try flux1 official

app.py

@@ -1,65 +1,204 @@
-import torch
 import gradio as gr
-from diffusers.pipelines.flux.pipeline_flux import FluxPipeline
-from diffusers.models.controlnet_flux import FluxControlNetModel
-from controlnet_aux import CannyDetector
+import numpy as np
+import random
+# import spaces
+import torch
+from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
+from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
+# from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
 
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-base_model = "black-forest-labs/FLUX.1-schnell"
-controlnet_model = "YishaoAI/flux-dev-controlnet-canny-kid-clothes"
-
-controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=dtype)
-pipe = FluxPipeline.from_pretrained(
-    base_model, controlnet=controlnet, torch_dtype=dtype
-).to(device)
-
-pipe.enable_model_cpu_offload()
-# pipe.to("cuda")
+taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
+good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
+pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device)
+torch.cuda.empty_cache()
 
-canny = CannyDetector()
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
 
+# pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
 
-def inpaint(
-    image,
-    mask,
-    prompt,
-    strength,
-    num_inference_steps,
-    guidance_scale,
-    controlnet_conditioning_scale,
-):
-    canny_image = canny(image)
+# @spaces.GPU(duration=75)
+def infer(prompt, 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)
+    generator = torch.Generator().manual_seed(seed)
+    
+    for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
+            prompt=prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator,
+            output_type="pil",
+            good_vae=good_vae,
+        ):
+            yield img, 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",
+]
 
-    image_res = pipe(
-        prompt,
-        image=image,
-        control_image=canny_image,
-        controlnet_conditioning_scale=controlnet_conditioning_scale,
-        mask_image=mask,
-        strength=strength,
-        num_inference_steps=num_inference_steps,
-        guidance_scale=guidance_scale,
-    ).images[0]
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
 
-    return image_res
+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"
+        )
 
-iface = gr.Interface(
-    fn=inpaint,
-    inputs=[
-        gr.Image(type="pil", label="Input Image"),
-        gr.Image(type="pil", label="Mask Image"),
-        gr.Textbox(label="Prompt"),
-        gr.Slider(0, 1, value=0.95, label="Strength"),
-        gr.Slider(1, 100, value=50, step=1, label="Number of Inference Steps"),
-        gr.Slider(0, 20, value=5, label="Guidance Scale"),
-        gr.Slider(0, 1, value=0.5, label="ControlNet Conditioning Scale"),
-    ],
-    outputs=gr.Image(type="pil", label="Output Image"),
-    title="Flux Inpaint AI Model",
-    description="Upload an image and a mask, then provide a prompt to generate an inpainted image.",
-)
+    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]
+    )
 
-iface.launch()
+demo.launch()
+# import torch
+# import gradio as gr
+# from diffusers.pipelines.flux.pipeline_flux import FluxPipeline
+# from diffusers.models.controlnet_flux import FluxControlNetModel
+# from controlnet_aux import CannyDetector
+#
+# dtype = torch.bfloat16
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+#
+# base_model = "black-forest-labs/FLUX.1-schnell"
+# controlnet_model = "YishaoAI/flux-dev-controlnet-canny-kid-clothes"
+#
+# controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=dtype)
+# pipe = FluxPipeline.from_pretrained(
+#     base_model, controlnet=controlnet, torch_dtype=dtype
+# ).to(device)
+#
+# pipe.enable_model_cpu_offload()
+# # pipe.to("cuda")
+#
+# canny = CannyDetector()
+#
+#
+# def inpaint(
+#     image,
+#     mask,
+#     prompt,
+#     strength,
+#     num_inference_steps,
+#     guidance_scale,
+#     controlnet_conditioning_scale,
+# ):
+#     canny_image = canny(image)
+#
+#     image_res = pipe(
+#         prompt,
+#         image=image,
+#         control_image=canny_image,
+#         controlnet_conditioning_scale=controlnet_conditioning_scale,
+#         mask_image=mask,
+#         strength=strength,
+#         num_inference_steps=num_inference_steps,
+#         guidance_scale=guidance_scale,
+#     ).images[0]
+#
+#     return image_res
+#
+#
+# iface = gr.Interface(
+#     fn=inpaint,
+#     inputs=[
+#         gr.Image(type="pil", label="Input Image"),
+#         gr.Image(type="pil", label="Mask Image"),
+#         gr.Textbox(label="Prompt"),
+#         gr.Slider(0, 1, value=0.95, label="Strength"),
+#         gr.Slider(1, 100, value=50, step=1, label="Number of Inference Steps"),
+#         gr.Slider(0, 20, value=5, label="Guidance Scale"),
+#         gr.Slider(0, 1, value=0.5, label="ControlNet Conditioning Scale"),
+#     ],
+#     outputs=gr.Image(type="pil", label="Output Image"),
+#     title="Flux Inpaint AI Model",
+#     description="Upload an image and a mask, then provide a prompt to generate an inpainted image.",
+# )
+#
+# iface.launch()