mirror of
https://github.com/invoke-ai/InvokeAI
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Replace --full_precision with --precision that works even if not specified
Allowed values are 'auto', 'float32', 'autocast', 'float16'. If not specified or 'auto' a working precision is automatically selected based on the torch device. Context: #526 Deprecated --full_precision / -F Tested on both cuda and cpu by calling scripts/dream.py without arguments and checked the auto configuration worked. With --precision=auto/float32/autocast/float16 it performs as expected, either working or failing with a reasonable error. Also checked Img2Img.
This commit is contained in:
Mihail Dumitrescu
Lincoln Stein
parent
30de9fcfae
commit
d176fb07cd
@ -5,8 +5,7 @@ SAMPLES_DIR=${OUT_DIR}
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python scripts/dream.py \
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--from_file ${PROMPT_FILE} \
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--outdir ${OUT_DIR} \
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--sampler plms \
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--full_precision
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--sampler plms
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# original output by CompVis/stable-diffusion
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IMAGE1=".dev_scripts/images/v1_4_astronaut_rides_horse_plms_step50_seed42.png"
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4
.github/workflows/test-dream-conda.yml
vendored
4
.github/workflows/test-dream-conda.yml
vendored
@ -85,9 +85,9 @@ jobs:
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fi
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# Utterly hacky, but I don't know how else to do this
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if [[ ${{ github.ref }} == 'refs/heads/master' ]]; then
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time ${{ steps.vars.outputs.PYTHON_BIN }} scripts/dream.py --from_file tests/preflight_prompts.txt --full_precision
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time ${{ steps.vars.outputs.PYTHON_BIN }} scripts/dream.py --from_file tests/preflight_prompts.txt
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elif [[ ${{ github.ref }} == 'refs/heads/development' ]]; then
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time ${{ steps.vars.outputs.PYTHON_BIN }} scripts/dream.py --from_file tests/dev_prompts.txt --full_precision
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time ${{ steps.vars.outputs.PYTHON_BIN }} scripts/dream.py --from_file tests/dev_prompts.txt
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fi
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mkdir -p outputs/img-samples
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- name: Archive results
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18
README.md
18
README.md
@ -86,17 +86,14 @@ You wil need one of the following:
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- At least 6 GB of free disk space for the machine learning model, Python, and all its dependencies.
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> Note
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>
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> If you have an Nvidia 10xx series card (e.g. the 1080ti), please run the dream script in
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> full-precision mode as shown below.
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#### Note
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Similarly, specify full-precision mode on Apple M1 hardware.
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To run in full-precision mode, start `dream.py` with the `--full_precision` flag:
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Precision is auto configured based on the device. If however you encounter
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errors like 'expected type Float but found Half' or 'not implemented for Half'
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you can try starting `dream.py` with the `--precision=float32` flag:
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```bash
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(ldm) ~/stable-diffusion$ python scripts/dream.py --full_precision
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(ldm) ~/stable-diffusion$ python scripts/dream.py --precision=float32
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```
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### Features
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@ -125,6 +122,11 @@ To run in full-precision mode, start `dream.py` with the `--full_precision` flag
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### Latest Changes
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- vNEXT (TODO 2022)
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- Deprecated `--full_precision` / `-F`. Simply omit it and `dream.py` will auto
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configure. To switch away from auto use the new flag like `--precision=float32`.
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- v1.14 (11 September 2022)
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- Memory optimizations for small-RAM cards. 512x512 now possible on 4 GB GPUs.
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@ -74,7 +74,7 @@ prompt arguments] (#list-of-prompt-arguments). Others
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| --prompt_as_dir | -p | False | Name output directories using the prompt text. |
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| --from_file <path> | | None | Read list of prompts from a file. Use "-" to read from standard input |
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| --model <modelname> | | stable-diffusion-1.4 | Loads model specified in configs/models.yaml. Currently one of "stable-diffusion-1.4" or "laion400m" |
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| --full_precision | -F | False | Run in slower full-precision mode. Needed for Macintosh M1/M2 hardware and some older video cards. |
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| --precision <pname> | | auto | Set to a specific precision. Rare but you may need to switch to 'float32' on some video cards. |
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| --web | | False | Start in web server mode |
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| --host <ip addr> | | localhost | Which network interface web server should listen on. Set to 0.0.0.0 to listen on any. |
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| --port <port> | | 9090 | Which port web server should listen for requests on. |
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@ -57,9 +57,7 @@ Once the model is trained, specify the trained .pt or .bin file when starting
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dream using
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```bash
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python3 ./scripts/dream.py \
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--embedding_path /path/to/embedding.pt \
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--full_precision
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python3 ./scripts/dream.py --embedding_path /path/to/embedding.pt
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```
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Then, to utilize your subject at the dream prompt
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@ -62,15 +62,12 @@ You wil need one of the following:
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### Note
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If you are have a Nvidia 10xx series card (e.g. the 1080ti), please run the dream script in
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full-precision mode as shown below.
