Spaces:
Runtime error
Runtime error
File size: 4,946 Bytes
458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 458776d 5614c61 |
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 |
import gradio as gr
import numpy as np
import random
import torch
from diffusers import StableDiffusion3Pipeline, SD3Transformer2DModel, FlowMatchEulerDiscreteScheduler
import spaces
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16
repo = "stabilityai/stable-diffusion-3-medium-diffusers"
pipe = StableDiffusion3Pipeline.from_pretrained(repo, torch_dtype=torch.float16).to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1344
def infer(prompts, negative_prompts, seeds, randomize_seeds, widths, heights, guidance_scales, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
images = []
for i, prompt in enumerate(prompts):
if randomize_seeds[i]:
seeds[i] = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seeds[i])
image = pipe(
prompt=prompt,
negative_prompt=negative_prompts[i],
guidance_scale=guidance_scales[i],
num_inference_steps=num_inference_steps[i],
width=widths[i],
height=heights[i],
generator=generator
).images[0]
images.append(image)
return images, seeds
examples = [
["Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", "A blurry astronaut", 0, True, 512, 512, 7.5, 28],
["An astronaut riding a green horse", "Astronaut on a regular horse", 0, True, 512, 512, 7.5, 28],
["A delicious ceviche cheesecake slice", "A cheesecake that looks boring", 0, True, 512, 512, 7.5, 28],
]
css="""
#col-container {
margin: 0 auto;
max-width: 580px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# Demo [Automated Stable Diffusion 3 Medium](https://huggingface.co/stabilityai/stable-diffusion-3-medium)
""")
with gr.Row():
prompt_group = gr.Group(elem_id="prompt_group")
with prompt_group:
prompt_input = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
negative_prompt_input = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
)
seed_input = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed_input = gr.Checkbox(label="Randomize seed", value=True)
width_input = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=64,
value=512,
)
height_input = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=64,
value=512,
)
guidance_scale_input = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=7.5,
)
num_inference_steps_input = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=28,
)
run_button = gr.Button("Run", scale=0)
result = gr.Gallery(label="Results", show_label=False, columns=4, rows=1)
add_button = gr.Button("Add Prompt")
with gr.Accordion("Advanced Settings", open=False):
pass
gr.Examples(
examples = examples,
inputs = [
prompt_input,
negative_prompt_input,
seed_input,
randomize_seed_input,
width_input,
height_input,
guidance_scale_input,
num_inference_steps_input
]
)
def add_prompt():
prompt_group.duplicate()
def clear_prompts():
prompt_group.clear()
add_button.click(add_prompt)
gr.on(
triggers=[run_button.click, prompt_input.submit, negative_prompt_input.submit],
fn=infer,
inputs=[
prompt_input,
negative_prompt_input,
seed_input,
randomize_seed_input,
width_input,
height_input,
guidance_scale_input,
num_inference_steps_input
],
outputs=[result, seed_input],
api_name="infer"
)
demo.launch() |