Hugging Face's logo

Spaces:
keras-dreambooth
/
dreambooth-pug-ace
Runtime error

App Files Community
dreambooth-pug-ace
File size: 10,932 Bytes 
5055c26
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
 
from huggingface_hub import from_pretrained_keras
from keras_cv import models
import gradio as gr

from tensorflow import keras

from diffusers import StableDiffusionPipeline

keras.mixed_precision.set_global_policy("mixed_float16")

# prepare model
resolution = 512
# sd_dreambooth_model = models.StableDiffusion(
#     img_width=resolution, img_height=resolution
# )
# db_diffusion_model = from_pretrained_keras("keras-dreambooth/dreambooth_diffusion_model")
# sd_dreambooth_model._diffusion_model = db_diffusion_model

# checkpoint of the converted Stable Diffusion from KerasCV
model_ckpt = "nielsgl/dreambooth-keras-pug-ace-sd2.1"
pipeline = StableDiffusionPipeline.from_pretrained(model_ckpt)
pipeline.to("cuda")

unique_id = "puggieace"
class_label = "dog"
prompt = f"A photo of {unique_id} {class_label} on the beach"
image = pipeline(prompt, num_inference_steps=50).images[0]

# generate images
def infer(prompt, negative_prompt, guidance_scale=10, num_inference_steps=50):
    neg = negative_prompt if negative_prompt else None
    imgs = []
    while len(imgs) != 2:
        next_prompt = pipeline(prompt, negative_prompt=neg, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, num_images_per_prompt=5)
        for img, is_neg in zip(next_prompt.images, next_prompt.nsfw_content_detected):
            if not is_neg:
                imgs.append(img)
            if len(imgs) == 2:
                break
            
    return imgs
    
output = gr.Gallery(label="Outputs").style(grid=(1,2))

# customize interface
title = "KerasCV Stable Diffusion Demo on images of Ace."
description = "This is a dreambooth model fine-tuned on images of my pug named Ace. To try it, input the concept with `puggieace dog`."
examples=[
    ["Portrait photo of puggieace dog on a beachtowel wearing sunglasses on the beach, sunset in background, golden hour", "", 12, 50],
    ["A photo of a cute puggieace dog getting a haircut in a barbershop, ultra realistic, intricate details, highly detailed, photorealistic, octane render, 8 k, unreal engine. art by artgerm and greg rutkowski and charlie bowater and magali villeneuve and alphonse mucha", "", 12, 75],
    ["Portrait of puggieace dog as a Roman Emperor, city in background, ultra realistic, intricate details, eerie, highly detailed, photorealistic, octane render, 8 k, unreal engine. art by artgerm and greg rutkowski and charlie bowater and magali villeneuve and alphonse mucha", "", 15, 75],
    ["Photo of cute puggieace dog as an astronaut, space and planet in background, ultra realistic, concept art, intricate details, highly detailed, photorealistic, octane render, 8 k, unreal engine. trending on artstation", "", 15, 75],
    ["Photo of cute puggieace dog as super hero, futuristic city in background, cinematic light, high dynamic range, insane intricate details, stunning cinema effects, aesthetic, masterpiece, trending on artstation, cartoon art", "", 12, 75],
]

base_14 = "https://huggingface.co/nielsgl/dreambooth-pug-ace-sd1.4/resolve/main/"
base_21 = "https://huggingface.co/nielsgl/dreambooth-keras-pug-ace-sd2.1/resolve/main/"

model_card_1 = f"""
# KerasCV Stable Diffusion in Diffusers 🧨🤗

DreamBooth model for the `puggieace` concept trained by nielsgl on the [nielsgl/dreambooth-ace](https://huggingface.co/datasets/nielsgl/dreambooth-ace) dataset.
It can be used by modifying the `instance_prompt`: **a photo of puggieace**.

The examples are from 2 different Keras CV models (`StableDiffusion` and `StableDiffusionV2`, corresponding to Stable Diffusion V1.4 and V2.1, respectively) trained on the same dataset (`nielsgl/dreambooth-ace`).

## Description

The Stable Diffusion V2 pipeline contained in the corresponding repository (`nielsgl/dreambooth-keras-pug-ace-sd2.1`) was created using a modified version of [this Space](https://huggingface.co/spaces/sayakpaul/convert-kerascv-sd-diffusers) for StableDiffusionV2 from KerasCV. The purpose is to convert the KerasCV Stable Diffusion weights in a way that is compatible with [Diffusers](https://github.com/huggingface/diffusers). This allows users to fine-tune using KerasCV and use the fine-tuned weights in Diffusers taking advantage of its nifty features (like [schedulers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/schedulers), [fast attention](https://huggingface.co/docs/diffusers/optimization/fp16), etc.).
This model was created as part of the Keras DreamBooth Sprint 🔥. Visit the [organisation page](https://huggingface.co/keras-dreambooth) for instructions on how to take part!

