File size: 8,569 Bytes
4094792
8226b60
 
 
 
 
 
 
 
 
 
 
edb8b5e
8226b60
 
 
 
edb8b5e
 
8226b60
 
 
 
edb8b5e
 
8226b60
 
 
 
edb8b5e
 
8226b60
 
 
 
edb8b5e
8226b60
 
 
 
 
 
 
07d0333
8226b60
 
 
 
 
 
 
 
 
07d0333
8226b60
 
 
 
 
 
 
 
 
 
 
 
 
7a30042
8226b60
 
 
 
 
d7a9923
8226b60
 
 
 
 
 
 
 
19de901
 
c995383
 
 
713ad06
c995383
19de901
713ad06
 
 
8226b60
 
 
1e15c22
72572e5
8226b60
ef753b3
ab25924
ef753b3
 
 
ab25924
ef753b3
 
 
ab25924
ef753b3
 
 
ab25924
ef753b3
 
 
ab25924
 
 
 
 
 
 
 
68c53ae
edba0de
68c53ae
 
 
 
 
c53db80
68c53ae
 
 
 
 
c53db80
68c53ae
 
c53db80
 
 
 
1ee93ec
2ca84f7
8226b60
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
import spaces
import gradio as gr
from diffusers import AutoPipelineForText2Image
import numpy as np
import math
import torch 
import random

theme = gr.themes.Base(
    font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
)

device="cuda"
pipe_xlc = AutoPipelineForText2Image.from_pretrained(
    "temp-org-cc/CommonCanvas-XLC",
    custom_pipeline="multimodalart/sdxl_perturbed_attention_guidance",
    torch_dtype=torch.float16
).to(device)

pipe_xlnc = AutoPipelineForText2Image.from_pretrained(
    "temp-org-cc/CommonCanvas-XLNC",
    custom_pipeline="multimodalart/sdxl_perturbed_attention_guidance",
    torch_dtype=torch.float16
).to(device)

pipe_sc = AutoPipelineForText2Image.from_pretrained(
    "temp-org-cc/CommonCanvas-SC",
    custom_pipeline="hyoungwoncho/sd_perturbed_attention_guidance",
    torch_dtype=torch.float16
).to(device)

pipe_snc = AutoPipelineForText2Image.from_pretrained(
    "temp-org-cc/CommonCanvas-SNC",
    custom_pipeline="hyoungwoncho/sd_perturbed_attention_guidance",
    torch_dtype=torch.float16
).to(device)

@spaces.GPU
def run_xlc(prompt, negative_prompt=None, guidance_scale=7.0, pag_scale=3.0, pag_layers=["mid"], randomize_seed=True, seed=42, progress=gr.Progress(track_tqdm=True)):
    if(randomize_seed):
        seed = random.randint(0, 9007199254740991)
    
    generator = torch.Generator(device="cuda").manual_seed(seed)
    image = pipe_xlc(prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, pag_scale=pag_scale, pag_applied_layers=pag_layers, generator=generator, num_inference_steps=25).images[0]    
    
    return image, seed

@spaces.GPU
def run_xlnc(prompt, negative_prompt=None, guidance_scale=7.0, pag_scale=3.0, pag_layers=["mid"], randomize_seed=True, seed=42, progress=gr.Progress(track_tqdm=True)):
    if(randomize_seed):
        seed = random.randint(0, 9007199254740991)
    
    generator = torch.Generator(device="cuda").manual_seed(seed)
    image = pipe_xlnc(prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, pag_scale=pag_scale, pag_applied_layers=pag_layers, generator=generator, num_inference_steps=25).images[0]    
    
    return image, seed

@spaces.GPU
def run_sc(prompt, negative_prompt=None, guidance_scale=7.0, pag_scale=3.0, pag_layers=["mid"], randomize_seed=True, seed=42, progress=gr.Progress(track_tqdm=True)):
    if(randomize_seed):
        seed = random.randint(0, 9007199254740991)
    
    generator = torch.Generator(device="cuda").manual_seed(seed)
    image = pipe_sc(prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, pag_scale=pag_scale, pag_applied_layers=pag_layers, generator=generator, num_inference_steps=25).images[0]    
    
    return image, seed

@spaces.GPU
def run_snc(prompt, negative_prompt=None, guidance_scale=7.0, pag_scale=3.0, pag_layers=["mid"], randomize_seed=True, seed=42, progress=gr.Progress(track_tqdm=True)):
    if(randomize_seed):
        seed = random.randint(0, 9007199254740991)
    
    generator = torch.Generator(device="cuda").manual_seed(seed)
    image = pipe_snc(prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, pag_scale=pag_scale, pag_applied_layers=pag_layers, generator=generator, num_inference_steps=25).images[0]    
    
    return image, seed

css = '''
.gradio-container{
max-width: 768px !important;
margin: 0 auto;
}
.tabitem{
padding: 0 !important;
border-radius: 0;
}
.gr-group {
    background: transparent !important;
    border: 0 !important;
}
.styler{
    background: transparent !important
}
'''

