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Runtime error
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7cb93bd
1
Parent(s):
42d89c8
add token
Browse files
app.py
CHANGED
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@@ -2,6 +2,7 @@ from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeli
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import gradio as gr
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import torch
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from PIL import Image
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scheduler = DPMSolverMultistepScheduler(
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beta_start=0.00085,
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@@ -56,16 +57,18 @@ last_mode = "txt2img"
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current_model = models[1]
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current_model_path = current_model.path
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if is_colab:
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pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16, scheduler=scheduler)
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else: # download all models
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vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae", torch_dtype=torch.float16)
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for model in models[1:]:
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try:
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unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet", torch_dtype=torch.float16)
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model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler)
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model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler)
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except:
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models.remove(model)
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pipe = models[1].pipe_t2i
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@@ -104,7 +107,7 @@ def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, g
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current_model_path = model_path
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if is_colab or current_model == models[0]:
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pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16)
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else:
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pipe.to("cpu")
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pipe = current_model.pipe_t2i
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@@ -135,7 +138,7 @@ def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, w
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current_model_path = model_path
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if is_colab or current_model == models[0]:
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16)
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else:
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pipe.to("cpu")
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pipe = current_model.pipe_i2i
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@@ -266,19 +269,21 @@ with gr.Blocks(css=css) as demo:
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prompt.submit(inference, inputs=inputs, outputs=image_out)
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generate.click(inference, inputs=inputs, outputs=image_out)
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ex = gr.Examples([
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[models[1].name, "jason bateman disassembling the demon core", 7.5, 50],
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[models[4].name, "portrait of dwayne johnson", 7.0, 75],
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[models[5].name, "portrait of a beautiful alyx vance half life", 10, 50],
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[models[6].name, "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7.0, 45],
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[models[5].name, "fantasy portrait painting, digital art", 4.0, 30],
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], [model_name, prompt, guidance, steps, seed], image_out, inference, cache_examples=False)
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gr.Markdown('''
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Models by [@nitrosocke](https://huggingface.co/nitrosocke), [@haruu1367](https://twitter.com/haruu1367), [@Helixngc7293](https://twitter.com/DGSpitzer) and others. β€οΈ<br>
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Space by: [](https://twitter.com/hahahahohohe)
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-

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if not is_colab:
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import gradio as gr
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import torch
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from PIL import Image
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import os
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scheduler = DPMSolverMultistepScheduler(
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beta_start=0.00085,
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current_model = models[1]
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current_model_path = current_model.path
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auth_token = os.getenv("HUGGING_FACE_HUB_TOKEN")
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if is_colab:
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pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16, scheduler=scheduler, use_auth_token=auth_token)
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else: # download all models
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vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae", torch_dtype=torch.float16, use_auth_token=auth_token)
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for model in models[1:]:
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try:
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unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet", torch_dtype=torch.float16, use_auth_token=auth_token)
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model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler, use_auth_token=auth_token)
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model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler, use_auth_token=auth_token)
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except:
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models.remove(model)
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pipe = models[1].pipe_t2i
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current_model_path = model_path
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if is_colab or current_model == models[0]:
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pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, use_auth_token=auth_token)
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else:
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pipe.to("cpu")
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pipe = current_model.pipe_t2i
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current_model_path = model_path
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if is_colab or current_model == models[0]:
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, use_auth_token=auth_token)
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else:
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pipe.to("cpu")
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pipe = current_model.pipe_i2i
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prompt.submit(inference, inputs=inputs, outputs=image_out)
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generate.click(inference, inputs=inputs, outputs=image_out)
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# TODO: the docs here are wrong.
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ex = gr.Examples([
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[models[1+2].name, "jason bateman disassembling the demon core", 7.5, 50],
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[models[4+2].name, "portrait of dwayne johnson", 7.0, 75],
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[models[5+2].name, "portrait of a beautiful alyx vance half life", 10, 50],
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[models[6+2].name, "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7.0, 45],
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[models[5+2].name, "fantasy portrait painting, digital art", 4.0, 30],
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], [model_name, prompt, guidance, steps, seed], image_out, inference, cache_examples=False)
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gr.Markdown('''
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Models by [@nitrosocke](https://huggingface.co/nitrosocke), [@haruu1367](https://twitter.com/haruu1367), [@Helixngc7293](https://twitter.com/DGSpitzer) and others. β€οΈ<br>
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Space by: [](https://twitter.com/hahahahohohe)
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''')
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if not is_colab:
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