Spaces:
Runtime error
Runtime error
File size: 3,697 Bytes
8d691c3 b79e3f7 8d691c3 a15d459 166d9fd e221206 a15d459 8d691c3 21e5fc0 8d691c3 b79e3f7 8d691c3 b79e3f7 8d691c3 166d9fd 21e5fc0 8d691c3 b79e3f7 8d691c3 821924e 8d691c3 821924e 8d691c3 b79e3f7 36abcb2 b79e3f7 821924e 8d691c3 821924e 8d691c3 21e5fc0 166d9fd e221206 166d9fd 21e5fc0 8d691c3 8749689 21e5fc0 8d691c3 36abcb2 166d9fd 21e5fc0 8749689 36abcb2 8d691c3 36abcb2 8d691c3 36abcb2 8d691c3 36abcb2 890a980 21e5fc0 821924e 21e5fc0 36abcb2 3a091ee 36abcb2 8d691c3 |
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 |
import gradio as gr
import numpy as np
import random
import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch
import subprocess
from groq import Groq
import base64
import os
subprocess.run("rm -rf /data-nvme/zerogpu-offload/*", env={}, shell=True)
# Load FLUX image generator
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "black-forest-labs/FLUX.1-schnell" # Replace to the model you would like to use
lora_path = "matteomarjanovic/flatsketcher"
weigths_file = "lora.safetensors"
if torch.cuda.is_available():
torch_dtype = torch.float16
else:
torch_dtype = torch.float32
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)
pipe.load_lora_weights(lora_path, weight_name=weigths_file)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
@spaces.GPU #[uncomment to use ZeroGPU]
def infer(
prompt,
progress=gr.Progress(track_tqdm=True),
):
# seed = random.randint(0, MAX_SEED)
# generator = torch.Generator().manual_seed(seed)
image = pipe(
prompt=prompt,
guidance_scale=0.,
num_inference_steps=4,
width=1420,
height=1080,
max_sequence_length=256,
).images[0]
return image
@spaces.GPU #[uncomment to use ZeroGPU]
def generate_description_fn(
image,
progress=gr.Progress(track_tqdm=True),
):
base64_image = encode_image(image)
client = Groq(
api_key=os.environ.get("GROQ_API_KEY"),
)
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "What's in this image?"},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}",
},
},
],
}
],
model="llama-3.2-11b-vision-preview",
)
return chat_completion.choices[0].message.content
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
# generated_prompt = ""
with gr.Blocks(css=css) as demo:
with gr.Row():
with gr.Column(elem_id="col-input-image"):
gr.Markdown(" # Drop your image here")
input_image = gr.Image(type="filepath")
generate_button = gr.Button("Generate", scale=0, variant="primary")
generated_prompt = gr.Markdown("")
with gr.Column(elem_id="col-container"):
gr.Markdown(" # Text-to-Image Gradio Template")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0, variant="primary")
result = gr.Image(label="Result", show_label=False)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
prompt,
],
outputs=[result],
)
gr.on(
triggers=[generate_button.click],
fn=generate_description_fn,
inputs=[
input_image,
],
outputs=[generated_prompt],
)
if __name__ == "__main__":
demo.launch()
|