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Similarly, specify full-precision mode on Apple M1 hardware.
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To run in full-precision mode, start `dream.py` with the `--full_precision` flag:
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Precision is auto configured based on the device. If however you encounter
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errors like 'expected type Float but found Half' or 'not implemented for Half'
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you can try starting `dream.py` with the `--precision=float32` flag:
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```bash
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(ldm) ~/stable-diffusion$ python scripts/dream.py --full_precision
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(ldm) ~/stable-diffusion$ python scripts/dream.py --precision=float32
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```
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## Features
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@ -98,6 +95,11 @@ To run in full-precision mode, start `dream.py` with the `--full_precision` flag
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## Latest Changes
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### vNEXT <small>(TODO 2022)</small>
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- Deprecated `--full_precision` / `-F`. Simply omit it and `dream.py` will auto
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configure. To switch away from auto use the new flag like `--precision=float32`.
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### v1.14 <small>(11 September 2022)</small>
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- Memory optimizations for small-RAM cards. 512x512 now possible on 4 GB GPUs.
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@ -97,7 +97,7 @@ conda activate ldm
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python scripts/preload_models.py
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# run SD!
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python scripts/dream.py --full_precision # half-precision requires autocast and won't work
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python scripts/dream.py
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# or run the web interface!
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python scripts/dream.py --web
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@ -453,5 +453,3 @@ Abort trap: 6
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warnings.warn('resource_tracker: There appear to be %d '
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```
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Macs do not support `autocast/mixed-precision`, so you need to supply
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`--full_precision` to use float32 everywhere.
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@ -100,6 +100,13 @@ SAMPLER_CHOICES = [
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'plms',
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]
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PRECISION_CHOICES = [
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'auto',
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'float32',
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'autocast',
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'float16',
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]
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# is there a way to pick this up during git commits?
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APP_ID = 'lstein/stable-diffusion'
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APP_VERSION = 'v1.15'
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@ -322,7 +329,16 @@ class Args(object):
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'--full_precision',
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dest='full_precision',
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action='store_true',
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help='Use more memory-intensive full precision math for calculations',
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help='Deprecated way to set --precision=float32',
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)
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model_group.add_argument(
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'--precision',
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dest='precision',
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type=str,
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choices=PRECISION_CHOICES,
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metavar='PRECISION',
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help=f'Set model precision. Defaults to auto selected based on device. Options: {", ".join(PRECISION_CHOICES)}',
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default='auto',
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)
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file_group.add_argument(
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'--from_file',
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@ -1,6 +1,6 @@
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import torch
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from torch import autocast
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from contextlib import contextmanager, nullcontext
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from contextlib import nullcontext
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def choose_torch_device() -> str:
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'''Convenience routine for guessing which GPU device to run model on'''
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@ -10,15 +10,18 @@ def choose_torch_device() -> str:
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return 'mps'
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return 'cpu'
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def choose_autocast_device(device):
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'''Returns an autocast compatible device from a torch device'''
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device_type = device.type # this returns 'mps' on M1
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# autocast only for cuda, but GTX 16xx have issues with it
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if device_type == 'cuda':
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device_name = torch.cuda.get_device_name()
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if 'GeForce GTX 1660' in device_name or 'GeForce GTX 1650' in device_name:
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return device_type,nullcontext
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else:
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return device_type,autocast
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else:
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return 'cpu',nullcontext
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def choose_precision(device) -> str:
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'''Returns an appropriate precision for the given torch device'''
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if device.type == 'cuda':
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device_name = torch.cuda.get_device_name(device)
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if not ('GeForce GTX 1660' in device_name or 'GeForce GTX 1650' in device_name):
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return 'float16'
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return 'float32'
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def choose_autocast(precision):
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'''Returns an autocast context or nullcontext for the given precision string'''
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# float16 currently requires autocast to avoid errors like:
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# 'expected scalar type Half but found Float'
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if precision == 'autocast' or precision == 'float16':
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return autocast
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return nullcontext
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@ -9,13 +9,14 @@ from tqdm import tqdm, trange
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from PIL import Image
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from einops import rearrange, repeat
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from pytorch_lightning import seed_everything
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from ldm.dream.devices import choose_autocast_device
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from ldm.dream.devices import choose_autocast
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downsampling = 8
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class Generator():
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def __init__(self,model):
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def __init__(self, model, precision):
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self.model = model
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self.precision = precision
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self.seed = None
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self.latent_channels = model.channels
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self.downsampling_factor = downsampling # BUG: should come from model or config
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@ -38,7 +39,7 @@ class Generator():
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def generate(self,prompt,init_image,width,height,iterations=1,seed=None,
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image_callback=None, step_callback=None,
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**kwargs):
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device_type,scope = choose_autocast_device(self.model.device)
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scope = choose_autocast(self.precision)
