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Parent(s):
fe3a594
Update app.py
Browse files
app.py
CHANGED
@@ -9,10 +9,11 @@ import numpy as np
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from PIL import Image
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import spaces
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import torch
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from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
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DESCRIPTION = """
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# [
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"""
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if not torch.cuda.is_available():
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@@ -51,6 +52,14 @@ if torch.cuda.is_available():
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pipe_epic.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_epic.scheduler.config)
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pipe_epic.to(device)
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pipe_xl = StableDiffusionXLPipeline.from_pretrained(
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"fluently/Fluently-XL-v2",
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torch_dtype=torch.float16,
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@@ -74,10 +83,20 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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seed = random.randint(0, MAX_SEED)
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return seed
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@spaces.GPU(enable_queue=True)
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def generate(
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model,
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prompt: str,
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negative_prompt: str = "",
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use_negative_prompt: bool = False,
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@@ -127,7 +146,7 @@ def generate(
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num_images_per_prompt=1,
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output_type="pil",
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).images
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images = pipe_xl(
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prompt=prompt,
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negative_prompt=negative_prompt,
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@@ -138,11 +157,28 @@ def generate(
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num_images_per_prompt=1,
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output_type="pil",
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).images
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image_paths = [save_image(img) for img in images]
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print(image_paths)
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return image_paths, seed
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@@ -173,10 +209,20 @@ with gr.Blocks(title="Fluently Playground", css=css) as demo:
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with gr.Row():
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model = gr.Radio(
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label="Model",
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choices=["Fluently XL v2", "Fluently v3.5", "Fluently Anime", "Fluently Epic"],
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value="Fluently v3.5",
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interactive=True,
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)
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with gr.Group():
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with gr.Row():
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prompt = gr.Text(
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@@ -244,6 +290,13 @@ with gr.Blocks(title="Fluently Playground", css=css) as demo:
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outputs=negative_prompt,
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api_name=False,
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)
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gr.on(
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@@ -255,6 +308,10 @@ with gr.Blocks(title="Fluently Playground", css=css) as demo:
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fn=generate,
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inputs=[
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model,
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prompt,
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negative_prompt,
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use_negative_prompt,
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from PIL import Image
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import spaces
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import torch
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from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline, StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
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from diffusers.utils import load_image
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DESCRIPTION = """
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# [Fluently Playground](https://huggingface.co/fluently)
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"""
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if not torch.cuda.is_available():
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pipe_epic.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_epic.scheduler.config)
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pipe_epic.to(device)
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pipe_inpaint = StableDiffusionInpaintPipeline.from_pretrained(
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"fluently/Fluently-v3-inpainting",
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torch_dtype=torch.float16,
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use_safetensors=True,
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)
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#pipe_inpaint.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_inpaint.scheduler.config)
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pipe_inpaint.to(device)
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pipe_xl = StableDiffusionXLPipeline.from_pretrained(
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"fluently/Fluently-XL-v2",
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torch_dtype=torch.float16,
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seed = random.randint(0, MAX_SEED)
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return seed
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def get_model(model):
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if model == "Fluently v3 inpaint":
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return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
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else:
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return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
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@spaces.GPU(enable_queue=True)
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def generate(
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model,
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inpaint_image,
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mask_image,
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blur_factor,
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strength,
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prompt: str,
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negative_prompt: str = "",
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use_negative_prompt: bool = False,
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num_images_per_prompt=1,
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output_type="pil",
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).images
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elif model == "Fluently XL v2":
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images = pipe_xl(
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prompt=prompt,
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negative_prompt=negative_prompt,
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num_images_per_prompt=1,
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output_type="pil",
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).images
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else:
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blurred_mask = pipe_inpaint.mask_processor.blur(mask_image, blur_factor=blur_factor)
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images = pipe_inpaint(
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prompt=prompt,
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image=inpaint_image,
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mask_image=blurred_mask,
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negative_prompt=negative_prompt,
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width=width,
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height=height,
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guidance_scale=guidance_scale,
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num_inference_steps=30,
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strength=strength,
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num_images_per_prompt=1,
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output_type="pil",
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).images
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image_paths = [save_image(img) for img in images]
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print(image_paths)
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return image_paths, seed
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with gr.Row():
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model = gr.Radio(
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label="Model",
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choices=["Fluently XL v2", "Fluently v3.5", "Fluently Anime", "Fluently Epic", "Fluently v3 inpaint"],
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value="Fluently v3.5",
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interactive=True,
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)
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md_mask = gr.Markdown("""
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β οΈ To generate an inpaint mask, go [here](https://huggingface.co/spaces/stevhliu/inpaint-mask-maker).
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""", visible=False)
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inpaint_image = gr.Image(label="Inpaint Image", interactive=True, scale=5, visible=False, type="pil")
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mask_image = gr.Image(label="Mask Image", interactive=True, scale=5, visible=False, type="pil")
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blur_factor = gr.Slider(label="Mask Blur Factor", minimum=0, maximum=100, value=4, step=1, interactive=True, visible=False)
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strength = gr.Slider(label="Denoising Strength", minimum=0.00, maximum=1.00, value=0.70, step=0.01, interactive=True, visible=False)
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with gr.Group():
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with gr.Row():
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prompt = gr.Text(
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outputs=negative_prompt,
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api_name=False,
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)
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model.change(
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fn=get_model,
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inputs=model,
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outputs=[md_mask, inpaint_image, mask_image, blur_factor, strength],
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api_name=False,
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)
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gr.on(
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fn=generate,
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inputs=[
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model,
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inpaint_image,
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mask_image,
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blur_factor,
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strength,
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prompt,
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negative_prompt,
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use_negative_prompt,
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