Update app.py
Browse files
app.py
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@@ -1,4 +1,3 @@
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#Lisence: Apache 2.0
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import gradio as gr
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import numpy as np
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import random
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@@ -12,10 +11,9 @@ MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1216
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#pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
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#pipe = pipe.to("cuda")
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pipe = StableDiffusionXLPipeline.from_pretrained(
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"yodayo-ai/
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torch_dtype=torch.float16,
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use_safetensors=True,
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custom_pipeline="lpw_stable_diffusion_xl",
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@@ -36,7 +34,7 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
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generator = torch.Generator().manual_seed(seed)
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image = pipe(
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prompt = prompt,
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negative_prompt = negative_prompt,
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guidance_scale = guidance_scale,
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num_inference_steps = num_inference_steps,
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import gradio as gr
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import numpy as np
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import random
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MAX_IMAGE_SIZE = 1216
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#pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
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pipe = StableDiffusionXLPipeline.from_pretrained(
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"yodayo-ai/kivotos-xl-2.0",
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#"yodayo-ai/holodayo-xl-2.1",
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torch_dtype=torch.float16,
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use_safetensors=True,
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custom_pipeline="lpw_stable_diffusion_xl",
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generator = torch.Generator().manual_seed(seed)
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image = pipe(
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prompt = prompt+", masterpiece, best quality, very aesthetic, absurdres",
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negative_prompt = negative_prompt,
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guidance_scale = guidance_scale,
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num_inference_steps = num_inference_steps,
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