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https://github.com/afiaka87/clip-guided-diffusion
A CLI tool/python module for generating images from text using guided diffusion and CLIP from OpenAI.
https://github.com/afiaka87/clip-guided-diffusion
artificial-intelligence deep-learning diffusion image-generation multimodal multimodality openai openai-clip text-to-image text-to-image-synthesis
Last synced: 2 months ago
JSON representation
A CLI tool/python module for generating images from text using guided diffusion and CLIP from OpenAI.
- Host: GitHub
- URL: https://github.com/afiaka87/clip-guided-diffusion
- Owner: afiaka87
- License: mit
- Created: 2021-07-21T02:04:09.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-02-08T06:13:56.000Z (over 2 years ago)
- Last Synced: 2024-07-03T10:17:30.899Z (3 months ago)
- Topics: artificial-intelligence, deep-learning, diffusion, image-generation, multimodal, multimodality, openai, openai-clip, text-to-image, text-to-image-synthesis
- Language: Python
- Homepage:
- Size: 51.2 MB
- Stars: 449
- Watchers: 12
- Forks: 62
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-CLIP - [code
README
# CLIP Guided Diffusion
From [@crowsonkb](https://github.com/crowsonkb).
Disclaimer: I'm redirecting efforts to [pyglide](https://replicate.ai/afiaka87/pyglide) and may be slow to address bugs here.
I also recommend looking at [@crowsonkb's](https://github.com/crowsonkb) [v-diffusion-pytorch](https://github.com/crowsonkb/v-diffusion-pytorch).
See captions and more generations in the [Gallery](/images/README.md).
## Install
```sh
git clone https://github.com/afiaka87/clip-guided-diffusion.git
cd clip-guided-diffusion
git clone https://github.com/crowsonkb/guided-diffusion.git
pip3 install -e guided-diffusion
python3 setup.py install
```
## Run
`cgd -txt "Alien friend by Odilon Redo"`
A gif of the full run will be saved to `./outputs/caption_{j}.gif` by default.
- `./outputs` will contain all intermediate outputs
- `current.png` will contain the current generation.
- (optional) Provide **`--wandb_project `** to enable logging intermediate outputs to wandb. Requires free account. URL to run will be provided in CLI - [example run](https://wandb.ai/dalle-pytorch-replicate/red_ball_cgd/reports/CLIP-Guided-Diffusion--VmlldzoxMDc1MjMz)
- `~/.cache/clip-guided-diffusion/` will contain downloaded checkpoints from OpenAI/Katherine Crowson.
## Usage - CLI
### Text to image generation
`--prompts` / `-txts`
`--image_size` / `-size`
`cgd --image_size 256 --prompts "32K HUHD Mushroom"`
### Text to image generation (multiple prompts with weights)
- multiple prompts can be specified with the `|` character.
- you may optionally specify a weight for each prompt using a `:` character.
- e.g. `cgd --prompts "Noun to visualize:1.0|style:0.1|location:0.1|something you dont want:-0.1"`
- weights must not sum to 0
`cgd -txt "32K HUHD Mushroom|Green grass:-0.1"`
### CPU
- **Using a CPU will take a very long time** compared to using a GPU.
`cgd --device cpu --prompt "Some text to be generated"`
### CUDA GPU
`cgd --prompt "Theres no need to specify a device, it will be chosen automatically"`
### Iterations/Steps (Timestep Respacing)
`--timestep_respacing` or `-respace` (default: `1000`)
- Uses fewer timesteps over the same diffusion schedule. Sacrifices accuracy/alignment for quicker runtime.
- options: - `25`, `50`, `150`, `250`, `500`, `1000`, `ddim25`,`ddim50`,`ddim150`, `ddim250`,`ddim500`,`ddim1000`
- (default: `1000`)
- prepending a number with `ddim` will use the ddim scheduler. e.g. `ddim25` will use the 25 timstep ddim scheduler. This method may be better at shorter timestep_respacing values.
### Existing image
#### `--init_image`/`-init`
- Blend an image with the diffusion for a number of steps.
#### `--skip_timesteps`/`-skip`
The number of timesteps to spend blending the image with the guided-diffusion samples.
Must be less than `--timestep_respacing` and greater than 0.
Good values using timestep_respacing of 1000 are 250 to 500.
- `-respace 1000 -skip 500`
- `-respace 500 -skip 250`
- `-respace 250 -skip 125`
- `-respace 125 -skip 75`
#### (optional)`--init_scale`/`-is`
To enable a VGG perceptual loss after the blending, you must specify an `--init_scale` value. 1000 seems to work well.
