MotionAdapter,
StableCascadeUNet,
UNet1DModel,
UNet2DConditionModel,
UNet2DModel,
UNet3DConditionModel,
UNetMotionModel,
UNetSpatioTemporalConditionModel,
UVit2DModel,
)
from .vq_model import VQModel

if is_flax_available():
    from .controlnet_flax import FlaxControlNetModel
    from .unets import FlaxUNet2DConditionModel
    from .vae_flax import FlaxAutoencoderKL

else:
import sys

sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

from fresco.