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Merge branch 'main' into patch-1

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Lincoln Stein 2023-01-17 23:52:13 -05:00 committed by GitHub
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23 changed files with 143 additions and 73 deletions
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1
.github/workflows/test-invoke-pip.yml vendored
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@ -119,6 +119,7 @@ jobs:
run: >
configure_invokeai.py
--yes
--default_only
--full-precision # can't use fp16 weights without a GPU
- name: Run the tests

49
configs/INITIAL_MODELS.yaml
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@ -1,18 +1,32 @@
stable-diffusion-2.1-768:
description: Stable Diffusion version 2.1 diffusers model, trained on 768x768 images (5.21 GB)
repo_id: stabilityai/stable-diffusion-2-1
format: diffusers
recommended: True
stable-diffusion-2.1-base:
description: Stable Diffusion version 2.1 diffusers base model, trained on 512x512 images (5.21 GB)
repo_id: stabilityai/stable-diffusion-2-1-base
format: diffusers
recommended: False
stable-diffusion-1.5:
description: Stable Diffusion version 1.5 weight file (4.27 GB)
repo_id: runwayml/stable-diffusion-v1-5
format: diffusers
recommended: True
default: True
vae:
repo_id: stabilityai/sd-vae-ft-mse
default: True
stable-diffusion-2.1:
description: Stable Diffusion version 2.1 diffusers model (5.21 GB)
repo_id: stabilityai/stable-diffusion-2-1
stable-diffusion-1.4:
description: The original Stable Diffusion version 1.4 weight file (4.27 GB)
repo_id: CompVis/stable-diffusion-v1-4
recommended: False
format: diffusers
recommended: True
vae:
repo_id: stabilityai/sd-vae-ft-mse
width: 512
height: 512
inpainting-1.5:
description: RunwayML SD 1.5 model optimized for inpainting (4.27 GB)
description: RunwayML SD 1.5 model optimized for inpainting (ckpt version) (4.27 GB)
repo_id: runwayml/stable-diffusion-inpainting
config: v1-inpainting-inference.yaml
file: sd-v1-5-inpainting.ckpt
@ -23,19 +37,13 @@ inpainting-1.5:
recommended: True
width: 512
height: 512
stable-diffusion-1.4:
description: The original Stable Diffusion version 1.4 weight file (4.27 GB)
repo_id: CompVis/stable-diffusion-v1-4
recommended: False
format: diffusers
vae:
repo_id: stabilityai/sd-vae-ft-mse
waifu-diffusion-1.4:
description: Waifu diffusion 1.4
description: Latest waifu diffusion 1.4 (diffusers version)
format: diffusers
repo_id: hakurei/waifu-diffusion
recommended: True
waifu-diffusion-1.3:
description: Stable Diffusion 1.4 fine tuned on anime-styled images (4.27 GB)
description: Stable Diffusion 1.4 fine tuned on anime-styled images (ckpt version) (4.27 GB)
repo_id: hakurei/waifu-diffusion-v1-3
config: v1-inference.yaml
file: model-epoch09-float32.ckpt
@ -53,8 +61,8 @@ trinart-2.0:
recommended: False
vae:
repo_id: stabilityai/sd-vae-ft-mse
trinart_characters-2.0:
description: An SD model finetuned with 19.2M anime/manga style images (4.27 GB)
trinart_characters-2_0:
description: An SD model finetuned with 19.2M anime/manga style images (ckpt version) (4.27 GB)
repo_id: naclbit/trinart_derrida_characters_v2_stable_diffusion
config: v1-inference.yaml
file: derrida_final.ckpt
@ -65,6 +73,11 @@ trinart_characters-2.0:
recommended: False
width: 512
height: 512
anything-4.0:
description: High-quality, highly detailed anime style images with just a few prompts
format: diffusers
repo_id: andite/anything-v4.0
recommended: False
papercut-1.0:
description: SD 1.5 fine-tuned for papercut art (use "PaperCut" in your prompts) (2.13 GB)
repo_id: Fictiverse/Stable_Diffusion_PaperCut_Model
@ -72,8 +85,6 @@ papercut-1.0:
vae:
repo_id: stabilityai/sd-vae-ft-mse
recommended: False
width: 512
height: 512
voxel_art-1.0:
description: Stable Diffusion trained on voxel art (use "VoxelArt" in your prompts) (4.27 GB)
repo_id: Fictiverse/Stable_Diffusion_VoxelArt_Model

