Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -1,9 +1,12 @@
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import gradio as gr
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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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device = "cuda" if torch.cuda.is_available() else "cpu"
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unet = UNet2DConditionModel.from_pretrained(
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@@ -12,7 +15,8 @@ unet = UNet2DConditionModel.from_pretrained(
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torch_dtype=torch.float16,
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)
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scheduler = DDIMScheduler.from_pretrained("briaai/BRIA-2.3", subfolder="scheduler",
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pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
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"briaai/BRIA-2.3",
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@@ -114,15 +118,11 @@ with image_blocks as demo:
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with gr.Column():
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image_out = gr.Image(label="Output", elem_id="output-img", height=400)
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community_icon = gr.HTML(community_icon_html)
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loading_icon = gr.HTML(loading_icon_html)
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share_button = gr.Button("Share to community", elem_id="share-btn",visible=True)
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btn.click(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, share_btn_container], api_name='run')
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prompt.submit(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, share_btn_container])
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share_button.click(None, [], [], _js=share_js)
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gr.Examples(
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examples=[
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import gradio as gr
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import torch
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import diffusers
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import os
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hf_token = os.environ.get("HF_TOKEN")
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import spaces
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from diffusers import StableDiffusionXLInpaintPipeline, DDIMScheduler, UNet2DConditionModel
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device = "cuda" if torch.cuda.is_available() else "cpu"
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unet = UNet2DConditionModel.from_pretrained(
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torch_dtype=torch.float16,
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)
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scheduler = DDIMScheduler.from_pretrained("briaai/BRIA-2.3", subfolder="scheduler",
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rescale_betas_zero_snr=True,prediction_type='v_prediction',timestep_spacing="trailing",clip_sample=False)
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pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
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"briaai/BRIA-2.3",
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with gr.Column():
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image_out = gr.Image(label="Output", elem_id="output-img", height=400)
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btn.click(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, share_btn_container], api_name='run')
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prompt.submit(fn=predict, inputs=[image, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, share_btn_container])
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gr.Examples(
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examples=[
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