Spaces:
Running
on
Zero
Running
on
Zero
Update raw.py
Browse files
raw.py
CHANGED
@@ -2,19 +2,22 @@ import torch
|
|
2 |
import spaces
|
3 |
import os
|
4 |
from diffusers.utils import load_image
|
5 |
-
from diffusers import FluxControlNetModel, FluxControlNetPipeline
|
6 |
import gradio as gr
|
7 |
huggingface_token = os.getenv("HUGGINFACE_TOKEN")
|
8 |
|
|
|
|
|
9 |
# Load pipeline
|
10 |
controlnet = FluxControlNetModel.from_pretrained(
|
11 |
"jasperai/Flux.1-dev-Controlnet-Upscaler",
|
12 |
torch_dtype=torch.bfloat16
|
13 |
)
|
14 |
pipe = FluxControlNetPipeline.from_pretrained(
|
15 |
-
"
|
16 |
controlnet=controlnet,
|
17 |
torch_dtype=torch.bfloat16,
|
|
|
18 |
token=huggingface_token
|
19 |
)
|
20 |
pipe.to("cuda")
|
@@ -25,7 +28,7 @@ def generate_image(prompt, scale, steps, control_image):
|
|
25 |
control_image = load_image(control_image)
|
26 |
w, h = control_image.size
|
27 |
# Upscale x1
|
28 |
-
control_image = control_image.resize((w * scale, h * scale))
|
29 |
image = pipe(
|
30 |
prompt=prompt,
|
31 |
control_image=control_image,
|
|
|
2 |
import spaces
|
3 |
import os
|
4 |
from diffusers.utils import load_image
|
5 |
+
from diffusers import FluxControlNetModel, FluxControlNetPipeline, AutoencoderKL
|
6 |
import gradio as gr
|
7 |
huggingface_token = os.getenv("HUGGINFACE_TOKEN")
|
8 |
|
9 |
+
good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16, token=huggingface_token).to(device)
|
10 |
+
|
11 |
# Load pipeline
|
12 |
controlnet = FluxControlNetModel.from_pretrained(
|
13 |
"jasperai/Flux.1-dev-Controlnet-Upscaler",
|
14 |
torch_dtype=torch.bfloat16
|
15 |
)
|
16 |
pipe = FluxControlNetPipeline.from_pretrained(
|
17 |
+
"LPX55/FLUX.1-merged_uncensored",
|
18 |
controlnet=controlnet,
|
19 |
torch_dtype=torch.bfloat16,
|
20 |
+
vae=good_vae,
|
21 |
token=huggingface_token
|
22 |
)
|
23 |
pipe.to("cuda")
|
|
|
28 |
control_image = load_image(control_image)
|
29 |
w, h = control_image.size
|
30 |
# Upscale x1
|
31 |
+
control_image = control_image.resize((int(w * scale), int(h * scale)))
|
32 |
image = pipe(
|
33 |
prompt=prompt,
|
34 |
control_image=control_image,
|