2
docs/installation/020_INSTALL_MANUAL.md
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@ -217,7 +217,7 @@ manager, please follow these steps:
!!! tip
Do not move the source code repository after installation. The virtual environment directory has absolute paths in it that get confused if the directory is moved.
Do not move the source code repository after installation. The virtual environment directory has absolute paths in it that get confused if the directory is moved.
---

2
environments-and-requirements/requirements-lin-amd.txt
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@ -1,6 +1,6 @@
-r environments-and-requirements/requirements-base.txt
# Get hardware-appropriate torch/torchvision
--extra-index-url https://download.pytorch.org/whl/rocm5.1.1 --trusted-host https://download.pytorch.org
--extra-index-url https://download.pytorch.org/whl/rocm5.2 --trusted-host https://download.pytorch.org
torch>=1.13.1
torchvision>=0.14.1
-e .

2
environments-and-requirements/requirements-lin-cuda.txt
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@ -1,4 +1,4 @@
--extra-index-url https://download.pytorch.org/whl/cu116 --trusted-host https://download.pytorch.org
--trusted-host https://download.pytorch.org
-r environments-and-requirements/requirements-base.txt
torch>=1.13.1
torchvision>=0.14.1

2
environments-and-requirements/requirements-win-colab-cuda.txt
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@ -1,6 +1,6 @@
-r environments-and-requirements/requirements-base.txt
# Get hardware-appropriate torch/torchvision
--extra-index-url https://download.pytorch.org/whl/cu116 --trusted-host https://download.pytorch.org
--extra-index-url https://download.pytorch.org/whl/cu117 --trusted-host https://download.pytorch.org
torch==1.13.1
torchvision==0.14.1
-e .

3
ldm/generate.py
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@ -29,7 +29,7 @@ from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
from ldm.invoke.conditioning import get_uc_and_c_and_ec
from ldm.invoke.devices import choose_torch_device, choose_precision
from ldm.invoke.generator.inpaint import infill_methods
from ldm.invoke.globals import global_cache_dir
from ldm.invoke.globals import global_cache_dir, Globals
from ldm.invoke.image_util import InitImageResizer
from ldm.invoke.model_manager import ModelManager
from ldm.invoke.pngwriter import PngWriter
@ -201,6 +201,7 @@ class Generate:
self.precision = 'float32'
if self.precision == 'auto':
self.precision = choose_precision(self.device)
Globals.full_precision = self.precision=='float32'
# model caching system for fast switching
self.model_manager = ModelManager(mconfig,self.device,self.precision,max_loaded_models=max_loaded_models)

7
ldm/invoke/CLI.py
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@ -613,8 +613,6 @@ def import_diffuser_model(path_or_repo:str, gen, opt, completer)->str:
description = model_description):
print('** model failed to import')
return None
if input('Make this the default model? [n] ').startswith(('y','Y')):
manager.set_default_model(model_name)
return model_name
def import_ckpt_model(path_or_url:str, gen, opt, completer)->str:
@ -647,8 +645,6 @@ def import_ckpt_model(path_or_url:str, gen, opt, completer)->str:
print('** model failed to import')
return None
if input('Make this the default model? [n] ').startswith(('y','Y')):
manager.set_model_default(model_name)
return model_name
def _verify_load(model_name:str, gen)->bool:
@ -726,6 +722,9 @@ def del_config(model_name:str, gen, opt, completer):
if model_name == current_model:
print("** Can't delete active model. !switch to another model first. **")
return
if model_name not in gen.model_manager.config:
print(f"** Unknown model {model_name}")
return
gen.model_manager.del_model(model_name)
gen.model_manager.commit(opt.conf)
print(f'** {model_name} deleted')

