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Update app.py
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app.py
CHANGED
@@ -4,7 +4,7 @@ import random
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import os
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# import spaces #[uncomment to use ZeroGPU]
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from diffusers import AutoPipelineForText2Image
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import torch
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device = "cuda" if torch.cuda.is_available() else "cpu"
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@@ -12,11 +12,14 @@ hf_token = os.getenv('HF_TOKEN')
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if torch.cuda.is_available():
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torch_dtype = torch.float16
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else:
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torch_dtype = torch.float32
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pipe = pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", token=hf_token, torch_dtype=torch.bfloat16)
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pipe.load_lora_weights('aleksa-codes/flux-ghibsky-illustration', weight_name='lora.safetensors')
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pipe = pipe.to(device)
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MAX_SEED = np.iinfo(np.int32).max
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@@ -26,13 +29,12 @@ MAX_IMAGE_SIZE = 1024
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# @spaces.GPU #[uncomment to use ZeroGPU]
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def infer(
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prompt,
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num_inference_steps,
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progress=gr.Progress(track_tqdm=True),
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):
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if randomize_seed:
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@@ -42,12 +44,12 @@ def infer(
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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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width=width,
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height=height,
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generator=generator,
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).images[0]
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return image, seed
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import os
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# import spaces #[uncomment to use ZeroGPU]
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from diffusers import AutoPipelineForText2Image, AutoencoderKL, AutoencoderTiny
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import torch
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device = "cuda" if torch.cuda.is_available() else "cpu"
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if torch.cuda.is_available():
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torch_dtype = torch.float16
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torch.cuda.empty_cache()
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else:
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torch_dtype = torch.float32
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taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
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pipe = pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", token=hf_token, torch_dtype=torch.bfloat16)
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pipe.load_lora_weights('aleksa-codes/flux-ghibsky-illustration', weight_name='lora.safetensors')
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good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
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pipe = pipe.to(device)
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MAX_SEED = np.iinfo(np.int32).max
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# @spaces.GPU #[uncomment to use ZeroGPU]
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def infer(
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prompt,
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seed=42,
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randomize_seed=True,
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width=1024,
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height=1024,
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guidance_scale=3.5,
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num_inference_steps=28,
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progress=gr.Progress(track_tqdm=True),
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):
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if randomize_seed:
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image = pipe(
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prompt=prompt,
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guidance_scale=guidance_scale,
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num_inference_steps=num_inference_steps,
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width=width,
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height=height,
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generator=generator,
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good_vae=good_vae,
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).images[0]
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return image, seed
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