with gr.Blocks(css=css, theme=theme) as demo:
    gr.Markdown('''# CommonCanvas Demo
[CommonCanvas suite of models](https://huggingface.co/collections/common-canvas/commoncanvas-66226ef9688b3580a5954653) trained on [CommonCatalogue](https://huggingface.co/collections/common-canvas/commoncatalog-6530907589ffafffe87c31c5), ~70M Creative Commons images.
    ''')
    #with gr.Group():
    with gr.Tab("XL-C model"):
      with gr.Row():
        prompt_xlc = gr.Textbox(show_label=False, scale=4, placeholder="Your prompt for XL-C")
        button_xlc = gr.Button("Generate", min_width=120)
    with gr.Tab("XL-NC model"):
      with gr.Row():
        prompt_xlnc = gr.Textbox(show_label=False, scale=4, placeholder="Your prompt for XL-NC")
        button_xlnc = gr.Button("Generate", min_width=120)
    with gr.Tab("S-C model"):
      with gr.Row():            
        prompt_sc = gr.Textbox(show_label=False, scale=4, placeholder="Your prompt for S-C")
        button_sc = gr.Button("Generate", min_width=120)
    with gr.Tab("S-NC model"):
      with gr.Row():
        prompt_snc = gr.Textbox(show_label=False, scale=4, placeholder="Your prompt for S-NC")
        button_snc = gr.Button("Generate", min_width=120)  
    output = gr.Image(label="Your result", interactive=False)
    with gr.Accordion("Advanced Settings", open=False):
        guidance_scale = gr.Number(label="CFG Guidance Scale", info="The guidance scale for CFG, ignored if no prompt is entered (unconditional generation)", value=7.0)
        negative_prompt = gr.Textbox(label="Negative prompt", info="Is only applied for the CFG part, leave blank for unconditional generation")
        pag_scale = gr.Number(label="Pag Scale", value=3.0)
        pag_layers = gr.Dropdown(label="Model layers to apply Pag to", info="mid is the one used on the paper, up and down blocks seem unstable", choices=["up", "mid", "down"], multiselect=True, value="mid")
        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
        seed = gr.Slider(minimum=1, maximum=9007199254740991, step=1, randomize=True)
    
    with gr.Accordion("Use it with 🧨 diffusers, ComfyUI, AUTOMATIC111, Forge, SD.Next, Invoke, etc.", open=False):
        gr.Markdown('''The CommonCanvas S and CommonCanvas XL collections are drop-in replacements of Stable Diffusion 2 and Stable Diffusion XL respectively and can be used as such with `diffusers` or in UIs such as ComfyUI, AUTOMATIC1111, SDNext, InvokeAI, etc.
## Using it with diffusers
```py
from diffusers import AutoPipelineForText2Image
pipe = AutoPipelineForText2Image.from_pretrained(
    "common-canvas/CommonCanvasXL-C", #here you can pick between all models
    custom_pipeline="multimodalart/sdxl_perturbed_attention_guidance",
    torch_dtype=torch.float16
).to(device)

prompt = "a cat"
image = pipe(prompt, num_inference_steps=25).images[0]    
```
## Using it ComfyUI/Automatic1111
- [CommonCanvasS-C.safetensors](https://huggingface.co/common-canvas/CommonCanvas-S-C/resolve/main/commoncanvas_s_c.safetensors?download=true) (SD2 drop-in, commercial)
- [CommonCanvasS-NC.safetensors](https://huggingface.co/common-canvas/CommonCanvas-S-NC/resolve/main/commoncanvas_s_nc.safetensors?download=true) (SD2 drop-in, non-commercial - trained on more data)
- [CommonCanvasXL-C.safetensors](https://huggingface.co/common-canvas/CommonCanvas-XL-NC/blob/main/commoncanvas_xl_nc.safetensors) (SDXL drop-in, commercial)
- [CommonCanvasXL-NC.safetensors](https://huggingface.co/common-canvas/CommonCanvas-XL-NC/resolve/main/commoncanvas_xl_nc.safetensors?download=true) (SDXL drop-in, non-commercial - trained on more data)
''')
    #gr.Examples(fn=run, examples=[" ", "an insect robot preparing a delicious meal, anime style", "a photo of a group of friends at an amusement park"], inputs=prompt, outputs=[output, seed], cache_examples=True)
    gr.on(
        triggers=[
            button_xlc.click,
            prompt_xlc.submit
        ],
        fn=run_xlc,
        inputs=[prompt_xlc, negative_prompt, guidance_scale, pag_scale, pag_layers, randomize_seed, seed],
        outputs=[output, seed],
    )
    gr.on(
        triggers=[
            button_xlnc.click,
            prompt_xlnc.submit
        ],
        fn=run_xlnc,
        inputs=[prompt_xlnc, negative_prompt, guidance_scale, pag_scale, pag_layers, randomize_seed, seed],
        outputs=[output, seed],
    )
    gr.on(
        triggers=[
            button_sc.click,
            prompt_sc.submit
        ],
        fn=run_sc,
        inputs=[prompt_sc, negative_prompt, guidance_scale, pag_scale, pag_layers, randomize_seed, seed],
        outputs=[output, seed],
    )
    gr.on(
        triggers=[
            button_snc.click,
            prompt_snc.submit
        ],
        fn=run_sc,
        inputs=[prompt_snc, negative_prompt, guidance_scale, pag_scale, pag_layers, randomize_seed, seed],
        outputs=[output, seed],
    )
if __name__ == "__main__":
    demo.launch(share=True)