Merge pull request #1 from Nuullll/main
Browse files- app-img2img.py +7 -2
- app-txt2img.py +7 -2
app-img2img.py
CHANGED
@@ -12,6 +12,10 @@ from fastapi.staticfiles import StaticFiles
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from diffusers import DiffusionPipeline, AutoencoderTiny
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from compel import Compel
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import torch
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from PIL import Image
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import numpy as np
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import gradio as gr
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@@ -31,7 +35,8 @@ USE_TINY_AUTOENCODER=True
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# check if MPS is available OSX only M1/M2/M3 chips
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mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
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-
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torch_device = device
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# change to torch.float16 to save GPU memory
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@@ -72,7 +77,7 @@ pipe.unet.to(memory_format=torch.channels_last)
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if psutil.virtual_memory().total < 64 * 1024**3:
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pipe.enable_attention_slicing()
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-
if not mps_available:
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pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
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pipe(prompt="warmup", image=[Image.new("RGB", (512, 512))])
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from diffusers import DiffusionPipeline, AutoencoderTiny
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from compel import Compel
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import torch
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try:
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import intel_extension_for_pytorch as ipex
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except:
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pass
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from PIL import Image
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import numpy as np
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import gradio as gr
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# check if MPS is available OSX only M1/M2/M3 chips
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mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
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xpu_available = hasattr(torch, 'xpu') and torch.xpu.is_available()
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device = torch.device("cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu")
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torch_device = device
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# change to torch.float16 to save GPU memory
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if psutil.virtual_memory().total < 64 * 1024**3:
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pipe.enable_attention_slicing()
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if not mps_available and not xpu_available:
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pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
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pipe(prompt="warmup", image=[Image.new("RGB", (512, 512))])
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app-txt2img.py
CHANGED
@@ -12,6 +12,10 @@ from fastapi.staticfiles import StaticFiles
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from diffusers import DiffusionPipeline, AutoencoderTiny
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from compel import Compel
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import torch
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from PIL import Image
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import numpy as np
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import gradio as gr
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@@ -32,7 +36,8 @@ USE_TINY_AUTOENCODER=True
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# check if MPS is available OSX only M1/M2/M3 chips
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mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
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-
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torch_device = device
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# change to torch.float16 to save GPU memory
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torch_dtype = torch.float32
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@@ -72,7 +77,7 @@ pipe.unet.to(memory_format=torch.channels_last)
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if psutil.virtual_memory().total < 64 * 1024**3:
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pipe.enable_attention_slicing()
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-
if not mps_available:
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pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
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pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)
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from diffusers import DiffusionPipeline, AutoencoderTiny
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from compel import Compel
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import torch
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+
try:
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import intel_extension_for_pytorch as ipex
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except:
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pass
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from PIL import Image
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import numpy as np
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import gradio as gr
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# check if MPS is available OSX only M1/M2/M3 chips
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mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
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+
xpu_available = hasattr(torch, 'xpu') and torch.xpu.is_available()
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device = torch.device("cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu")
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torch_device = device
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# change to torch.float16 to save GPU memory
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torch_dtype = torch.float32
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if psutil.virtual_memory().total < 64 * 1024**3:
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pipe.enable_attention_slicing()
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if not mps_available and not xpu_available:
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pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
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pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)
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