3
ldm/invoke/ckpt_generator/base.py
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@ -335,4 +335,5 @@ class CkptGenerator():
os.makedirs(dirname, exist_ok=True)
image.save(filepath,'PNG')
def torch_dtype(self)->torch.dtype:
return torch.float16 if self.precision == 'float16' else torch.float32

18
ldm/invoke/ckpt_generator/txt2img.py
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@ -72,16 +72,18 @@ class CkptTxt2Img(CkptGenerator):
device = self.model.device
if self.use_mps_noise or device.type == 'mps':
x = torch.randn([1,
self.latent_channels,
height // self.downsampling_factor,
width // self.downsampling_factor],
device='cpu').to(device)
self.latent_channels,
height // self.downsampling_factor,
width // self.downsampling_factor],
dtype=self.torch_dtype(),
device='cpu').to(device)
else:
x = torch.randn([1,
self.latent_channels,
height // self.downsampling_factor,
width // self.downsampling_factor],
device=device)
self.latent_channels,
height // self.downsampling_factor,
width // self.downsampling_factor],
dtype=self.torch_dtype(),
device=device)
if self.perlin > 0.0:
x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(width // self.downsampling_factor, height // self.downsampling_factor)
return x

9
ldm/invoke/devices.py
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@ -21,10 +21,19 @@ def choose_precision(device) -> str:
return 'float16'
return 'float32'
def torch_dtype(device) -> torch.dtype:
if Globals.full_precision:
return torch.float32
if choose_precision(device) == 'float16':
return torch.float16
else:
return torch.float32
def choose_autocast(precision):
'''Returns an autocast context or nullcontext for the given precision string'''
# float16 currently requires autocast to avoid errors like:
# 'expected scalar type Half but found Float'
print(f'DEBUG: choose_autocast() called')
if precision == 'autocast' or precision == 'float16':
return autocast
return nullcontext

11
ldm/invoke/generator/base.py
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@ -8,6 +8,7 @@ import os
import os.path as osp
import random
import traceback
from contextlib import nullcontext
import cv2
import numpy as np
@ -18,8 +19,6 @@ from einops import rearrange
from pytorch_lightning import seed_everything
from tqdm import trange
from ldm.invoke.devices import choose_autocast
from ldm.models.diffusion.cross_attention_map_saving import AttentionMapSaver
from ldm.models.diffusion.ddpm import DiffusionWrapper
from ldm.util import rand_perlin_2d
@ -64,7 +63,7 @@ class Generator:
image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
safety_checker:dict=None,
**kwargs):
scope = choose_autocast(self.precision)
scope = nullcontext
self.safety_checker = safety_checker
attention_maps_images = []
attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image())
@ -236,7 +235,8 @@ class Generator:
def get_perlin_noise(self,width,height):
fixdevice = 'cpu' if (self.model.device.type == 'mps') else self.model.device
return torch.stack([rand_perlin_2d((height, width), (8, 8), device = self.model.device).to(fixdevice) for _ in range(self.latent_channels)], dim=0).to(self.model.device)
noise = torch.stack([rand_perlin_2d((height, width), (8, 8), device = self.model.device).to(fixdevice) for _ in range(self.latent_channels)], dim=0).to(self.model.device)
return noise
def new_seed(self):
self.seed = random.randrange(0, np.iinfo(np.uint32).max)
@ -341,3 +341,6 @@ class Generator:
image.save(filepath,'PNG')
def torch_dtype(self)->torch.dtype:
return torch.float16 if self.precision == 'float16' else torch.float32