```sh
cgd --prompts "A mushroom in the style of Vincent Van Gogh" \
--timestep_respacing 1000 \
--init_image "images/32K_HUHD_Mushroom.png" \
--init_scale 1000 \
--skip_timesteps 350
```
### Image size
- options: `64, 128, 256, 512 pixels (square)`
- **Note about 64x64** when using the 64x64 checkpoint, the cosine noise scheduler is used. For unclear reasons, this noise scheduler requires different values for `--clip_guidance_scale` and `--tv_scale`. I recommend starting with `-cgs 5 -tvs 0.00001` and experimenting from around there. **`--clip_guidance_scale` and `--tv_scale` will require experimentation.**
- For all other checkpoints, clip_guidance_scale seems to work well around 1000-2000 and tv_scale at 0, 100, 150 or 200
```sh
cgd --init_image=images/32K_HUHD_Mushroom.png \
--skip_timesteps=500 \
--image_size 64 \
--prompt "8K HUHD Mushroom"
```
_resized to 200 pixels for visibility_
```sh
cgd --image_size 512 --prompt "8K HUHD Mushroom"
```
**New: Non-square Generations (experimental)**
Generate portrait or landscape images by specifying a number to offset the width and/or height.
- offset should be a multiple of 16 for image sizes 64x64, 128x128
- offset should be a multiple of 32 for image sizes 256x256, 512x512
- may cause NaN/Inf errors.
- a positive offset will require more memory.
- a _negative_ offset uses less memory and is faster.
```sh
my_caption="a photo of beautiful green hills and a sunset, taken with a blackberry in 2004"
cgd --prompts "$my_caption" \
--image_size 128 \
--width_offset 32
```
## Full Usage - Python
```python
# Initialize diffusion generator
from cgd import clip_guided_diffusion
import cgd_util
cgd_generator = clip_guided_diffusion(
prompts=["an image of a fox in a forest"],
image_prompts=["image_to_compare_with_clip.png"],
batch_size=1,
clip_guidance_scale=1500,
sat_scale=0,
tv_scale=150,
init_scale=1000,
range_scale=50,
image_size=256,
class_cond=False,
randomize_class=False, # only works with class conditioned checkpoints
cutout_power=1.0,
num_cutouts=16,
timestep_respacing="1000",
seed=0,
diffusion_steps=1000, # dont change this
skip_timesteps=400,
init_image="image_to_blend_and_compare_with_vgg.png",
clip_model_name="ViT-B/16",
dropout=0.0,
device="cuda",
prefix_path="store_images/",
wandb_project=None,
wandb_entity=None,
progress=True,
)
prefix_path.mkdir(exist_ok=True)
list(enumerate(tqdm(cgd_generator))) # iterate over generator
```
## Full Usage - CLI
```sh
usage: cgd [-h] [--prompts PROMPTS] [--image_prompts IMAGE_PROMPTS]
[--image_size IMAGE_SIZE] [--init_image INIT_IMAGE]
[--init_scale INIT_SCALE] [--skip_timesteps SKIP_TIMESTEPS]
[--prefix PREFIX] [--checkpoints_dir CHECKPOINTS_DIR]
[--batch_size BATCH_SIZE]
[--clip_guidance_scale CLIP_GUIDANCE_SCALE] [--tv_scale TV_SCALE]
[--range_scale RANGE_SCALE] [--sat_scale SAT_SCALE] [--seed SEED]
[--save_frequency SAVE_FREQUENCY]
[--diffusion_steps DIFFUSION_STEPS]
[--timestep_respacing TIMESTEP_RESPACING]
[--num_cutouts NUM_CUTOUTS] [--cutout_power CUTOUT_POWER]
[--clip_model CLIP_MODEL] [--uncond]
[--noise_schedule NOISE_SCHEDULE] [--dropout DROPOUT]
[--device DEVICE] [--wandb_project WANDB_PROJECT]
[--wandb_entity WANDB_ENTITY] [--height_offset HEIGHT_OFFSET]
[--width_offset WIDTH_OFFSET] [--use_augs] [--use_magnitude]
[--quiet]
optional arguments:
-h, --help show this help message and exit
--prompts PROMPTS, -txts PROMPTS
the prompt/s to reward paired with weights. e.g. 'My
text:0.5|Other text:-0.5' (default: )
--image_prompts IMAGE_PROMPTS, -imgs IMAGE_PROMPTS
the image prompt/s to reward paired with weights. e.g.
