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Browse files- __pycache__/app.cpython-310.pyc +0 -0
- app.py +19 -5
__pycache__/app.cpython-310.pyc
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Binary files a/__pycache__/app.cpython-310.pyc and b/__pycache__/app.cpython-310.pyc differ
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app.py
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@@ -2,9 +2,10 @@ import gradio as gr
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import torch
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from diffusers import AutoPipelineForInpainting, UNet2DConditionModel
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from share_btn import community_icon_html, loading_icon_html, share_js
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unet = UNet2DConditionModel.from_pretrained("valhalla/sdxl-inpaint-ema")
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pipe = AutoPipelineForInpainting.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
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def read_content(file_path: str) -> str:
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return content
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def predict(dict, prompt="", guidance_scale=7.5, steps=20, strength=1.0):
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init_image = dict["image"].convert("RGB").resize((1024, 1024))
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mask = dict["mask"].convert("RGB").resize((1024, 1024))
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output = pipe(prompt = prompt, image=init_image, mask_image=mask, guidance_scale=guidance_scale,
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return output.images[0], gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
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@@ -79,6 +91,8 @@ with image_blocks as demo:
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guidance_scale = gr.Number(value=7.5, minimum=1.0, maximum=20.0, step=0.1, label="guidance_scale")
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steps = gr.Number(value=20, minimum=10, maximum=50, step=0.1, label="steps")
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strength = gr.Number(value=1.0, minimum=0.0, maximum=1.0, step=0.05, label="strength")
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btn = gr.Button("Inpaint!").style(
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margin=False,
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share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
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btn.click(fn=predict, inputs=[image, prompt, guidance_scale, steps, strength], outputs=[image_out, community_icon, loading_icon, share_button])
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share_button.click(None, [], [], _js=share_js)
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gr.HTML(
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"""
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<div class="footer">
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<p>Model by <a href="https://huggingface.co/diffusers" style="text-decoration: underline;" target="_blank">
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</p>
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</div>
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"""
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import torch
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from diffusers import AutoPipelineForInpainting, UNet2DConditionModel
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import diffusers
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from share_btn import community_icon_html, loading_icon_html, share_js
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unet = UNet2DConditionModel.from_pretrained("valhalla/sdxl-inpaint-ema", torch_dtype=torch.float16)
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pipe = AutoPipelineForInpainting.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
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def read_content(file_path: str) -> str:
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return content
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def predict(dict, prompt="", guidance_scale=7.5, steps=20, strength=1.0, scheduler="EulerDiscreteScheduler"):
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scheduler_class_name = scheduler.split("")[0]
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scheduler = getattr(diffusers, scheduler_class_name)
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add_kwargs = {}
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if len(scheduler.split("")) > 1:
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add_kwargs["use_karras"] = True
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if len(scheduler.split("")) > 2:
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add_kwargs["algorithm_type"] = "sde-dpmsolver++"
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pipe.scheduler = scheduler.from_config(pipe.scheduler.config, **add_kwargs)
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init_image = dict["image"].convert("RGB").resize((1024, 1024))
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mask = dict["mask"].convert("RGB").resize((1024, 1024))
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output = pipe(prompt = prompt, image=init_image, mask_image=mask, guidance_scale=guidance_scale, num_inference_steps=int(steps), strength=strength)
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return output.images[0], gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
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guidance_scale = gr.Number(value=7.5, minimum=1.0, maximum=20.0, step=0.1, label="guidance_scale")
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steps = gr.Number(value=20, minimum=10, maximum=50, step=0.1, label="steps")
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strength = gr.Number(value=1.0, minimum=0.0, maximum=1.0, step=0.05, label="strength")
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schedulers = ["DEISMultistepScheduler", "HeunDiscreteScheduler", "EulerDiscreteScheduler", "DPMSolverMultistepScheduler", "DPMSolverMultistepScheduler Karras", "DPMSolverMultistepScheduler Karras SDE"]
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scheduler = gr.Dropdown(choices=schedulers, value="EulerDiscreteScheduler")
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btn = gr.Button("Inpaint!").style(
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margin=False,
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share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
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btn.click(fn=predict, inputs=[image, prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, community_icon, loading_icon, share_button])
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share_button.click(None, [], [], _js=share_js)
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gr.HTML(
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"""
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<div class="footer">
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<p>Model by <a href="https://huggingface.co/diffusers" style="text-decoration: underline;" target="_blank">Diffusers</a> - Gradio Demo by 🤗 Hugging Face
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</p>
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</div>
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"""
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