8
ldm/invoke/generator/diffusers_pipeline.py
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@ -391,7 +391,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
for i, t in enumerate(self.progress_bar(timesteps)):
batched_t.fill_(t)
step_output = self.step(batched_t, latents, conditioning_data,
i, additional_guidance=additional_guidance)
step_index=i,
total_step_count=len(timesteps),
additional_guidance=additional_guidance)
latents = step_output.prev_sample
predicted_original = getattr(step_output, 'pred_original_sample', None)
@ -410,7 +412,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
@torch.inference_mode()
def step(self, t: torch.Tensor, latents: torch.Tensor,
conditioning_data: ConditioningData,
step_index:int | None = None, additional_guidance: List[Callable] = None):
step_index:int, total_step_count:int,
additional_guidance: List[Callable] = None):
# invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
timestep = t[0]
@ -427,6 +430,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
conditioning_data.unconditioned_embeddings, conditioning_data.text_embeddings,
conditioning_data.guidance_scale,
step_index=step_index,
total_step_count=total_step_count,
threshold=conditioning_data.threshold
)

21
ldm/invoke/generator/txt2img.py
View File

@ -36,10 +36,9 @@ class Txt2Img(Generator):
threshold = ThresholdSettings(threshold, warmup=0.2) if threshold else None)
.add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
def make_image(x_T) -> PIL.Image.Image:
pipeline_output = pipeline.image_from_embeddings(
latents=torch.zeros_like(x_T),
latents=torch.zeros_like(x_T,dtype=self.torch_dtype()),
noise=x_T,
num_inference_steps=steps,
conditioning_data=conditioning_data,
@ -59,16 +58,18 @@ class Txt2Img(Generator):
input_channels = min(self.latent_channels, 4)
if self.use_mps_noise or device.type == 'mps':
x = torch.randn([1,
input_channels,
height // self.downsampling_factor,
width // self.downsampling_factor],
device='cpu').to(device)
input_channels,
height // self.downsampling_factor,
width // self.downsampling_factor],
dtype=self.torch_dtype(),
device='cpu').to(device)
else:
x = torch.randn([1,
input_channels,
height // self.downsampling_factor,
width // self.downsampling_factor],
device=device)
input_channels,
height // self.downsampling_factor,
width // self.downsampling_factor],
dtype=self.torch_dtype(),
device=device)
if self.perlin > 0.0:
x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(width // self.downsampling_factor, height // self.downsampling_factor)
return x

10
ldm/invoke/generator/txt2img2img.py
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@ -90,9 +90,9 @@ class Txt2Img2Img(Generator):
def get_noise_like(self, like: torch.Tensor):
device = like.device
if device.type == 'mps':
x = torch.randn_like(like, device='cpu').to(device)
x = torch.randn_like(like, device='cpu', dtype=self.torch_dtype()).to(device)
else:
x = torch.randn_like(like, device=device)
x = torch.randn_like(like, device=device, dtype=self.torch_dtype())
if self.perlin > 0.0:
shape = like.shape
x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
@ -117,10 +117,12 @@ class Txt2Img2Img(Generator):
self.latent_channels,
scaled_height // self.downsampling_factor,
scaled_width // self.downsampling_factor],
device='cpu').to(device)
dtype=self.torch_dtype(),
device='cpu').to(device)
else:
return torch.randn([1,
self.latent_channels,
scaled_height // self.downsampling_factor,
scaled_width // self.downsampling_factor],
device=device)
dtype=self.torch_dtype(),
device=device)

3
ldm/invoke/globals.py
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@ -43,6 +43,9 @@ Globals.always_use_cpu = False
# The CLI will test connectivity at startup time.
Globals.internet_available = True
# whether we are forcing full precision
Globals.full_precision = False
def global_config_dir()->Path:
return Path(Globals.root, Globals.config_dir)

