Changed PIL Image importing method to fix error
Browse files- pipeline.py +5 -5
pipeline.py
CHANGED
@@ -1,7 +1,7 @@
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from typing import Union, Callable, Optional
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import torch
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-
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from diffusers import (
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AutoencoderKL,
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DDIMScheduler,
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@@ -10,7 +10,7 @@ from diffusers import (
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PNDMScheduler,
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UNet2DConditionModel,
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)
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from PIL import Image
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from torchvision import transforms as tfms
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from tqdm.auto import tqdm
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from transformers import CLIPTextModel, CLIPTokenizer
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@@ -44,7 +44,7 @@ class MagicMixPipeline(DiffusionPipeline):
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img = (img / 2 + 0.5).clamp(0, 1)
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img = img.detach().cpu().permute(0, 2, 3, 1).numpy()
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img = (img * 255).round().astype("uint8")
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-
return Image.fromarray(img[0])
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# convert prompt into text embeddings, also unconditional embeddings
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def prep_text(self, prompt):
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@@ -72,7 +72,7 @@ class MagicMixPipeline(DiffusionPipeline):
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def __call__(
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self,
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img: Image.Image,
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prompt: str,
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kmin: float = 0.3,
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kmax: float = 0.6,
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@@ -82,7 +82,7 @@ class MagicMixPipeline(DiffusionPipeline):
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guidance_scale: float = 7.5,
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callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
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callback_steps: Optional[int] = 1,
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) -> Image.Image:
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tmin = steps - int(kmin * steps)
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tmax = steps - int(kmax * steps)
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from typing import Union, Callable, Optional
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import torch
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+
import PIL
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from diffusers import (
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AutoencoderKL,
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DDIMScheduler,
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PNDMScheduler,
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UNet2DConditionModel,
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)
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+
#from PIL import Image
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from torchvision import transforms as tfms
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from tqdm.auto import tqdm
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from transformers import CLIPTextModel, CLIPTokenizer
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img = (img / 2 + 0.5).clamp(0, 1)
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img = img.detach().cpu().permute(0, 2, 3, 1).numpy()
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img = (img * 255).round().astype("uint8")
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+
return PIL.Image.fromarray(img[0])
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# convert prompt into text embeddings, also unconditional embeddings
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def prep_text(self, prompt):
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def __call__(
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self,
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+
img: PIL.Image.Image,
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prompt: str,
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kmin: float = 0.3,
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kmax: float = 0.6,
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guidance_scale: float = 7.5,
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callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
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callback_steps: Optional[int] = 1,
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) -> PIL.Image.Image:
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tmin = steps - int(kmin * steps)
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tmax = steps - int(kmax * steps)
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