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155 lines
5.0 KiB
Python
155 lines
5.0 KiB
Python
import os.path
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import torch
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from PIL import ImageDraw
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from imaginairy import ImaginePrompt, LazyLoadingImage, imagine, imagine_image_files
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from imaginairy.api import load_model
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from imaginairy.img_log import ImageLoggingContext, filesafe_text, log_latent
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from imaginairy.modules.clip_embedders import FrozenCLIPEmbedder
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from imaginairy.samplers.ddim import DDIMSampler
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from imaginairy.utils import get_device, pillow_img_to_torch_image
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from tests import TESTS_FOLDER
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def experiment_text_conditioning_combos():
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"""
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Can we do math with the embeddings?
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Yes. it works but doesn't look great.
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"""
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embedder = FrozenCLIPEmbedder()
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embedder.to(get_device())
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beach_e = embedder.encode(["a beach"])
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beach_water_e = embedder.encode(["a beach. ocean, waves, water"])
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waterness = beach_water_e - beach_e
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waterless_beach = beach_e - waterness
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imagine_image_files(
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[ImaginePrompt("waterless_beach", conditioning=waterless_beach, seed=1)],
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outdir=f"{TESTS_FOLDER}/test_output",
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)
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imagine_image_files(
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[ImaginePrompt("waterness", conditioning=waterness, seed=1)],
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outdir=f"{TESTS_FOLDER}/test_output",
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)
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imagine_image_files(
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[ImaginePrompt("beach", conditioning=beach_e, seed=1)],
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outdir=f"{TESTS_FOLDER}/test_output",
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)
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def experiment_step_repeats():
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"""
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Run the same step over and over on an image without noise
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Removes detail from the image.
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"""
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model = load_model()
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model.to(get_device())
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model.eval()
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embedder = FrozenCLIPEmbedder()
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embedder.to(get_device())
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sampler = DDIMSampler(model)
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sampler.make_schedule(1000)
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img = LazyLoadingImage(filepath=f"{TESTS_FOLDER}/data/beach_at_sainte_adresse.jpg")
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init_image, _, _ = pillow_img_to_torch_image(
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img,
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)
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init_image = init_image.to(get_device())
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init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image))
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log_latent(init_latent, "init_latent")
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base_count = 1
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neutral_embedding = embedder.encode([""])
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outdir = f"{TESTS_FOLDER}/test_output"
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def _record_step(img, description, step_count, prompt):
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steps_path = os.path.join(outdir, "steps", f"{base_count:08}_S{prompt.seed}")
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os.makedirs(steps_path, exist_ok=True)
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filename = f"{base_count:08}_S{prompt.seed}_step{step_count:04}_{filesafe_text(description)[:40]}.png"
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destination = os.path.join(steps_path, filename)
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draw = ImageDraw.Draw(img)
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draw.text((10, 10), str(description))
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img.save(destination)
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with ImageLoggingContext(
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prompt=ImaginePrompt(""),
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model=model,
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img_callback=_record_step,
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):
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x_prev = init_latent
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index = 50
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base_count = index
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t = torch.Tensor([index]).to(get_device())
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# noise_pred = model.apply_model(init_latent, t, neutral_embedding)
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# log_latent(noise_pred, "noise prediction")
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for _ in range(100):
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x_prev, pred_x0 = sampler.p_sample_ddim(x_prev, neutral_embedding, t, index)
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log_latent(pred_x0, "pred_x0")
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x_prev = pred_x0
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def experiment_repeated_img_2_img():
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"""
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Experiment with putting an image repeatedly through image2image
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It creates screwy images
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"""
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outdir = f"{TESTS_FOLDER}/test_output/img2img2img"
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img = LazyLoadingImage(filepath=f"{TESTS_FOLDER}/data/beach_at_sainte_adresse.jpg")
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img.save(f"{outdir}/0.png")
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for step_num in range(50):
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prompt = ImaginePrompt(
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"Beach at Sainte Adresse. hyperealistic photo. sharp focus, canon 5d",
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init_image=img,
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init_image_strength=0.50,
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width=512,
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height=512,
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steps=50,
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sampler_type="DDIM",
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)
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result = next(imagine(prompt))
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img = result.img
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os.makedirs(outdir, exist_ok=True)
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img.save(f"{outdir}/{step_num:04}.png")
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def experiment_superresolution():
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"""
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Try to trick it into making a superresolution image
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Did not work, resulting image was more blurry
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# i put this into the api.py file hardcoded
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row_a = torch.tensor([1, 0]).repeat(32)
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row_b = torch.tensor([0, 1]).repeat(32)
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grid = torch.stack([row_a, row_b]).repeat(32, 1)
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mask = grid
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mask = mask.to(get_device())
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"""
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description = "a black and white photo of a dog's face"
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# image was a quarter of existing image
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img = LazyLoadingImage(filepath=f"{TESTS_FOLDER}/../outputs/dog02.jpg")
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# todo: try with 1000 mask at image resultion (rencoding entire image+predicted image at every step)
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# todo: use a gaussian pyramid and only include the "high-detail" level of the pyramid into the next step
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prompt = ImaginePrompt(
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description,
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init_image=img,
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init_image_strength=0.8,
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width=512,
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height=512,
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steps=50,
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seed=1,
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sampler_type="DDIM",
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)
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out_folder = f"{TESTS_FOLDER}/test_output"
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imagine_image_files(prompt, outdir=out_folder)
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