29
ldm/invoke/model_manager.py
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@ -230,6 +230,9 @@ class ModelManager(object):
Delete the named model.
'''
omega = self.config
if model_name not in omega:
print(f'** Unknown model {model_name}')
return
del omega[model_name]
if model_name in self.stack:
self.stack.remove(model_name)
@ -253,9 +256,8 @@ class ModelManager(object):
assert (clobber or model_name not in omega), f'attempt to overwrite existing model definition "{model_name}"'
if model_name not in omega:
omega[model_name] = dict()
OmegaConf.update(omega,model_name,model_attributes,merge=False)
omega[model_name] = model_attributes
if 'weights' in omega[model_name]:
omega[model_name]['weights'].replace('\\','/')
@ -349,7 +351,7 @@ class ModelManager(object):
if self.precision == 'float16':
print(' | Using faster float16 precision')
model.to(torch.float16)
model = model.to(torch.float16)
else:
print(' | Using more accurate float32 precision')
@ -753,16 +755,20 @@ class ModelManager(object):
print('** Legacy version <= 2.2.5 model directory layout detected. Reorganizing.')
print('** This is a quick one-time operation.')
from shutil import move
from shutil import move, rmtree
# transformer files get moved into the hub directory
hub = models_dir / 'hub'
os.makedirs(hub, exist_ok=True)
for model in legacy_locations:
source = models_dir /model
source = models_dir / model
dest = hub / model.stem
print(f'** {source} => {dest}')
if source.exists():
print(f'DEBUG: Moving {models_dir / model} into hub')
move(models_dir / model, hub)
if dest.exists():
rmtree(source)
else:
move(source, dest)
# anything else gets moved into the diffusers directory
diffusers = models_dir / 'diffusers'
@ -773,7 +779,12 @@ class ModelManager(object):
if full_path.is_relative_to(hub) or full_path.is_relative_to(diffusers):
continue
if Path(dir).match('models--*--*'):
move(full_path,diffusers)
dest = diffusers / dir
print(f'** {full_path} => {dest}')
if dest.exists():
rmtree(full_path)
else:
move(full_path,dest)
# now clean up by removing any empty directories
empty = [root for root, dirs, files, in os.walk(models_dir) if not len(dirs) and not len(files)]

3
ldm/models/diffusion/cross_attention_control.py
View File

@ -7,6 +7,7 @@ import torch
import diffusers
from torch import nn
from diffusers.models.unet_2d_condition import UNet2DConditionModel
from ldm.invoke.devices import torch_dtype
# adapted from bloc97's CrossAttentionControl colab
# https://github.com/bloc97/CrossAttentionControl
@ -383,7 +384,7 @@ def inject_attention_function(unet, context: Context):
remapped_saved_attention_slice = torch.index_select(saved_attention_slice, -1, index_map)
this_attention_slice = suggested_attention_slice
mask = context.cross_attention_mask
mask = context.cross_attention_mask.to(torch_dtype(suggested_attention_slice.device))
saved_mask = mask
this_mask = 1 - mask
attention_slice = remapped_saved_attention_slice * saved_mask + \

11
ldm/models/diffusion/shared_invokeai_diffusion.py
View File

@ -89,6 +89,7 @@ class InvokeAIDiffuserComponent:
conditioning: Union[torch.Tensor,dict],
unconditional_guidance_scale: float,
step_index: Optional[int]=None,
total_step_count: Optional[int]=None,
threshold: Optional[ThresholdSettings]=None,
):
"""
@ -106,7 +107,15 @@ class InvokeAIDiffuserComponent:
cross_attention_control_types_to_do = []
context: Context = self.cross_attention_control_context
if self.cross_attention_control_context is not None:
percent_through = self.estimate_percent_through(step_index, sigma)
if step_index is not None and total_step_count is not None:
# 🧨diffusers codepath
percent_through = step_index / total_step_count # will never reach 1.0 - this is deliberate
else:
# legacy compvis codepath
# TODO remove when compvis codepath support is dropped
if step_index is None and sigma is None:
raise ValueError(f"Either step_index or sigma is required when doing cross attention control, but both are None.")
percent_through = self.estimate_percent_through(step_index, sigma)
cross_attention_control_types_to_do = context.get_active_cross_attention_control_types_for_step(percent_through)
wants_cross_attention_control = (len(cross_attention_control_types_to_do) > 0)

