From 7a33ee2480f2c5b3f67d9a47c7cc1f57bd21a967 Mon Sep 17 00:00:00 2001 From: Bryce Date: Sat, 10 Sep 2022 23:27:22 -0700 Subject: [PATCH] feature: cleaned up logging - cleans up all the logging. hide most of it - create better readme. show example images - save metadata into image --- .gitignore | 3 +- README.md | 75 ++++++++++++++- ...355545_PLMS50_PS7.5_a_scenic_landscape.jpg | Bin 0 -> 33094 bytes ...37692011_PLMS40_PS7.5_a_photo_of_a_dog.jpg | Bin 0 -> 23846 bytes ..._PLMS40_PS7.5_photo_of_a_bowl_of_fruit.jpg | Bin 0 -> 27065 bytes ...7.5_portrait_photo_of_a_freckled_woman.jpg | Bin 0 -> 38693 bytes imaginairy/__init__.py | 6 ++ imaginairy/{imagine.py => api.py} | 42 ++++---- imaginairy/cmd_wrap.py | 64 +++++++++++++ imaginairy/cmds.py | 90 ++++++++++++++++-- imaginairy/models/diffusion/ddpm.py | 6 +- imaginairy/models/diffusion/plms.py | 6 +- imaginairy/modules/diffusionmodules/model.py | 6 +- imaginairy/schema.py | 69 ++++++++++++-- imaginairy/utils.py | 11 ++- setup.py | 2 +- tests/data/beach_at_sainte_adresse.jpg | Bin 0 -> 580451 bytes tests/data/girl_with_a_pearl_earring.jpg | Bin 0 -> 91442 bytes tests/test_imagine.py | 33 +------ 19 files changed, 331 insertions(+), 82 deletions(-) create mode 100644 assets/000019_786355545_PLMS50_PS7.5_a_scenic_landscape.jpg create mode 100644 assets/000032_337692011_PLMS40_PS7.5_a_photo_of_a_dog.jpg create mode 100644 assets/000056_293284644_PLMS40_PS7.5_photo_of_a_bowl_of_fruit.jpg create mode 100644 assets/000078_260972468_PLMS40_PS7.5_portrait_photo_of_a_freckled_woman.jpg rename imaginairy/{imagine.py => api.py} (87%) create mode 100644 imaginairy/cmd_wrap.py create mode 100644 tests/data/beach_at_sainte_adresse.jpg create mode 100644 tests/data/girl_with_a_pearl_earring.jpg diff --git a/.gitignore b/.gitignore index a4affa9..0a61b0f 100644 --- a/.gitignore +++ b/.gitignore @@ -13,4 +13,5 @@ downloads build dist **/*.ckpt -**/*.egg-info \ No newline at end of file +**/*.egg-info +tests/test_output \ No newline at end of file diff --git a/README.md b/README.md index 0a53249..a4e138a 100644 --- a/README.md +++ b/README.md @@ -2,18 +2,83 @@ AI imagined images. +```bash +>> pip install imaginairy +>> imagine "a scenic landscape" "a photo of a dog" "photo of a fruit bowl" "portrait photo of a freckled woman" +``` + + + + +# Features + + - It makes images from text descriptions! + - Generate images either in code or from command line. + - It just works (if you have the right hardware) + - Noisy logs are gone (which was surprisingly hard to accomplish) + - WeightedPrompts let you smash together separate prompts () -# Models +# How To +```python +from imaginairy import imagine_images, imagine_image_files, ImaginePrompt, WeightedPrompt + +prompts = [ + ImaginePrompt("a scenic landscape", seed=1), + ImaginePrompt("a bowl of fruit"), + ImaginePrompt([ + WeightedPrompt("cat", weight=1), + WeightedPrompt("dog", weight=1), + ]) +] +for result in imagine_images(prompts): + # do something + result.save("my_image.jpg") + +# or + +imagine_image_files(prompts, outdir="./my-art") + +``` + +# Requirements + +- Computer with CUDA supported graphics card. ~10 gb video ram +OR +- Apple M1 computer + +# Improvements from CompVis + - img2img actually does # of steps you specify + +# Models Used + - CLIP - LDM - Latent Diffusion - - Stable Diffusion + - Stable Diffusion - https://github.com/CompVis/stable-diffusion # Todo - - add tests + - add safety model - https://github.com/CompVis/stable-diffusion/blob/main/scripts/txt2img.py#L21-L28 - add docs - - remove yaml config - deploy to pypi - - add image describe feature + - add tests + - set up ci (test/lint/format) + - remove yaml config + - performance optimizations https://github.com/huggingface/diffusers/blob/main/docs/source/optimization/fp16.mdx + - Interface improvements + - init-image at command line + - prompt expansion? + - webserver interface (low priority, this is a library) + - Image Generation Features + - image describe 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zpWKO!LnX72UNBh!#z(FyT|X&pc_*ZMe=}Dn?vWJvihW1a)a)^{9ddt}sE6J`&roUZ tN)*pz&*x2&hDXXw5BGCS+lbWnG_VopsBgO5AEBV*%qpiUG6gbE|Jj$Q0P6q% literal 0 HcmV?d00001 diff --git a/imaginairy/__init__.py b/imaginairy/__init__.py index e69de29..4b2fdbb 100644 --- a/imaginairy/__init__.py +++ b/imaginairy/__init__.py @@ -0,0 +1,6 @@ +import os + +os.putenv("PYTORCH_ENABLE_MPS_FALLBACK", "1") + +from api import imagine_images, imagine_image_files +from schema import ImaginePrompt, ImagineResult, WeightedPrompt diff --git a/imaginairy/imagine.py b/imaginairy/api.py similarity index 87% rename from imaginairy/imagine.py rename to imaginairy/api.py index 68b2c80..962efac 100755 --- a/imaginairy/imagine.py +++ b/imaginairy/api.py @@ -37,20 +37,20 @@ logger = logging.getLogger(__name__) def load_model_from_config(config): - ckpt_path = cached_path( - "https://www.googleapis.com/storage/v1/b/aai-blog-files/o/sd-v1-4.ckpt?alt=media" - ) - logger.info(f"Loading model from {ckpt_path}") + url = "https://www.googleapis.com/storage/v1/b/aai-blog-files/o/sd-v1-4.ckpt?alt=media" + ckpt_path = cached_path(url) + logger.info(f"Loading model onto {get_device()} backend...") + logger.debug(f"Loading model from {ckpt_path}") pl_sd = torch.load(ckpt_path, map_location="cpu") if "global_step" in pl_sd: - logger.info(f"Global Step: {pl_sd['global_step']}") + logger.debug(f"Global Step: {pl_sd['global_step']}") sd = pl_sd["state_dict"] model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) if len(m) > 0: - logger.info(f"missing keys: {m}") + logger.debug(f"missing keys: {m}") if len(u) > 0: - logger.info(f"unexpected keys: {u}") + logger.debug(f"unexpected keys: {u}") model.to(get_device()) model.eval() @@ -88,13 +88,17 @@ def imagine_image_files( downsampling_factor=8, precision="autocast", ddim_eta=0.0, - record_steps=False, + record_step_images=False, + output_file_extension="jpg", ): big_path = os.path.join(outdir, "upscaled") os.makedirs(outdir, exist_ok=True) os.makedirs(big_path, exist_ok=True) base_count = len(os.listdir(outdir)) step_count = 0 + output_file_extension = output_file_extension.lower() + if output_file_extension not in {"jpg", "png"}: + raise ValueError("Must output a png or jpg") def _record_steps(samples, i, model, prompt): nonlocal step_count @@ -110,7 +114,7 @@ def imagine_image_files( os.path.join(steps_path, filename) ) - img_callback = _record_steps if record_steps else None + img_callback = _record_steps if record_step_images else None for result in imagine_images( prompts, latent_channels=latent_channels, @@ -120,16 +124,15 @@ def imagine_image_files( img_callback=img_callback, ): prompt = result.prompt - img = result.img basefilename = f"{base_count:06}_{prompt.seed}_{prompt.sampler_type}{prompt.steps}_PS{prompt.prompt_strength}_{prompt_normalized(prompt.prompt_text)}" filepath = os.path.join(outdir, f"{basefilename}.jpg") - img.save(filepath) + result.save(filepath) + logger.info(f" 🖼 saved to: {filepath}") if prompt.upscale: - enlarge_realesrgan2x( - filepath, - os.path.join(big_path, basefilename) + ".jpg", - ) + bigfilepath = (os.path.join(big_path, basefilename) + ".jpg",) + enlarge_realesrgan2x(filepath, bigfilepath) + logger.info(f" upscaled 🖼 saved to: {filepath}") base_count += 1 @@ -146,9 +149,14 @@ def imagine_images( prompts = [prompts] if isinstance(prompts, ImaginePrompt) else prompts _img_callback = None - precision_scope = autocast if precision == "autocast" else nullcontext - with (torch.no_grad(), precision_scope("cuda"), fix_torch_nn_layer_norm()): + precision_scope = ( + autocast + if precision == "autocast" and get_device() in ("cuda", "cpu") + else nullcontext + ) + with (torch.no_grad(), precision_scope(get_device()), fix_torch_nn_layer_norm()): for prompt in prompts: + logger.info(f"Generating {prompt.prompt_description()}") seed_everything(prompt.seed) uc = None if prompt.prompt_strength != 1.0: diff --git a/imaginairy/cmd_wrap.py b/imaginairy/cmd_wrap.py new file mode 100644 index 0000000..2f67e9b --- /dev/null +++ b/imaginairy/cmd_wrap.py @@ -0,0 +1,64 @@ +# only builtin imports allowed at this point since we want to modify +# the environment and code before it's loaded +import importlib.abc +import importlib.util +import logging.config +import os +import site +import sys +import warnings + +# tells pytorch to allow MPS usage (for Mac M1 compatibility) +os.putenv("PYTORCH_ENABLE_MPS_FALLBACK", "1") + + +def disable_transformers_logging(): + """ + Disable `transformers` package custom logging. + + I can't believe it came to this. I tried like four other approaches first + + Loads up the source code from the transformers file and turns it into a module. + We then modify the module. Every other approach (import hooks, custom import function) + loaded the module before it could be modified. + """ + t_logging_path = f"{site.getsitepackages()[0]}/transformers/utils/logging.py" + with open(t_logging_path, "r", encoding="utf-8") as f: + src_code = f.read() + + spec = importlib.util.spec_from_loader("transformers.utils.logging", loader=None) + module = importlib.util.module_from_spec(spec) + + exec(src_code, module.__dict__) + module.get_logger = logging.getLogger + sys.modules["transformers.utils.logging"] = module + + +def disable_pytorch_lighting_custom_logging(): + from pytorch_lightning import _logger + + _logger.setLevel(logging.NOTSET) + + +def filter_torch_warnings(): + warnings.filterwarnings( + "ignore", + category=UserWarning, + message=r"The operator .*?is not currently supported.*", + ) + + +def setup_env(): + disable_transformers_logging() + disable_pytorch_lighting_custom_logging() + filter_torch_warnings() + + +setup_env() + + +from imaginairy.cmds import imagine_cmd # noqa + +# imagine_cmd = disable_transformers_logging_mess()(imagine_cmd) +if __name__ == "__main__": + imagine_cmd() diff --git a/imaginairy/cmds.py b/imaginairy/cmds.py index 4109bce..fde4200 100644 --- a/imaginairy/cmds.py +++ b/imaginairy/cmds.py @@ -1,19 +1,56 @@ -#!