Create app_alpha.py

app_alpha.py

@@ -0,0 +1,221 @@
+import gradio as gr
+import numpy as np
+import random
+from PIL import Image
+import spaces
+import torch
+from huggingface_hub import hf_hub_download, HfApi
+from diffusers import FluxPriorReduxPipeline, FluxPipeline
+from diffusers.utils import load_image
+import os
+api = HfApi(
+    token=os.getenv('HF_TOKEN'), # Token is not persisted on the machine.
+)
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
+
+pipe = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev", 
+    torch_dtype=torch.bfloat16,
+    token=os.getenv('HF_TOKEN'),
+).to("cuda")
+pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), lora_scale=0.125)
+pipe.fuse_lora(lora_scale=0.125)
+pipe.to(device="cuda", dtype=torch.bfloat16)
+
+pipe_prior_redux = FluxPriorReduxPipeline.from_pretrained(
+    "ostris/Flex.1-alpha-Redux",
+    text_encoder=pipe.text_encoder,
+    tokenizer=pipe.tokenizer,
+    text_encoder_2=pipe.text_encoder_2,
+    tokenizer_2=pipe.tokenizer_2,
+    torch_dtype=torch.bfloat16
+).to("cuda")
+
+examples = [[Image.open("mona_lisa.jpg"), "pink hair, at the beach", None, "", 0.035, 1., 1., 1., 1., 0, False], 
+            [Image.open("1665_Girl_with_a_Pearl_Earring.jpg"), "", Image.open("dali_example.jpg"), "", 0.08, .4, .6, .33, 1., 1912857110, False]]
+
+@spaces.GPU
+def infer(control_image, prompt,  image_2, prompt_2, reference_scale= 0.03 , 
+          prompt_embeds_scale_1 =1, prompt_embeds_scale_2 =1, pooled_prompt_embeds_scale_1 =1, pooled_prompt_embeds_scale_2 =1,
+          seed=42, randomize_seed=False, width=1024, height=1024, 
+          guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    if image_2 is not None:
+        pipe_prior_output = pipe_prior_redux([control_image, image_2], 
+                                             prompt=[prompt, prompt_2],
+                                            prompt_embeds_scale = [prompt_embeds_scale_1, prompt_embeds_scale_2],
+                                            pooled_prompt_embeds_scale = [pooled_prompt_embeds_scale_1, pooled_prompt_embeds_scale_2])
+    else:
+        pipe_prior_output = pipe_prior_redux(control_image, prompt=prompt,  prompt_embeds_scale = [prompt_embeds_scale_1],
+                                            pooled_prompt_embeds_scale = [pooled_prompt_embeds_scale_1])
+    cond_size = 729
+    hidden_size = 4096
+    max_sequence_length = 512
+    full_attention_size = max_sequence_length + hidden_size + cond_size
+    attention_mask = torch.zeros(
+        (full_attention_size, full_attention_size), device="cuda", dtype=torch.bfloat16
+    )
+    bias = torch.log(
+        torch.tensor(reference_scale, dtype=torch.bfloat16, device="cuda").clamp(min=1e-5, max=1)
+    )
+    attention_mask[:, max_sequence_length : max_sequence_length + cond_size] = bias
+    joint_attention_kwargs=dict(attention_mask=attention_mask)
+    images = pipe(
+        guidance_scale=guidance_scale,
+        width=width, 
+        height=height, 
+        num_inference_steps=num_inference_steps,
+        generator=torch.Generator("cpu").manual_seed(seed),
+        joint_attention_kwargs=joint_attention_kwargs,
+        **pipe_prior_output,
+    ).images[0]
+    return images, seed
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 960px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""# ⚡️ Fast FLUX.1 Redux [dev] ⚡️
+An adapter for FLUX [dev] to create image variations combined with ByteDance [
+Hyper FLUX 8 Steps LoRA](https://huggingface.co/ByteDance/Hyper-SD) 🏎️
+Now with added support: 
+- prompt input
+- attention masking for improved prompt adherence
+- multiple image interpolation
+
+[[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():
+            with gr.Column():
+                    input_image = gr.Image(label="Image to create variations", type="pil")
+                    prompt = gr.Text(
+                        label="Prompt",
+                        show_label=False,
+                        max_lines=1,
+                        placeholder="Enter your prompt",
+                        container=False,
+                    )
+                    reference_scale = gr.Slider(
+                        info="lower to enhance prompt adherence",
+                        label="Masking Scale",
+                        minimum=0.01,
+                        maximum=0.08,
+                        step=0.001,
+                        value=0.03,
+                    )
+                    run_button = gr.Button("Run")
+            with gr.Column():
+                    image_2 = gr.Image(label="2nd image to create interpolated variations", type="pil")
+                    prompt_2 = gr.Text(
+                        label="2nd Prompt",
+                        show_label=False,
+                        max_lines=1,
+                        placeholder="Enter your prompt",
+                        container=False,
+                    )
+                    
+            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():
+                prompt_embeds_scale_1 = gr.Slider(
+                        label="prompt embeds scale 1st image",
+                        minimum=0,
+                        maximum=1.5,
+                        step=0.01,
+                        value=1,
+                    )
+                prompt_embeds_scale_2 = gr.Slider(
+                        label="prompt embeds scale 2nd image",
+                        minimum=0,
+                        maximum=1.5,
+                        step=0.01,
+                        value=1,
+                    )
+                pooled_prompt_embeds_scale_1 = gr.Slider(
+                        label="pooled prompt embeds scale 1nd image",
+                        minimum=0,
+                        maximum=1.5,
+                        step=0.01,
+                        value=1,
+                    )
+                pooled_prompt_embeds_scale_2 = gr.Slider(
+                        label="pooled prompt embeds scale 2nd image",
+                        minimum=0,
+                        maximum=1.5,
+                        step=0.01,
+                        value=1,
+                    )
+               
+            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=30,
+                    step=1,
+                    value=8,
+                )
+
+        gr.Examples(
+                examples=examples,
+                inputs=[input_image, prompt, image_2, prompt_2, reference_scale, prompt_embeds_scale_1, prompt_embeds_scale_2, pooled_prompt_embeds_scale_1, pooled_prompt_embeds_scale_2, seed, randomize_seed],
+                outputs=[result, seed],
+                fn=infer,
+            )
+
+    gr.on(
+        triggers=[run_button.click],
+        fn = infer,
+        inputs = [input_image, prompt, image_2, prompt_2, reference_scale, prompt_embeds_scale_1, prompt_embeds_scale_2, pooled_prompt_embeds_scale_1, pooled_prompt_embeds_scale_2, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs = [result, seed]
+    )
+
+demo.launch()
+
+