'img1.png:0.5,img2.png:-0.5' (default: )
--image_size IMAGE_SIZE, -size IMAGE_SIZE
Diffusion image size. Must be one of [64, 128, 256,
512]. (default: 128)
--init_image INIT_IMAGE, -init INIT_IMAGE
Blend an image with diffusion for n steps (default: )
--init_scale INIT_SCALE, -is INIT_SCALE
(optional) Perceptual loss scale for init image.
(default: 0)
--skip_timesteps SKIP_TIMESTEPS, -skip SKIP_TIMESTEPS
Number of timesteps to blend image for. CLIP guidance
occurs after this. (default: 0)
--prefix PREFIX, -dir PREFIX
output directory (default: outputs)
--checkpoints_dir CHECKPOINTS_DIR, -ckpts CHECKPOINTS_DIR
Path subdirectory containing checkpoints. (default:
/home/samsepiol/.cache/clip-guided-diffusion)
--batch_size BATCH_SIZE, -bs BATCH_SIZE
the batch size (default: 1)
--clip_guidance_scale CLIP_GUIDANCE_SCALE, -cgs CLIP_GUIDANCE_SCALE
Scale for CLIP spherical distance loss. Values will
need tinkering for different settings. (default: 1000)
--tv_scale TV_SCALE, -tvs TV_SCALE
Controls the smoothness of the final output. (default:
150.0)
--range_scale RANGE_SCALE, -rs RANGE_SCALE
Controls how far out of RGB range values may get.
(default: 50.0)
--sat_scale SAT_SCALE, -sats SAT_SCALE
Controls how much saturation is allowed. Used for
ddim. From @nshepperd. (default: 0.0)
--seed SEED, -seed SEED
Random number seed (default: 0)
--save_frequency SAVE_FREQUENCY, -freq SAVE_FREQUENCY
Save frequency (default: 1)
--diffusion_steps DIFFUSION_STEPS, -steps DIFFUSION_STEPS
Diffusion steps (default: 1000)
--timestep_respacing TIMESTEP_RESPACING, -respace TIMESTEP_RESPACING
Timestep respacing (default: 1000)
--num_cutouts NUM_CUTOUTS, -cutn NUM_CUTOUTS
Number of randomly cut patches to distort from
diffusion. (default: 16)
--cutout_power CUTOUT_POWER, -cutpow CUTOUT_POWER
Cutout size power (default: 1.0)
--clip_model CLIP_MODEL, -clip CLIP_MODEL
clip model name. Should be one of: ('ViT-B/16',
'ViT-B/32', 'RN50', 'RN101', 'RN50x4', 'RN50x16') or a
checkpoint filename ending in `.pt` (default:
ViT-B/32)
--uncond, -uncond Use finetuned unconditional checkpoints from OpenAI
(256px) and Katherine Crowson (512px) (default: False)
--noise_schedule NOISE_SCHEDULE, -sched NOISE_SCHEDULE
Specify noise schedule. Either 'linear' or 'cosine'.
(default: linear)
--dropout DROPOUT, -drop DROPOUT
Amount of dropout to apply. (default: 0.0)
--device DEVICE, -dev DEVICE
Device to use. Either cpu or cuda. (default: )
--wandb_project WANDB_PROJECT, -proj WANDB_PROJECT
Name W&B will use when saving results. e.g.
`--wandb_project "my_project"` (default: None)
--wandb_entity WANDB_ENTITY, -ent WANDB_ENTITY
(optional) Name of W&B team/entity to log to.
(default: None)
--height_offset HEIGHT_OFFSET, -ht HEIGHT_OFFSET
Height offset for image (default: 0)
--width_offset WIDTH_OFFSET, -wd WIDTH_OFFSET
Width offset for image (default: 0)
--use_augs, -augs Uses augmentations from the `quick` clip guided
diffusion notebook (default: False)
--use_magnitude, -mag
Uses magnitude of the gradient (default: False)
--quiet, -q Suppress output. (default: False)
```
## Development
```sh
git clone https://github.com/afiaka87/clip-guided-diffusion.git
cd clip-guided-diffusion
git clone https://github.com/afiaka87/guided-diffusion.git
python3 -m venv cgd_venv
source cgd_venv/bin/activate
pip install -r requirements.txt
pip install -e guided-diffusion
```
### Run integration tests
- Some tests require a GPU; you may ignore them if you dont have one.
```sh
python -m unittest discover
```