/usr/bin/env python -import os - -os.putenv("PYTORCH_ENABLE_MPS_FALLBACK", "1") +import logging.config import click +from imaginairy.imagine import load_model -from imaginairy.imagine import imagine_image_files -from imaginairy.schema import ImaginePrompt +logger = logging.getLogger(__name__) + + +def configure_logging(level="INFO"): + fmt = "%(message)s" + if level == "DEBUG": + fmt = "%(asctime)s [%(levelname)s] %(name)s:%(lineno)d: %(message)s" + + LOGGING_CONFIG = { + "version": 1, + "disable_existing_loggers": True, + "formatters": { + "standard": {"format": fmt}, + }, + "handlers": { + "default": { + "level": "INFO", + "formatter": "standard", + "class": "logging.StreamHandler", + "stream": "ext://sys.stdout", # Default is stderr + }, + }, + "loggers": { + "": { # root logger + "handlers": ["default"], + "level": "WARNING", + "propagate": False, + }, + "imaginairy": {"handlers": ["default"], "level": level, "propagate": False}, + "transformers.modeling_utils": { + "handlers": ["default"], + "level": "ERROR", + "propagate": False, + }, + }, + } + logging.config.dictConfig(LOGGING_CONFIG) @click.command() -@click.argument("prompt_texts", default=None, nargs=-1) +@click.argument("prompt_texts", nargs=-1) @click.option("--outdir", default="./outputs", help="where to write results to") @click.option( - "-r", "--repeats", default=1, type=int, help="How many times to repeat the renders" + "-r", + "--repeats", + default=1, + type=int, + help="How many times to repeat the renders. If you provide two prompts and --repeat=3 then six images will be generated", ) @click.option( "-h", @@ -27,8 +64,9 @@ from imaginairy.schema import ImaginePrompt ) @click.option( "--steps", - default=50, + default=40, type=int, + show_default=True, help="How many diffusion steps to run. More steps, more detail, but with diminishing returns", ) @click.option( @@ -40,10 +78,29 @@ from imaginairy.schema import ImaginePrompt @click.option( "--prompt-strength", default=7.5, + show_default=True, help="How closely to follow the prompt. Image looks unnatural at higher values", ) -@click.option("--sampler-type", default="PLMS", help="What sampling strategy to use") +@click.option( + "--sampler-type", + default="PLMS", + type=click.Choice(["PLMS", "DDIM"]), + help="What sampling strategy to use", +) @click.option("--ddim-eta", default=0.0, type=float) +@click.option( + "--log-level", + default="INFO", + type=click.Choice(["DEBUG", "INFO", "WARNING", "ERROR"]), + help="What level of logs to show.", +) +@click.option( + "--show-work", + default=["none"], + type=click.Choice(["none", "images", "video"]), + multiple=True, + help="Make a video showing the image being created", +) def imagine_cmd( prompt_texts, outdir, @@ -55,9 +112,21 @@ def imagine_cmd( prompt_strength, sampler_type, ddim_eta, + log_level, + show_work, ): """Render an image""" + configure_logging(log_level) + from imaginairy.imagine import imagine_image_files + from imaginairy.schema import ImaginePrompt + + total_image_count = len(prompt_texts) * repeats + logger.info( + f"🤖🧠 received {len(prompt_texts)} prompt(s) and will repeat them {repeats} times to create {total_image_count} images." + ) + prompts = [] + load_model() for _ in range(repeats): for prompt_text in prompt_texts: prompt = ImaginePrompt( @@ -77,6 +146,7 @@ def imagine_cmd( prompts, outdir=outdir, ddim_eta=ddim_eta, + record_step_images="images" in show_work, ) diff --git a/imaginairy/models/diffusion/ddpm.py