Spaces:
Runtime error
Runtime error
Update app.py
Browse files
app.py
CHANGED
@@ -9,8 +9,6 @@ from huggingface_hub import hf_hub_download
|
|
9 |
from safetensors.torch import load_file
|
10 |
from PIL import Image
|
11 |
|
12 |
-
assert torch.cuda.is_available()
|
13 |
-
|
14 |
device = "cuda"
|
15 |
dtype = torch.float16
|
16 |
|
@@ -27,11 +25,11 @@ opts = {
|
|
27 |
step_loaded = 4
|
28 |
unet = UNet2DConditionModel.from_config(base, subfolder="unet")
|
29 |
unet.load_state_dict(load_file(hf_hub_download(repo, opts["4 Steps"][0])))
|
30 |
-
pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=dtype, variant="fp16")
|
31 |
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
|
32 |
|
33 |
# Safety checker.
|
34 |
-
safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
|
35 |
feature_extractor=CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32")
|
36 |
image_processor = VaeImageProcessor(vae_scale_factor=8)
|
37 |
|
@@ -42,6 +40,10 @@ def generate(prompt, option, progress=gr.Progress()):
|
|
42 |
print(prompt, option)
|
43 |
ckpt, step = opts[option]
|
44 |
progress((0, step))
|
|
|
|
|
|
|
|
|
45 |
if step != step_loaded:
|
46 |
print(f"Switching checkpoint from {step_loaded} to {step}")
|
47 |
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample" if step == 1 else "epsilon")
|
|
|
9 |
from safetensors.torch import load_file
|
10 |
from PIL import Image
|
11 |
|
|
|
|
|
12 |
device = "cuda"
|
13 |
dtype = torch.float16
|
14 |
|
|
|
25 |
step_loaded = 4
|
26 |
unet = UNet2DConditionModel.from_config(base, subfolder="unet")
|
27 |
unet.load_state_dict(load_file(hf_hub_download(repo, opts["4 Steps"][0])))
|
28 |
+
pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=dtype, variant="fp16")
|
29 |
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
|
30 |
|
31 |
# Safety checker.
|
32 |
+
safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
|
33 |
feature_extractor=CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32")
|
34 |
image_processor = VaeImageProcessor(vae_scale_factor=8)
|
35 |
|
|
|
40 |
print(prompt, option)
|
41 |
ckpt, step = opts[option]
|
42 |
progress((0, step))
|
43 |
+
|
44 |
+
pipe.to(device, dtype)
|
45 |
+
safety_checker.to(device, dtype)
|
46 |
+
|
47 |
if step != step_loaded:
|
48 |
print(f"Switching checkpoint from {step_loaded} to {step}")
|
49 |
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample" if step == 1 else "epsilon")
|