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import torch, os
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
import gradio as gr
prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=torch.bfloat16).to("cuda")
decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", torch_dtype=torch.float16).to("cuda")
def generate_images(
prompt="a photo of a girl",
negative_prompt="bad,ugly,deformed",
height=1024,
width=1024,
guidance_scale=4.0,
seed=42,
num_images_per_prompt=1,
prior_inference_steps=20,
decoder_inference_steps=10
):
"""
Generates images based on a given prompt using Stable Diffusion models on CUDA device.
Parameters:
- prompt (str): The prompt to generate images for.
- negative_prompt (str): The negative prompt to guide image generation away from.
- height (int): The height of the generated images.
- width (int): The width of the generated images.
- guidance_scale (float): The scale of guidance for the image generation.
- prior_inference_steps (int): The number of inference steps for the prior model.
- decoder_inference_steps (int): The number of inference steps for the decoder model.
Returns:
- List[PIL.Image]: A list of generated PIL Image objects.
"""
generator = torch.Generator(device="cuda").manual_seed(int(seed))
# Generate image embeddings using the prior model
prior_output = prior(
prompt=prompt,
generator=generator,
height=height,
width=width,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=prior_inference_steps
)
# Generate images using the decoder model and the embeddings from the prior model
decoder_output = decoder(
image_embeddings=prior_output.image_embeddings.half(),
prompt=prompt,
generator=generator,
negative_prompt=negative_prompt,
guidance_scale=0.0, # Guidance scale typically set to 0 for decoder as guidance is applied in the prior
output_type="pil",
num_inference_steps=decoder_inference_steps
).images
return decoder_output
def web_demo():
with gr.Blocks():
with gr.Row():
with gr.Column():
text2image_prompt = gr.Textbox(
lines=1,
placeholder="Prompt",
show_label=False,
)
text2image_negative_prompt = gr.Textbox(
lines=1,
placeholder="Negative Prompt",
show_label=False,
)
text2image_seed = gr.Number(
value=42,
label="Seed",
)
with gr.Row():
with gr.Column():
text2image_num_images_per_prompt = gr.Slider(
minimum=1,
maximum=2,
step=1,
value=1,
label="Number Image",
)
text2image_height = gr.Slider(
minimum=128,
maximum=1024,
step=32,
value=1024,
label="Image Height",
)
text2image_width = gr.Slider(
minimum=128,
maximum=1024,
step=32,
value=1024,
label="Image Width",
)
with gr.Row():
with gr.Column():
text2image_guidance_scale = gr.Slider(
minimum=0.1,
maximum=15,
step=0.1,
value=4.0,
label="Guidance Scale",
)
text2image_prior_inference_step = gr.Slider(
minimum=1,
maximum=50,
step=1,
value=20,
label="Prior Inference Step",
)
text2image_decoder_inference_step = gr.Slider(
minimum=1,
maximum=50,
step=1,
value=10,
label="Decoder Inference Step",
)
text2image_predict = gr.Button(value="Generate Image")
with gr.Column():
output_image = gr.Gallery(
label="Generated images",
show_label=False,
elem_id="gallery",
).style(grid=(1, 2), height=300)
text2image_predict.click(
fn=generate_images,
inputs=[
text2image_prompt,
text2image_negative_prompt,
text2image_height,
text2image_width,
text2image_guidance_scale,
text2image_seed,
text2image_num_images_per_prompt,
text2image_prior_inference_step,
text2image_decoder_inference_step
],
outputs=output_image,
) |