Update app.py
Browse files
app.py
CHANGED
@@ -4,7 +4,7 @@ import torch
|
|
4 |
import numpy as np
|
5 |
from PIL import Image
|
6 |
from diffusers import DiffusionPipeline
|
7 |
-
|
8 |
|
9 |
device = "cuda" if torch.cuda.is_available() else "cpu"
|
10 |
pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo")
|
@@ -15,16 +15,16 @@ def resize(value,img):
|
|
15 |
img = img.resize((value,value))
|
16 |
return img
|
17 |
|
18 |
-
def infer(source_img, prompt,
|
19 |
generator = torch.Generator(device).manual_seed(seed)
|
20 |
source_image = resize(512, source_img)
|
21 |
source_image.save('source.png')
|
22 |
-
image = pipe(prompt,
|
23 |
return image
|
24 |
|
25 |
-
gr.Interface(fn=infer, inputs=[gr.Image(source="upload", type="filepath", label="Raw Image. Must Be .png"), gr.Textbox(label = 'Prompt Input Text. 77 Token (Keyword or Symbol) Maximum'),
|
26 |
-
gr.Slider(2, 15, value = 7, label = 'Guidance Scale'),
|
27 |
-
gr.Slider(1,
|
28 |
gr.Slider(label = "Seed", minimum = 0, maximum = 987654321987654321, step = 1, randomize = True),
|
29 |
gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .5)],
|
30 |
outputs='image', title = "Stable Diffusion XL 1.0 Image to Image Pipeline CPU", description = "For more information on Stable Diffusion XL 1.0 see https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0 <br><br>Upload an Image (<b>MUST Be .PNG and 512x512 or 768x768</b>) enter a Prompt, or let it just do its Thing, then click submit. 10 Iterations takes about ~900-1200 seconds currently. For more informationon about Stable Diffusion or Suggestions for prompts, keywords, artists or styles see https://github.com/Maks-s/sd-akashic", article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").queue(max_size=5).launch()
|
|
|
4 |
import numpy as np
|
5 |
from PIL import Image
|
6 |
from diffusers import DiffusionPipeline
|
7 |
+
|
8 |
|
9 |
device = "cuda" if torch.cuda.is_available() else "cpu"
|
10 |
pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo")
|
|
|
15 |
img = img.resize((value,value))
|
16 |
return img
|
17 |
|
18 |
+
def infer(source_img, prompt, steps, seed, Strength):
|
19 |
generator = torch.Generator(device).manual_seed(seed)
|
20 |
source_image = resize(512, source_img)
|
21 |
source_image.save('source.png')
|
22 |
+
image = pipe(prompt, image=source_image, strength=Strength, guidance_scale=0.0, num_inference_steps=steps).images[0]
|
23 |
return image
|
24 |
|
25 |
+
gr.Interface(fn=infer, inputs=[gr.Image(source="upload", type="filepath", label="Raw Image. Must Be .png"), gr.Textbox(label = 'Prompt Input Text. 77 Token (Keyword or Symbol) Maximum'),
|
26 |
+
#gr.Slider(2, 15, value = 7, label = 'Guidance Scale'),
|
27 |
+
gr.Slider(1, 5, value = 2, step = 1, label = 'Number of Iterations'),
|
28 |
gr.Slider(label = "Seed", minimum = 0, maximum = 987654321987654321, step = 1, randomize = True),
|
29 |
gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .5)],
|
30 |
outputs='image', title = "Stable Diffusion XL 1.0 Image to Image Pipeline CPU", description = "For more information on Stable Diffusion XL 1.0 see https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0 <br><br>Upload an Image (<b>MUST Be .PNG and 512x512 or 768x768</b>) enter a Prompt, or let it just do its Thing, then click submit. 10 Iterations takes about ~900-1200 seconds currently. For more informationon about Stable Diffusion or Suggestions for prompts, keywords, artists or styles see https://github.com/Maks-s/sd-akashic", article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").queue(max_size=5).launch()
|