4
ldm/modules/prompt_to_embeddings_converter.py
View File

@ -4,7 +4,7 @@ import torch
from transformers import CLIPTokenizer, CLIPTextModel
from ldm.modules.textual_inversion_manager import TextualInversionManager
from ldm.invoke.devices import torch_dtype
class WeightedPromptFragmentsToEmbeddingsConverter():
@ -207,7 +207,7 @@ class WeightedPromptFragmentsToEmbeddingsConverter():
per_token_weights += [1.0] * pad_length
all_token_ids_tensor = torch.tensor(all_token_ids, dtype=torch.long, device=device)
per_token_weights_tensor = torch.tensor(per_token_weights, dtype=torch.float32, device=device)
per_token_weights_tensor = torch.tensor(per_token_weights, dtype=torch_dtype(self.text_encoder.device), device=device)
#print(f"assembled all_token_ids_tensor with shape {all_token_ids_tensor.shape}")
return all_token_ids_tensor, per_token_weights_tensor

1
ldm/modules/textual_inversion_manager.py
View File

@ -111,7 +111,6 @@ class TextualInversionManager():
if ti.trigger_token_id is not None:
raise ValueError(f"Tokens already injected for textual inversion with trigger '{ti.trigger_string}'")
print(f'DEBUG: Injecting token {ti.trigger_string}')
trigger_token_id = self._get_or_create_token_id_and_assign_embedding(ti.trigger_string, ti.embedding[0])
if ti.embedding_vector_length > 1:

4
ldm/util.py
View File

@ -8,6 +8,7 @@ from threading import Thread
from urllib import request
from tqdm import tqdm
from pathlib import Path
from ldm.invoke.devices import torch_dtype
import numpy as np
import torch
@ -235,7 +236,8 @@ def rand_perlin_2d(shape, res, device, fade = lambda t: 6*t**5 - 15*t**4 + 10*t*
n01 = dot(tile_grads([0, -1],[1, None]), [0, -1]).to(device)
n11 = dot(tile_grads([1, None], [1, None]), [-1,-1]).to(device)
t = fade(grid[:shape[0], :shape[1]])
return math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]).to(device)
noise = math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]).to(device)
return noise.to(dtype=torch_dtype(device))
def ask_user(question: str, answers: list):
from itertools import chain, repeat

13
scripts/configure_invokeai.py
View File

@ -197,6 +197,14 @@ def recommended_datasets()->dict:
datasets[ds]=True
return datasets
#---------------------------------------------
def default_dataset()->dict:
datasets = dict()
for ds in Datasets.keys():
if Datasets[ds].get('default',False):
datasets[ds]=True
return datasets
#---------------------------------------------
def all_datasets()->dict:
datasets = dict()
@ -646,7 +654,7 @@ def download_weights(opt:dict) -> Union[str, None]:
precision = 'float32' if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
if opt.yes_to_all:
models = recommended_datasets()
models = default_dataset() if opt.default_only else recommended_datasets()
access_token = authenticate(opt.yes_to_all)
if len(models)>0:
successfully_downloaded = download_weight_datasets(models, access_token, precision=precision)
@ -808,6 +816,9 @@ def main():
dest='yes_to_all',
action='store_true',
help='answer "yes" to all prompts')
parser.add_argument('--default_only',
action='store_true',
help='when --yes specified, only install the default model')
parser.add_argument('--config_file',
'-c',
dest='config_file',