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import gradio as gr
import numpy as np
import random
import spaces
from diffusers import DiffusionPipeline
from transformers import T5EncoderModel, CLIPTextModelWithProjection
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
text_encoder_repo = "silveroxides/CLIP_L_Fur"
text_encoder_2_repo = "silveroxides/SeaArtFurryCLIP_G"
text_encoder_3_repo = "silveroxides/t5xxl_flan_enc"
model_repo_id = "stabilityai/stable-diffusion-3.5-large-turbo"
if torch.cuda.is_available():
torch_dtype = torch.bfloat16
else:
torch_dtype = torch.float32
text_encoder = CLIPTextModelWithProjection.from_pretrained(text_encoder_repo, torch_dtype=torch_dtype)
text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(text_encoder_2_repo, torch_dtype=torch_dtype)
text_encoder_3 = T5EncoderModel.from_pretrained(text_encoder_3_repo, torch_dtype=torch_dtype)
pipe = DiffusionPipeline.from_pretrained(model_repo_id, text_encoder=text_encoder, text_encoder_2=text_encoder_2, text_encoder_3=text_encoder_3, torch_dtype=torch_dtype)
pipe = pipe.to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1216
@spaces.GPU
def infer(
prompt,
negative_prompt="",
seed=42,
randomize_seed=False,
width=1024,
height=1024,
guidance_scale=0.0,
num_inference_steps=4,
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
).images[0]
return image, seed
examples = [
"A capybara wearing a suit holding a sign that reads Hello World",
]
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(" # [Stable Diffusion 3.5 Large Turbo (8B)](https://huggingface.co/stabilityai/stable-diffusion-3.5-large-turbo)")
gr.Markdown("Space for testing alternative text encoders with SD 3.5 L Turbo")
with gr.Row():
prompt = gr.Textbox(
label="Prompt",
show_label=False,
max_lines=4,
lines=4,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0, variant="primary")
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Textbox(
label="Negative prompt",
max_lines=2,
lines=2,
placeholder="Enter a negative prompt",
visible=True,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=512,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=512,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=7.5,
step=0.1,
value=0.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=4,
)
gr.Examples(examples=examples, inputs=[prompt], outputs=[result, seed], fn=infer, cache_examples=True, cache_mode="lazy")
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch()
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