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Build error
Build error
add lora weight
Browse files
app.py
CHANGED
@@ -17,8 +17,8 @@ MAX_IMAGE_SIZE = 2048
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# Initialize the pipeline globally
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pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)
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@spaces.GPU(duration=
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def infer(prompt, lora_model, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
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global pipe
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# Load LoRA if specified
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@@ -39,7 +39,8 @@ def infer(prompt, lora_model, seed=42, randomize_seed=False, width=1024, height=
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height=height,
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num_inference_steps=num_inference_steps,
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generator=generator,
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guidance_scale=guidance_scale
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).images[0]
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# Unload LoRA weights after generation
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@@ -127,6 +128,13 @@ with gr.Blocks(css=css) as demo:
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step=1,
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value=28,
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)
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gr.Examples(
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examples=examples,
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@@ -139,8 +147,8 @@ with gr.Blocks(css=css) as demo:
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gr.on(
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triggers=[run_button.click, prompt.submit],
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fn=infer,
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inputs=[prompt, lora_model, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
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outputs=[result, seed, output_message]
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)
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demo.launch()
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# Initialize the pipeline globally
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pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)
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@spaces.GPU(duration=300)
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def infer(prompt, lora_model, lora_weight=1.0, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
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global pipe
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# Load LoRA if specified
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height=height,
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num_inference_steps=num_inference_steps,
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generator=generator,
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guidance_scale=guidance_scale,
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cross_attention_kwargs={"scale": lora_weight}
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).images[0]
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# Unload LoRA weights after generation
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step=1,
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value=28,
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)
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lora_weight = gr.Slider(
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label="LoRA Weight",
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minimum=0,
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maximum=2,
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step=0.01,
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value=1.0,
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)
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gr.Examples(
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examples=examples,
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gr.on(
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triggers=[run_button.click, prompt.submit],
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fn=infer,
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inputs=[prompt, lora_model, lora_weight, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
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outputs=[result, seed, output_message]
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)
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demo.launch()
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