b/imaginairy/models/diffusion/ddpm.py index db0b49e..985fab4 100644 --- a/imaginairy/models/diffusion/ddpm.py +++ b/imaginairy/models/diffusion/ddpm.py @@ -77,7 +77,7 @@ class DDPM(pl.LightningModule): "x0", ], 'currently only supporting "eps" and "x0"' self.parameterization = parameterization - logger.info( + logger.debug( f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode" ) self.cond_stage_model = None @@ -309,10 +309,10 @@ class LatentDiffusion(DDPM): def instantiate_cond_stage(self, config): if not self.cond_stage_trainable: if config == "__is_first_stage__": - logger.info("Using first stage also as cond stage.") + logger.debug("Using first stage also as cond stage.") self.cond_stage_model = self.first_stage_model elif config == "__is_unconditional__": - logger.info( + logger.debug( f"Training {self.__class__.__name__} as an unconditional model." ) self.cond_stage_model = None diff --git a/imaginairy/models/diffusion/plms.py b/imaginairy/models/diffusion/plms.py index 6506c53..0ff6841 100644 --- a/imaginairy/models/diffusion/plms.py +++ b/imaginairy/models/diffusion/plms.py @@ -129,7 +129,7 @@ class PLMSSampler(object): # sampling C, H, W = shape size = (batch_size, C, H, W) - logger.info(f"Data shape for PLMS sampling is {size}") + logger.debug(f"Data shape for PLMS sampling is {size}") samples, intermediates = self.plms_sampling( conditioning, @@ -202,9 +202,9 @@ class PLMSSampler(object): else np.flip(timesteps) ) total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] - logger.info(f"Running PLMS Sampling with {total_steps} timesteps") + logger.debug(f"Running PLMS Sampling with {total_steps} timesteps") - iterator = tqdm(time_range, desc="PLMS Sampler", total=total_steps) + iterator = tqdm(time_range, desc=" PLMS Sampler", total=total_steps) old_eps = [] for i, step in enumerate(iterator): diff --git a/imaginairy/modules/diffusionmodules/model.py b/imaginairy/modules/diffusionmodules/model.py index 5a8a464..6fbab45 100644 --- a/imaginairy/modules/diffusionmodules/model.py +++ b/imaginairy/modules/diffusionmodules/model.py @@ -196,7 +196,7 @@ class AttnBlock(nn.Module): def make_attn(in_channels, attn_type="vanilla"): assert attn_type in ["vanilla", "linear", "none"], f"attn_type {attn_type} unknown" - logger.info( + logger.debug( f"making attention of type '{attn_type}' with {in_channels} in_channels" ) if attn_type == "vanilla": @@ -361,7 +361,7 @@ class Decoder(nn.Module): block_in = ch * ch_mult[self.num_resolutions - 1] curr_res = resolution // 2 ** (self.num_resolutions - 1) self.z_shape = (1, z_channels, curr_res, curr_res) - logger.info( + logger.debug( f"Working with z of shape {self.z_shape} = {np.prod(self.z_shape)} dimensions." ) @@ -516,7 +516,7 @@ class Upsampler(nn.Module): assert out_size >= in_size num_blocks = int(np.log2(out_size // in_size)) + 1 factor_up = 1.0 + (out_size % in_size) - logger.info( + logger.debug( f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}" ) self.rescaler = LatentRescaler( diff --git a/imaginairy/schema.py b/imaginairy/schema.py index fb1e767..cc61f84 100644 --- a/imaginairy/schema.py +++ b/imaginairy/schema.py @@ -1,7 +1,12 @@ import hashlib +import json import random +from datetime import datetime, timezone import numpy +from PIL.Image import Exif + +from imaginairy.utils import get_device, get_device_name class WeightedPrompt: @@ -17,35 +22,33 @@ class ImaginePrompt: def __init__( self, prompt=None, - seed=None, prompt_strength=7.5, - sampler_type="PLMS", init_image=None, init_image_strength=0.3, + seed=None, steps=50, height=512, width=512, upscale=False, fix_faces=False, - parts=None, + sampler_type="PLMS", ): prompt = prompt if prompt is not None else "a scenic landscape" if isinstance(prompt, str): self.prompts = [WeightedPrompt(prompt, 1)] else: self.prompts = prompt + self.prompts.sort(key=lambda p: p.weight, reverse=True) + self.prompt_strength = prompt_strength self.init_image = init_image self.init_image_strength = init_image_strength - self.prompts.sort(key=lambda p: p.weight, reverse=True) self.seed = random.randint(1, 1_000_000_000) if seed is None else seed - self.prompt_strength = prompt_strength - self.sampler_type = sampler_type self.steps = steps self.height = height self.width = width self.upscale = upscale self.fix_faces = fix_faces - self.parts = parts or {} + self.sampler_type = sampler_type @property def prompt_text(self): @@ -53,11 +56,46 @@ class ImaginePrompt: return self.prompts[0].text return "|".join(str(p) for p in self.prompts) + def prompt_description(self): + return ( + f'🖼 : "{self.prompt_text}" {self.width}x{self.height}px ' + f"seed:{self.seed} prompt-strength:{self.prompt_strength} steps:{self.steps} sampler-type:{self.sampler_type}" + ) + + def as_dict(self): + prompts = [(p.weight, p.text) for p in self.prompts] + return { + "software": "imaginairy", + "prompts": prompts, + "prompt_strength": self.prompt_strength, + "init_image": self.init_image, + "init_image_strength": self.init_image_strength, + "seed": self.seed, + "steps": self.steps, + "height": self.height, + "width": self.width, + "upscale": self.upscale, + "fix_faces": self.fix_faces, + "sampler_type": self.sampler_type, + } + + +class ExifCodes: + """https://www.awaresystems.be/imaging/tiff/tifftags/baseline.html""" + ImageDescription = 0x010E + Software = 0x0131 + DateTime = 0x0132 + HostComputer = 0x013C + UserComment = 0x9286 + class ImagineResult: - def __init__(self, img, prompt): + def __init__(self, img, prompt: ImaginePrompt): self.img = img self.prompt = prompt + self.created_at = datetime.utcnow().replace(tzinfo=timezone.utc) + self.torch_backend = get_device() + self.hardware_name = get_device_name(get_device()) def cv2_img(self): open_cv_image = numpy.array(self.img) @@ -68,3 +106,18 @@ class ImagineResult: def md5(self): return hashlib.md5(self.img.tobytes()).hexdigest() + + def metadata_dict(self): + return { + "prompt": self.prompt.as_dict(), + } + + def save(self, save_path): + exif = Exif() + exif[ExifCodes.ImageDescription] = self.prompt.prompt_description() + exif[ExifCodes.UserComment] = json.dumps(self.metadata_dict()) + # help future web scrapes not ingest AI generated art + exif[ExifCodes.Software] = "Imaginairy / Stable Diffusion v1.4" + exif[ExifCodes.DateTime] = self.created_at.isoformat(sep=" ")[:19] + exif[ExifCodes.HostComputer] = f"{self.torch_backend}:{self.hardware_name}" + self.img.save(save_path, exif=exif) diff --git a/imaginairy/utils.py b/imaginairy/utils.py index 068c8e0..8c084fa 100644 --- a/imaginairy/utils.py +++ b/imaginairy/utils.py @@ -1,6 +1,6 @@ -import os import importlib import logging +import platform from contextlib import contextmanager from functools import lru_cache from typing import