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| import os | |
| import gradio as gr | |
| import numpy as np | |
| import random | |
| import spaces | |
| import torch | |
| #from diffusers import DiffusionPipeline | |
| from diffusers import AutoPipelineForImage2Image | |
| from huggingface_hub import InferenceClient | |
| dtype = torch.bfloat16 | |
| device = "cuda" | |
| #if torch.cuda.is_available() else "cpu" | |
| #pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device) | |
| sdxl = InferenceClient(model="stabilityai/stable-diffusion-xl-base-1.0", token=os.environ['HF_TOKEN']) | |
| print('sdxl loaded') | |
| "kandinsky-community/kandinsky-2-2-decoder" | |
| #pipeline2Image = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtypes=torch.bfloat16).to(device) | |
| #pipeline2Image = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtypes=torch.bfloat16).to(device) | |
| pipeline2Image = AutoPipelineForImage2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=dtype) | |
| pipeline2Image.enable_model_cpu_offload() | |
| print("pipeline 2 image loaded") | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MAX_IMAGE_SIZE = 2048 | |
| # (duration=190) | |
| #@spaces.GPU | |
| def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)): | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| # generator = torch.Generator().manual_seed(seed) | |
| # image = pipe( | |
| # prompt=prompt, | |
| # width=width, | |
| # height=height, | |
| # num_inference_steps=num_inference_steps, | |
| # generator=generator, | |
| # guidance_scale=guidance_scale | |
| # ).images[0] | |
| image = sdxl.text_to_image( | |
| prompt, | |
| guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, seed=seed,width=width, height=height | |
| ) | |
| return image, seed | |
| examples = [ | |
| "a tiny astronaut hatching from an egg on the moon", | |
| "a cat holding a sign that says hello world", | |
| "an anime illustration of a wiener schnitzel", | |
| ] | |
| css=""" | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 520px; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown(f"""# FLUX.1 [dev] | |
| 12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) | |
| [[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)] | |
| """) | |
| with gr.Row(): | |
| prompt = gr.Text( | |
| label="Prompt", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your prompt", | |
| container=False, | |
| ) | |
| run_button = gr.Button("Run", scale=0) | |
| result = gr.Image(label="Result", show_label=False) | |
| with gr.Accordion("Advanced Settings", open=False): | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| with gr.Row(): | |
| width = gr.Slider( | |
| label="Width", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=1024, | |
| ) | |
| height = gr.Slider( | |
| label="Height", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=1024, | |
| ) | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance Scale", | |
| minimum=1, | |
| maximum=15, | |
| step=0.1, | |
| value=3.5, | |
| ) | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=50, | |
| step=1, | |
| value=28, | |
| ) | |
| gr.Examples( | |
| examples=examples, | |
| fn=infer, | |
| inputs=[prompt], | |
| outputs=[result, seed], | |
| cache_examples="lazy" | |
| ) | |
| gr.on( | |
| triggers=[run_button.click, prompt.submit], | |
| fn=infer, | |
| inputs=[prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], | |
| outputs=[result, seed] | |
| ) | |
| # Adding image input options at the bottom | |
| gr.Markdown("## Upload or select an additional image") | |
| with gr.Row(): | |
| uploaded_image = gr.Image(label="Upload Image", type="pil") | |
| image_url = gr.Textbox(label="Image URL", placeholder="Enter image URL") | |
| use_generated_image = gr.Button("Use Generated Image") | |
| with gr.Accordion("Advanced Settings", open=False): | |
| seed2 = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| ) | |
| randomize_seed2 = gr.Checkbox(label="Randomize seed", value=True) | |
| with gr.Row(): | |
| width2 = gr.Slider( | |
| label="Width", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=1024, | |
| ) | |
| height2 = gr.Slider( | |
| label="Height", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=1024, | |
| ) | |
| with gr.Row(): | |
| strength2 = gr.Slider( | |
| label="Strength", | |
| minimum=.1, | |
| maximum=1, | |
| step=0.1, | |
| value=.5, | |
| ) | |
| guidance_scale2 = gr.Slider( | |
| label="Guidance Scale", | |
| minimum=1, | |
| maximum=15, | |
| step=0.1, | |
| value=3.5, | |
| ) | |
| num_inference_steps2 = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=50, | |
| step=1, | |
| value=28, | |
| ) | |
| prompt2 = gr.Text( | |
| label="Prompt", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your prompt", | |
| container=False, | |
| ) | |
| run2_button = gr.Button("Run", scale=0) | |
| additional_image_output = gr.Image(label="Selected Image", show_label=False) | |
| def select_image(uploaded_image, image_url, use_generated=False): | |
| if use_generated: | |
| return result.value | |
| elif uploaded_image is not None: | |
| return uploaded_image | |
| elif image_url: | |
| try: | |
| img = gr.Image.load(image_url) | |
| return img | |
| except Exception as e: | |
| return f"Failed to load image from URL: {e}" | |
| return None | |
| def image2image(uploaded_image, image_url, use_generated=False): | |
| image = select_image(uploaded_image, image_url, use_generated=use_generated) | |
| #prompt = "one awesome dude" | |
| #generator = torch.Generator(device=device).manual_seed(1024) | |
| #image = pipeline2Image(prompt=prompt, image=image, strength=0.75, guidance_scale=7.5, generator=generator).images[0] | |
| return image | |
| use_generated_image.click(fn=lambda: image2image(None, None, True), inputs=[], outputs=additional_image_output) | |
| uploaded_image.change(fn=image2image, inputs=[uploaded_image, image_url, gr.State(False)], outputs=additional_image_output) | |
| image_url.submit(fn=image2image, inputs=[uploaded_image, image_url, gr.State(False)], outputs=additional_image_output) | |
| def infer2(prompt, image, seed=42, randomize_seed=False, width=1024, height=1024, strength=.5, guidance_scale=5.0, num_inference_steps=28): | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| image2 = pipeline2Image(prompt=prompt, image=image, strength=strength, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, generator=generator).images[0] | |
| # generator = torch.Generator().manual_seed(seed) | |
| # image = pipe( | |
| # prompt=prompt, | |
| # width=width, | |
| # height=height, | |
| # num_inference_steps=num_inference_steps, | |
| # generator=generator, | |
| # guidance_scale=guidance_scale | |
| # ).images[0] | |
| return image2, seed | |
| final_image_output = gr.Image(label="Final Image", show_label=False) | |
| gr.on( | |
| triggers=[run2_button.click, prompt2.submit], | |
| fn=infer2, | |
| inputs=[prompt2, torch.from_numpy(additional_image_output), seed2, randomize_seed2, width2, height2, strength2, guidance_scale2, num_inference_steps2], | |
| outputs=[final_image_output, seed2] | |
| ) | |
| demo.launch() | |