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| import gradio as gr | |
| import numpy as np | |
| from optimum.intel import OVStableDiffusionPipeline, OVStableDiffusionXLPipeline, OVLatentConsistencyModelPipeline | |
| from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker | |
| from diffusers import DiffusionPipeline | |
| #model_id = "echarlaix/sdxl-turbo-openvino-int8" | |
| #model_id = "echarlaix/LCM_Dreamshaper_v7-openvino" | |
| model_id = "OpenVINO/LCM_Dreamshaper_v7-int8-ov" | |
| #safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker") | |
| #pipeline = OVLatentConsistencyModelPipeline.from_pretrained(model_id, compile=False, safety_checker=safety_checker) | |
| pipeline = OVLatentConsistencyModelPipeline.from_pretrained(model_id, compile=False) | |
| batch_size, num_images, height, width = 1, 1, 1024, 512 | |
| pipeline.reshape(batch_size=batch_size, height=height, width=width, num_images_per_prompt=num_images) | |
| #pipeline.load_textual_inversion("./badhandv4.pt", "badhandv4") | |
| #hiten1 | |
| pipeline.load_textual_inversion("./hiten1.pt", "hiten1") | |
| pipeline.compile() | |
| #TypeError: LatentConsistencyPipelineMixin.__call__() got an unexpected keyword argument 'negative_prompt' | |
| negative_prompt="easynegative,bad anatomy, bad hands, missing fingers, extra fingers, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn, cartoon, cg, 3d, unreal, animate, amputation, disconnected limbs, nsfw, nude, censored, " | |
| def infer(prompt, num_inference_steps): | |
| image = pipeline( | |
| prompt = prompt, | |
| #negative_prompt = negative_prompt, | |
| guidance_scale = 7.0, | |
| num_inference_steps = num_inference_steps, | |
| width = width, | |
| height = height, | |
| num_images_per_prompt=num_images, | |
| ).images[0] | |
| return image | |
| examples = [ | |
| "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", | |
| "An astronaut riding a green horse", | |
| "A delicious ceviche cheesecake slice", | |
| ] | |
| css=""" | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 520px; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown(f""" | |
| # Demo : [Fast LCM](https://huggingface.co/OpenVINO/LCM_Dreamshaper_v7-int8-ov) quantized with NNCF ⚡ | |
| """) | |
| 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) | |
| result = gr.Image(label="Result", show_label=False) | |
| with gr.Accordion("Advanced Settings", open=False): | |
| #with gr.Row(): | |
| # negative_prompt = gr.Text( | |
| # label="Negative prompt", | |
| # max_lines=1, | |
| # placeholder="Enter a negative prompt", | |
| # ) | |
| with gr.Row(): | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=10, | |
| step=1, | |
| value=5, | |
| ) | |
| gr.Examples( | |
| examples = examples, | |
| inputs = [prompt] | |
| ) | |
| run_button.click( | |
| fn = infer, | |
| inputs = [prompt, num_inference_steps], | |
| outputs = [result] | |
| ) | |
| demo.queue().launch(share=True) |