List, Optional @@ -21,9 +21,16 @@ def get_device(): return "cpu" +@lru_cache() +def get_device_name(device_type): + if device_type == "cuda": + return torch.cuda.get_device_name(0) + return platform.processor() + + def log_params(model): total_params = sum(p.numel() for p in model.parameters()) - logger.info(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.") + logger.debug(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.") def instantiate_from_config(config): diff --git a/setup.py b/setup.py index b99b502..218b2cb 100644 --- a/setup.py +++ b/setup.py @@ -6,7 +6,7 @@ setup( description="AI imagined images.", packages=find_packages(include=("imaginairy", "imaginairy.*")), entry_points={ - "console_scripts": ["imagine=imaginairy.cmds:imagine_cmd"], + "console_scripts": ["imagine=imaginairy.cmd_wrap:imagine_cmd"], }, package_data={"imaginairy": ["configs/*.yaml"]}, install_requires=[ diff --git a/tests/data/beach_at_sainte_adresse.jpg b/tests/data/beach_at_sainte_adresse.jpg new file mode 100644 index 0000000000000000000000000000000000000000..d74cd44862e4be1147816301d0acd1cc409cb6ce GIT binary patch literal 580451 zcmb5VWl$T+7e0)8D6WO#9yEbMf#ME@;K41B0Ht`b7I$}oThO2h1qxi;-3gQyic5>O zh1=fyzrX)G?}zu}`|Rv9XZP%$*_}Bb&g?w<@8-W>@M!e3bhYsC3Gndn|7&>vZsV!r z5#aw1|6_vxfr#*bOiV;XNJK(RLh^q*DLEMlDLE+#2^l3BImQ3*pG8GULG{1E|APEK zTM38>35h94Nl5?ilK)@y?=T)M8PP6DJ|R929sw;rAuayDqj*p8@CXS1cRIZP7lcIk z1jHnGq-6hj+3@lHzs~<|ML1f z8ZoZ7rNBa+2DXn6>VVVnq|k>i#5{R%QXQY@PZeg}q|0CBAzhc(Oe{K!c<#k0W(e7y zv+?5Q?JwU(FKT&tzzpTX|HUKUK8~`~+5npc&K%HYnBb9AUVH1;8uHF0NU5bKvV3t@ z=@-`R=X?+5=y+P}_n0efQ+vthsG!8DREhYS$h)>W1FxIT0Vx0DR@>=S&(zqn!49)# z_(?gb57x`5VfbUdgk^pp1VxXkYW62 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a/tests/test_imagine.py +++ b/tests/test_imagine.py @@ -1,5 +1,5 @@ -from imaginairy.imagine import imagine_images, imagine_image_files -from imaginairy.schema import ImaginePrompt, WeightedPrompt +from imaginairy.api import imagine_images, imagine_image_files +from imaginairy.schema import ImaginePrompt from . import TESTS_FOLDER @@ -24,43 +24,18 @@ def test_img_to_img(): sampler_type="DDIM", ) out_folder = f"{TESTS_FOLDER}/test_output" - out_folder = "/home/bryce/Mounts/drennanfiles/art/tests" imagine_image_files(prompt, outdir=out_folder) def test_img_to_file(): prompt = ImaginePrompt( - [ - WeightedPrompt( - "an old growth forest, diffuse light poking through the canopy. high-resolution, nature photography, nat geo photo" - ) - ], - # init_image=f"{TESTS_FOLDER}/data/beach_at_sainte_adresse.jpg", - init_image_strength=0.5, + "an old growth forest, diffuse light poking through the canopy. high-resolution, nature photography, nat geo photo", width=512 + 64, height=512 - 64, steps=50, - # seed=2, + seed=2, sampler_type="PLMS", upscale=True, ) out_folder = f"{TESTS_FOLDER}/test_output" - out_folder = "/home/bryce/Mounts/drennanfiles/art/tests" imagine_image_files(prompt, outdir=out_folder) - - -def test_img_conditioning(): - prompt = ImaginePrompt( - "photo", - init_image=f"{TESTS_FOLDER}/data/beach_at_sainte_adresse.jpg", - init_image_strength=0.5, - width=512 + 64, - height=512 - 64, - steps=50, - # seed=2, - sampler_type="PLMS", - upscale=True, - ) - out_folder = f"{TESTS_FOLDER}/test_output" - out_folder = "/home/bryce/Mounts/drennanfiles/art/tests" - imagine_image_files(prompt, outdir=out_folder, record_steps=True)