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Update app.py
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app.py
CHANGED
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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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import spaces
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import torch
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from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
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from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
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dtype = torch.bfloat16
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device = "cuda" if torch.cuda.is_available() else "cpu"
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pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 2048
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@spaces.GPU(duration=
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def infer(prompt, seed=
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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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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width = width,
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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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examples = [
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"a tiny astronaut hatching from an egg on the moon",
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"a cat holding a sign that says hello world",
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"an anime illustration of a wiener schnitzel",
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]
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css="""
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#col-container {
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margin: 0 auto;
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max-width: 520px;
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}
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"""
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with gr.Blocks(css=
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with gr.Column(elem_id="col-container"):
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gr.Markdown(
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12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)
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[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
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""")
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with gr.Row():
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prompt = gr.Text(
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label="Prompt",
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show_label=False,
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max_lines=
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placeholder="
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container=False
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)
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run_button = gr.Button("Run", scale=0)
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result =
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with gr.Accordion("Advanced Settings", open=False):
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@@ -71,52 +62,45 @@ with gr.Blocks(css=css) as demo:
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=0,
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)
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randomize_seed = gr.Checkbox(label="Randomize
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with gr.Row():
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width = gr.Slider(
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label="Width",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=
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)
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height = gr.Slider(
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label="Height",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=
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)
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with gr.Row():
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guidance_scale = gr.Slider(
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label="Guidance Scale",
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minimum=1,
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maximum=15,
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step=0.1,
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value=3
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)
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num_inference_steps = gr.Slider(
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label="
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minimum=1,
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maximum=50,
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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
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fn
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inputs
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outputs
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cache_examples="lazy"
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)
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import random
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import gradio as gr
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import numpy as np
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import spaces
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import torch
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from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
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from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
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from gradio_imagefeed import ImageFeed
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dtype = torch.bfloat16
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device = "cuda" if torch.cuda.is_available() else "cpu"
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pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 2048
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LICENSE=f"""# Better UI for FLUX.1 [dev] [[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)]"""
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CSS = "#col-container { margin: 0 auto; max-width: 900px; }"
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EXAMPLES = ["a tiny elephant hatching from a turtle egg in the palm of a human hand, highly detailed textures, close-up"]
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@spaces.GPU(duration=120)
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def infer(prompt, seed=99999, randomize_seed=True, width=896, height=1152, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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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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width = width,
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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).images[0]
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yield image, seed
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with gr.Blocks(css=CSS) as demo:
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with gr.Column(elem_id="col-container"):
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gr.Markdown(LICENSE)
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with gr.Row():
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prompt = gr.Text(
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label="Prompt",
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show_label=False,
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max_lines=5,
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placeholder="Prompt",
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container=False)
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run_button = gr.Button("Run", scale=0)
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result = ImageFeed(label="Result", show_label=False)
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# result = gr.Image(label="Result", show_label=False)
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with gr.Accordion("Advanced Settings", open=False):
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=random.randint(0, MAX_SEED))
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randomize_seed = gr.Checkbox(label="Randomize", value=True)
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with gr.Row():
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width = gr.Slider(
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label="Width",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=896)
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height = gr.Slider(
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label="Height",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=1152)
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with gr.Row():
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guidance_scale = gr.Slider(
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label="Guidance Scale",
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minimum=1,
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maximum=15,
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step=0.1,
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value=3)
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num_inference_steps = gr.Slider(
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label="Inference Steps",
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minimum=1,
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maximum=50,
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step=1,
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value=28)
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gr.Examples(
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examples=EXAMPLES,
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fn=infer,
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inputs=[prompt],
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outputs=[result, seed],
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cache_examples="lazy"
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)
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