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make_image = self.get_make_image(
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prompt,
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init_image = init_image,
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@ -51,7 +52,7 @@ class Generator():
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results = []
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seed = seed if seed else self.new_seed()
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seed, initial_noise = self.generate_initial_noise(seed, width, height)
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with scope(device_type), self.model.ema_scope():
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with scope(self.model.device.type), self.model.ema_scope():
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for n in trange(iterations, desc='Generating'):
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x_T = None
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if self.variation_amount > 0:
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@ -11,8 +11,8 @@ from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.dream.generator.img2img import Img2Img
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class Embiggen(Generator):
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def __init__(self,model):
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super().__init__(model)
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def __init__(self, model, precision):
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super().__init__(model, precision)
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self.init_latent = None
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@torch.no_grad()
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@ -4,13 +4,13 @@ ldm.dream.generator.img2img descends from ldm.dream.generator
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import torch
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import numpy as np
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from ldm.dream.devices import choose_autocast_device
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from ldm.dream.devices import choose_autocast
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from ldm.dream.generator.base import Generator
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from ldm.models.diffusion.ddim import DDIMSampler
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class Img2Img(Generator):
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def __init__(self,model):
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super().__init__(model)
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def __init__(self, model, precision):
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super().__init__(model, precision)
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self.init_latent = None # by get_noise()
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@torch.no_grad()
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@ -32,8 +32,8 @@ class Img2Img(Generator):
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ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
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)
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device_type,scope = choose_autocast_device(self.model.device)
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with scope(device_type):
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scope = choose_autocast(self.precision)
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with scope(self.model.device.type):
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self.init_latent = self.model.get_first_stage_encoding(
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self.model.encode_first_stage(init_image)
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) # move to latent space
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@ -5,14 +5,14 @@ ldm.dream.generator.inpaint descends from ldm.dream.generator
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import torch
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import numpy as np
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from einops import rearrange, repeat
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from ldm.dream.devices import choose_autocast_device
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from ldm.dream.devices import choose_autocast
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from ldm.dream.generator.img2img import Img2Img
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from ldm.models.diffusion.ddim import DDIMSampler
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class Inpaint(Img2Img):
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def __init__(self,model):
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def __init__(self, model, precision):
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self.init_latent = None
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super().__init__(model)
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super().__init__(model, precision)
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@torch.no_grad()
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def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
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@ -38,8 +38,8 @@ class Inpaint(Img2Img):
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ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
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)
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device_type,scope = choose_autocast_device(self.model.device)
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with scope(device_type):
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scope = choose_autocast(self.precision)
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with scope(self.model.device.type):
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self.init_latent = self.model.get_first_stage_encoding(
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self.model.encode_first_stage(init_image)
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) # move to latent space
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@ -7,8 +7,8 @@ import numpy as np
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from ldm.dream.generator.base import Generator
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class Txt2Img(Generator):
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def __init__(self,model):
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super().__init__(model)
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def __init__(self, model, precision):
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super().__init__(model, precision)
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@torch.no_grad()
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def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
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@ -29,7 +29,7 @@ from ldm.models.diffusion.plms import PLMSSampler
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from ldm.models.diffusion.ksampler import KSampler
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from ldm.dream.pngwriter import PngWriter
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from ldm.dream.image_util import InitImageResizer
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from ldm.dream.devices import choose_torch_device
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from ldm.dream.devices import choose_torch_device, choose_precision
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from ldm.dream.conditioning import get_uc_and_c
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def fix_func(orig):
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@ -104,7 +104,7 @@ gr = Generate(
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# these values are set once and shouldn't be changed
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conf = path to configuration file ('configs/models.yaml')
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model = symbolic name of the model in the configuration file
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full_precision = False
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precision = float precision to be used
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# this value is sticky and maintained between generation calls
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sampler_name = ['ddim', 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms', 'plms'] // k_lms
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@ -130,6 +130,7 @@ class Generate:
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sampler_name = 'k_lms',
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ddim_eta = 0.0, # deterministic
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full_precision = False,
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precision = 'auto',
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# these are deprecated; if present they override values in the conf file
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weights = None,
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config = None,
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@ -145,7 +146,7 @@ class Generate:
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self.cfg_scale = 7.5
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self.sampler_name = sampler_name
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self.ddim_eta = 0.0 # same seed always produces same image
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self.full_precision = True if choose_torch_device() == 'mps' else full_precision
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self.precision = precision
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self.strength = 0.75
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self.seamless = False
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self.embedding_path = embedding_path
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@ -162,6 +163,14 @@ class Generate:
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# it wasn't actually doing anything. This logic could be reinstated.
