File size: 4,706 Bytes
258d8c9 0ae97cf 258d8c9 |
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 |
from PIL import Image
import gradio as gr
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
import torch
torch.backends.cuda.matmul.allow_tf32 = True
controlnet = ControlNetModel.from_pretrained("JFoz/dog-cat-pose", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
controlnet=controlnet,
torch_dtype=torch.float16,
safety_checker=None,
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()
pipe.enable_attention_slicing()
def infer(
prompt,
negative_prompt,
conditioning_image,
num_inference_steps=30,
size=768,
guidance_scale=7.0,
seed=1234,
):
conditioning_image_raw = Image.fromarray(conditioning_image)
#conditioning_image = conditioning_image_raw.convert('L')
g_cpu = torch.Generator()
if seed == -1:
generator = g_cpu.manual_seed(g_cpu.seed())
else:
generator = g_cpu.manual_seed(seed)
output_image = pipe(
prompt,
conditioning_image,
height=size,
width=size,
num_inference_steps=num_inference_steps,
generator=generator,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
controlnet_conditioning_scale=1.0,
).images[0]
#del conditioning_image, conditioning_image_raw
#gc.collect()
return output_image
with gr.Blocks(theme=gr.themes.Default(font=[gr.themes.GoogleFont("Inconsolata"), "Arial", "sans-serif"])) as demo:
gr.Markdown(
"""
# Animal Pose Control Net
# This is a demo of Animal Pose Control Net, which is a model trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
""")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(
label="Prompt",
)
negative_prompt = gr.Textbox(
label="Negative Prompt",
)
conditioning_image = gr.Image(
label="Conditioning Image",
)
with gr.Accordion('Advanced options', open=False):
with gr.Row():
num_inference_steps = gr.Slider(
10, 40, 20,
step=1,
label="Steps",
)
size = gr.Slider(
256, 768, 512,
step=128,
label="Size",
)
with gr.Row():
guidance_scale = gr.Slider(
label='Guidance Scale',
minimum=0.1,
maximum=30.0,
value=7.0,
step=0.1
)
seed = gr.Slider(
label='Seed',
value=-1,
minimum=-1,
maximum=2147483647,
step=1,
# randomize=True
)
submit_btn = gr.Button(
value="Submit",
variant="primary"
)
with gr.Column(min_width=300):
output = gr.Image(
label="Result",
)
submit_btn.click(
fn=infer,
inputs=[
prompt, negative_prompt, conditioning_image, num_inference_steps, size, guidance_scale, seed
#prompt, size, seed
],
outputs=output
)
gr.Examples(
examples=[
#["a tortoiseshell cat is sitting on a cushion"],
#["a yellow dog standing on a lawn"],
["a tortoiseshell cat is sitting on a cushion", "https://huggingface.co/JFoz/dog-cat-pose/blob/main/images_0.png"],
["a yellow dog standing on a lawn", "https://huggingface.co/JFoz/dog-cat-pose/blob/main/images_1.png"],
],
inputs=[
#prompt, negative_prompt, conditioning_image
prompt
],
outputs=output,
fn=infer,
cache_examples=True,
)
gr.Markdown(
"""
* [Dataset](https://huggingface.co/datasets/JFoz/dog-poses-controlnet-dataset)
* [Diffusers model](), [Web UI model](https://huggingface.co/JFoz/dog-pose)
* [Training Report](https://wandb.ai/john-fozard/dog-cat-pose/runs/kmwcvae5))
""")
#gr.Interface(infer, inputs=["text"], outputs=[output], title=title, description=description, examples=examples).queue().launch()
demo.launch() |