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) @spaces.GPU(duration=190) 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()