## Demo

"""

model_card_2 = f"""
## Examples

### Stable Diffusion V1.4

> Portrait of puggieace dog as a Roman Emperor, city in background

![Portrait of puggieace dog as a Roman Emperor, city in background, ultra realistic, intricate details, eerie, highly detailed, photorealistic, octane render, 8 k, unreal engine. art by artgerm and greg rutkowski and charlie bowater and magali villeneuve and alphonse mucha]({base_14}examples/emperor-1.4.jpeg)

> Photo of puggieace dog wearing sunglasses on the beach, sunset in background, golden hour

![Photo of puggieace dog wearing sunglasses on the beach, sunset in background, golden hour]({base_14}examples/beach-1.4.jpg)

> Photo of cute puggieace dog as an astronaut, planet and spaceship in background

![Photo of cute puggieace dog as an astronaut, planet and spaceship in background, ultra realistic, intricate details, highly detailed, photorealistic, octane render, 8 k, unreal engine. trending on artstation]({base_14}examples/astronaut-1.4.jpg)

### Stable Diffusion V2.1

> Portrait painting of a cute puggieace dog as a samurai

![Portrait painting of a cute puggieace dog as a samurai, ultra realistic, concept art, intricate details, eerie, highly detailed, photorealistic, octane render, 8 k, unreal engine. art by artgerm and greg rutkowski and charlie bowater and magali villeneuve and alphonse mucha]({base_21}examples/samurai-2.1.jpg)

> Photo of cute puggieace dog as an astronaut, space and planet in background

![Photo of cute puggieace dog as an astronaut, space and planet in background, ultra realistic, concept art, intricate details, highly detailed, photorealistic, octane render, 8 k, unreal engine. art by artgerm and greg rutkowski and charlie bowater, trending on artstation]({base_21}examples/astronaut-2.1.jpg)

> A photo of a cute puggieace dog getting a haircut in a barbershop

![A photo of a cute puggieace dog getting a haircut in a barbershop, ultra realistic, intricate details, highly detailed, photorealistic, octane render, 8 k, unreal engine. art by artgerm and greg rutkowski and charlie bowater and magali villeneuve and alphonse mucha]({base_21}examples/haircut-2.1.jpg)

> Portrait photo of puggieace dog in New York

![Portrait photo of puggieace dog in New York, city and skyscrapers in background, highly detailed, photorealistic, hdr, 4k]({base_21}examples/ny-2.1.jpg)

> Portrait of puggieace dog as a Roman Emperor, city in background

![Portrait of puggieace dog as a Roman Emperor, city in background, ultra realistic, intricate details, eerie, highly detailed, photorealistic, octane render, 8 k, unreal engine. art by artgerm and greg rutkowski and charlie bowater and magali villeneuve and alphonse mucha]({base_21}examples/emperor-2.1.jpg)


## Usage with Stable Diffusion V1.4

```python
from huggingface_hub import from_pretrained_keras
import keras_cv
import matplotlib.pyplot as plt


model = keras_cv.models.StableDiffusion(img_width=512, img_height=512, jit_compile=True)
model._diffusion_model = from_pretrained_keras("nielsgl/dreambooth-pug-ace")
model._text_encoder = from_pretrained_keras("nielsgl/dreambooth-pug-ace-text-encoder")

images = model.text_to_image("a photo of puggieace dog on the beach", batch_size=3)
plt.imshow(image[0])
```

## Usage with Stable Diffusion V2.1

```python
from diffusers import StableDiffusionPipeline

pipeline = StableDiffusionPipeline.from_pretrained('nielsgl/dreambooth-keras-pug-ace-sd2.1')
image = pipeline().images[0]
image
```

### Training hyperparameters

The following hyperparameters were used during training for Stable Diffusion v1.4:

| Hyperparameters | Value |
| :-- | :-- |
| name | RMSprop |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | 100 |
| jit_compile | True |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| rho | 0.9 |
| momentum | 0.0 |
| epsilon | 1e-07 |
| centered | False |
| training_precision | float32 |
"""

with gr.Blocks() as demo:
    with gr.Row():
        gr.Markdown(model_card_1)
    with gr.Row():
        with gr.Column():
            prompt_pos = gr.Textbox(label="Positive Prompt", value="a photo of puggieace dog getting a haircut")
            prompt_neg = gr.Textbox(label="Negative Prompt", value="bad anatomy, blurry")
            # gr.Slider(label='Number of gen image', minimum=1, maximum=4, value=2, step=1),
            prompt_gs = gr.Number(label='Guidance scale', value=12)
            prompt_steps = gr.Slider(label="Inference Steps",value=50)
            prompt_btn = gr.Button("Generate")
        with gr.Column():
            output = gr.Gallery(label="Outputs").style(grid=(1,2))
    prompt_btn.click(infer, inputs=[prompt_pos, prompt_neg, prompt_gs, prompt_steps], outputs=[output])
    with gr.Row():
        gr.Examples(examples, inputs=[prompt_pos, prompt_neg, prompt_gs, prompt_steps], outputs=output, fn=infer, cache_examples=True)
        # gr.Interface(infer, inputs=[gr.Textbox(label="Positive Prompt", value="a photo of puggieace dog getting a haircut"),
        #     gr.Textbox(label="Negative Prompt", value="bad anatomy, blurry"),
        #     # gr.Slider(label='Number of gen image', minimum=1, maximum=4, value=2, step=1),
        #     gr.Number(label='Guidance scale', value=12),
        #     gr.Slider(label="Inference Steps",value=50),
        #     ], outputs=[output], title=title, description=description, examples=examples).queue()
    with gr.Row():
        with gr.Column():
            gr.Markdown(model_card_2)
        with gr.Column():
            gr.Markdown(" ")

demo.queue().launch()



# with gr.Blocks() as card_interface:
#     gr.Markdown(model_card)

# demo_interface = gr.Interface(infer, inputs=[gr.Textbox(label="Positive Prompt", value="a photo of puggieace dog getting a haircut"),
#         gr.Textbox(label="Negative Prompt", value="bad anatomy, blurry"),
#         # gr.Slider(label='Number of gen image', minimum=1, maximum=4, value=2, step=1),
#         gr.Number(label='Guidance scale', value=12),
#         gr.Slider(label="Inference Steps",value=50),
#         ], outputs=[output], title=title, description=description, examples=examples).queue()


# gr.TabbedInterface([card_interface, demo_interface], ["Model Card", "Demo 🤗"]).launch()