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import torch
import os
import gradio as gr
from torch import autocast
from diffusers import StableDiffusionPipeline, DDIMScheduler
from IPython.display import display
from text_generation import Client, InferenceAPIClient
model_path = "ashishtanwer/Radiance/shoe.ckpt"
pipe = StableDiffusionPipeline.from_pretrained(model_path, safety_checker=None, torch_dtype=torch.float16).to("cuda")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
g_cuda = None
#@markdown Can set random seed here for reproducibility.
g_cuda = torch.Generator(device='cuda')
seed = 52362 #@param {type:"number"}
g_cuda.manual_seed(seed)
#@title Run for generating images.
prompt = "photo of zwx dog in a bucket" #@param {type:"string"}
negative_prompt = "" #@param {type:"string"}
num_samples = 4 #@param {type:"number"}
guidance_scale = 7.5 #@param {type:"number"}
num_inference_steps = 24 #@param {type:"number"}
height = 512 #@param {type:"number"}
width = 512 #@param {type:"number"}
with autocast("cuda"), torch.inference_mode():
images = pipe(
prompt,
height=height,
width=width,
negative_prompt=negative_prompt,
num_images_per_prompt=num_samples,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
generator=g_cuda
).images
for img in images:
display(img)
def inference(prompt, negative_prompt, num_samples, height=512, width=512, num_inference_steps=50, guidance_scale=7.5):
with torch.autocast("cuda"), torch.inference_mode():
return pipe(
prompt, height=int(height), width=int(width),
negative_prompt=negative_prompt,
num_images_per_prompt=int(num_samples),
num_inference_steps=int(num_inference_steps), guidance_scale=guidance_scale,
generator=g_cuda
).images
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt", value="photo of zwx dog in a bucket")
negative_prompt = gr.Textbox(label="Negative Prompt", value="")
run = gr.Button(value="Generate")
with gr.Row():
num_samples = gr.Number(label="Number of Samples", value=4)
guidance_scale = gr.Number(label="Guidance Scale", value=7.5)
with gr.Row():
height = gr.Number(label="Height", value=512)
width = gr.Number(label="Width", value=512)
num_inference_steps = gr.Slider(label="Steps", value=24)
with gr.Column():
gallery = gr.Gallery()
run.click(inference, inputs=[prompt, negative_prompt, num_samples, height, width, num_inference_steps, guidance_scale], outputs=gallery)
demo.launch(debug=True) |