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device_type = choose_torch_device()
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self.device = torch.device(device_type)
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if full_precision:
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if self.precision != 'auto':
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raise ValueError('Remove --full_precision / -F if using --precision')
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print('Please remove deprecated --full_precision / -F')
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print('If auto config does not work you can use --precision=float32')
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self.precision = 'float32'
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if self.precision == 'auto':
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self.precision = choose_precision(self.device)
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# for VRAM usage statistics
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self.session_peakmem = torch.cuda.max_memory_allocated() if self._has_cuda else None
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@ -440,25 +449,25 @@ class Generate:
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def _make_img2img(self):
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if not self.generators.get('img2img'):
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from ldm.dream.generator.img2img import Img2Img
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self.generators['img2img'] = Img2Img(self.model)
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self.generators['img2img'] = Img2Img(self.model, self.precision)
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return self.generators['img2img']
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def _make_embiggen(self):
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if not self.generators.get('embiggen'):
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from ldm.dream.generator.embiggen import Embiggen
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self.generators['embiggen'] = Embiggen(self.model)
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self.generators['embiggen'] = Embiggen(self.model, self.precision)
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return self.generators['embiggen']
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def _make_txt2img(self):
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if not self.generators.get('txt2img'):
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from ldm.dream.generator.txt2img import Txt2Img
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self.generators['txt2img'] = Txt2Img(self.model)
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self.generators['txt2img'] = Txt2Img(self.model, self.precision)
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return self.generators['txt2img']
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def _make_inpaint(self):
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if not self.generators.get('inpaint'):
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from ldm.dream.generator.inpaint import Inpaint
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self.generators['inpaint'] = Inpaint(self.model)
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self.generators['inpaint'] = Inpaint(self.model, self.precision)
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return self.generators['inpaint']
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def load_model(self):
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@ -469,7 +478,7 @@ class Generate:
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model = self._load_model_from_config(self.config, self.weights)
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if self.embedding_path is not None:
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model.embedding_manager.load(
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self.embedding_path, self.full_precision
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self.embedding_path, self.precision == 'float32' or self.precision == 'autocast'
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)
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self.model = model.to(self.device)
|
||||
# model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
|
||||
@ -620,15 +629,12 @@ class Generate:
|
||||
model = instantiate_from_config(c.model)
|
||||
m, u = model.load_state_dict(sd, strict=False)
|
||||
|
||||
if self.full_precision:
|
||||
print(
|
||||
'>> Using slower but more accurate full-precision math (--full_precision)'
|
||||
)
|
||||
if self.precision == 'float16':
|
||||
print('Using faster float16 precision')
|
||||
model.to(torch.float16)
|
||||
else:
|
||||
print(
|
||||
'>> Using half precision math. Call with --full_precision to use more accurate but VRAM-intensive full precision.'
|
||||
)
|
||||
model.half()
|
||||
print('Using more accurate float32 precision')
|
||||
|
||||
model.to(self.device)
|
||||
model.eval()
|
||||
|
||||
|
||||
@ -54,6 +54,7 @@ def main():
|
||||
sampler_name = opt.sampler_name,
|
||||
embedding_path = opt.embedding_path,
|
||||
full_precision = opt.full_precision,
|
||||
precision = opt.precision,
|
||||
)
|
||||
except (FileNotFoundError, IOError, KeyError) as e:
|
||||
print(f'{e}. Aborting.')
|
||||
|
||||
@ -119,7 +119,7 @@ def main():
|
||||
# "height": height,
|
||||
# "sampler_name": opt.sampler_name,
|
||||
# "weights": weights,
|
||||
# "full_precision": opt.full_precision,
|
||||
# "precision": opt.precision,
|
||||
# "config": config,
|
||||
# "grid": opt.grid,
|
||||
# "latent_diffusion_weights": opt.laion400m,
|
||||
|
||||
@ -23,14 +23,14 @@ class Container(containers.DeclarativeContainer):
|
||||
model = config.model,
|
||||
sampler_name = config.sampler_name,
|
||||
embedding_path = config.embedding_path,
|
||||
full_precision = config.full_precision
|
||||
precision = config.precision
|
||||
# config = config.model.config,
|
||||
|
||||
# width = config.model.width,
|
||||
# height = config.model.height,
|
||||
# sampler_name = config.model.sampler_name,
|
||||
# weights = config.model.weights,
|
||||
# full_precision = config.model.full_precision,
|
||||
# precision = config.model.precision,
|
||||
# grid = config.model.grid,
|
||||
# seamless = config.model.seamless,
|
||||
# embedding_path = config.model.embedding_path,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user