sd make change sampler work
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@ -4,12 +4,13 @@ import PIL.Image
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import cv2
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import cv2
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import numpy as np
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import numpy as np
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import torch
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import torch
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from diffusers import PNDMScheduler, DDIMScheduler
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from loguru import logger
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from loguru import logger
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from lama_cleaner.helper import norm_img
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from lama_cleaner.helper import norm_img
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from lama_cleaner.model.base import InpaintModel
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from lama_cleaner.model.base import InpaintModel
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from lama_cleaner.schema import Config
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from lama_cleaner.schema import Config, SDSampler
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#
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#
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@ -43,13 +44,12 @@ class SD(InpaintModel):
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min_size = 512
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min_size = 512
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def init_model(self, device: torch.device, **kwargs):
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def init_model(self, device: torch.device, **kwargs):
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# return
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from .sd_pipeline import StableDiffusionInpaintPipeline
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from .sd_pipeline import StableDiffusionInpaintPipeline
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self.model = StableDiffusionInpaintPipeline.from_pretrained(
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self.model = StableDiffusionInpaintPipeline.from_pretrained(
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self.model_id_or_path,
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self.model_id_or_path,
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revision="fp16",
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revision="fp16" if torch.cuda.is_available() else 'main',
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torch_dtype=torch.float16,
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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use_auth_token=kwargs["hf_access_token"],
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use_auth_token=kwargs["hf_access_token"],
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)
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)
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# https://huggingface.co/docs/diffusers/v0.3.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
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# https://huggingface.co/docs/diffusers/v0.3.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
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@ -59,7 +59,6 @@ class SD(InpaintModel):
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@torch.cuda.amp.autocast()
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@torch.cuda.amp.autocast()
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def forward(self, image, mask, config: Config):
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def forward(self, image, mask, config: Config):
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# return image
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"""Input image and output image have same size
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"""Input image and output image have same size
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image: [H, W, C] RGB
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image: [H, W, C] RGB
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mask: [H, W, 1] 255 means area to repaint
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mask: [H, W, 1] 255 means area to repaint
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@ -76,9 +75,30 @@ class SD(InpaintModel):
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#
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#
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# image = torch.from_numpy(image).unsqueeze(0).to(self.device)
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# image = torch.from_numpy(image).unsqueeze(0).to(self.device)
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# mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)
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# mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)
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# import time
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# time.sleep(2)
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if config.sd_sampler == SDSampler.ddim:
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# return image
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scheduler = DDIMScheduler(
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beta_start=0.00085,
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beta_end=0.012,
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beta_schedule="scaled_linear",
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clip_sample=False,
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set_alpha_to_one=False,
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)
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elif config.sd_sampler == SDSampler.pndm:
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PNDM_kwargs = {
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"tensor_format": "pt",
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"beta_schedule": "scaled_linear",
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"beta_start": 0.00085,
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"beta_end": 0.012,
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"num_train_timesteps": 1000,
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"skip_prk_steps": True
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}
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scheduler = PNDMScheduler(**PNDM_kwargs)
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else:
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raise ValueError(config.sd_sampler)
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self.model.scheduler = scheduler
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seed = config.sd_seed
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seed = config.sd_seed
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random.seed(seed)
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random.seed(seed)
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np.random.seed(seed)
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np.random.seed(seed)
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@ -14,6 +14,11 @@ class LDMSampler(str, Enum):
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plms = 'plms'
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plms = 'plms'
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class SDSampler(str, Enum):
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ddim = 'ddim'
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pndm = 'pndm'
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class Config(BaseModel):
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class Config(BaseModel):
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ldm_steps: int
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ldm_steps: int
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ldm_sampler: str = LDMSampler.plms
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ldm_sampler: str = LDMSampler.plms
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@ -35,6 +40,6 @@ class Config(BaseModel):
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sd_strength: float = 0.75
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sd_strength: float = 0.75
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sd_steps: int = 50
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sd_steps: int = 50
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sd_guidance_scale: float = 7.5
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sd_guidance_scale: float = 7.5
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sd_sampler: str = 'ddim' # ddim/pndm
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sd_sampler: str = SDSampler.ddim
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# -1 mean random seed
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# -1 mean random seed
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sd_seed: int = 42
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sd_seed: int = 42
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@ -6,7 +6,7 @@ import pytest
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import torch
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import torch
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from lama_cleaner.model_manager import ModelManager
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from lama_cleaner.model_manager import ModelManager
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from lama_cleaner.schema import Config, HDStrategy, LDMSampler
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from lama_cleaner.schema import Config, HDStrategy, LDMSampler, SDSampler
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current_dir = Path(__file__).parent.absolute().resolve()
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current_dir = Path(__file__).parent.absolute().resolve()
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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@ -155,25 +155,27 @@ def test_fcf(strategy):
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fy=2
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fy=2
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)
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)
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@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
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@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
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def test_sd(strategy, capfd):
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@pytest.mark.parametrize("sampler", [SDSampler.ddim, SDSampler.pndm])
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def test_sd(strategy, sampler, capfd):
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def callback(step: int):
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def callback(step: int):
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print(f"sd_step_{step}")
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print(f"sd_step_{step}")
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sd_steps = 2
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sd_steps = 50
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model = ModelManager(name="sd", device=device, hf_access_token=os.environ['HF_ACCESS_TOKEN'], callbacks=[callback])
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model = ModelManager(name="sd1.4", device=device, hf_access_token=os.environ['HF_ACCESS_TOKEN'],
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callbacks=[callback])
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cfg = get_config(strategy, prompt='a cat sitting on a bench', sd_steps=sd_steps)
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cfg = get_config(strategy, prompt='a cat sitting on a bench', sd_steps=sd_steps)
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cfg.sd_sampler = sampler
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assert_equal(
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assert_equal(
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model,
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model,
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cfg,
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cfg,
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f"sd_{strategy.capitalize()}_result.png",
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f"sd_{strategy.capitalize()}_{sampler}_result.png",
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img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
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img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
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mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
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mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
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fx=0.5,
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fy=0.5
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)
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)
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captured = capfd.readouterr()
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# captured = capfd.readouterr()
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for i in range(sd_steps):
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# for i in range(sd_steps):
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assert f'sd_step_{i}' in captured.out
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# assert f'sd_step_{i}' in captured.out
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