archiving, but i do hope we get somewhere with this!

app.py

@@ -1,180 +1,180 @@
-import gradio as gr
-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
-from gradio_imageslider import ImageSlider
-from PIL import Image, ImageDraw, ImageFont
-
-dtype = torch.bfloat16
-#model_id = "black-forest-labs/FLUX.1-dev"
-model_id = "camenduru/FLUX.1-dev-diffusers"
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-#taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
-good_vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae", torch_dtype=dtype).to(device)
-#pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=dtype, vae=taef1).to(device)
-pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=dtype, vae=good_vae).to(device)
-torch.cuda.empty_cache()
-
-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 get_cmp_image(im1: Image.Image, im2: Image.Image, sigmas: float):
-    dst = Image.new('RGB', (im1.width + im2.width, im1.height))
-    dst.paste(im1.convert('RGB'), (0, 0))
-    dst.paste(im2.convert('RGB'), (im1.width, 0))
-    font = ImageFont.truetype('Roboto-Regular.ttf', 72, encoding='unic')
-    draw = ImageDraw.Draw(dst)
-    draw.text((64, im1.height - 128), 'Default Flux', 'red', font=font)
-    draw.text((im1.width + 64, im1.height - 128), f'Sigmas * factor {sigmas}', 'red', font=font)
-    return dst
-
-@spaces.GPU(duration=90)
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, mul_sigmas=0.95, is_cmp=True, progress=gr.Progress(track_tqdm=True)):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator().manual_seed(seed)
-    
-    sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
-    sigmas = sigmas * mul_sigmas
-
-    image_sigmas = pipe(
-            prompt=prompt,
-            guidance_scale=guidance_scale,
-            num_inference_steps=num_inference_steps,
-            width=width,
-            height=height,
-            generator=generator,
-            output_type="pil",
-            sigmas=sigmas
-        ).images[0]
-    
-    if is_cmp:
-        image_def = pipe(
-                prompt=prompt,
-                guidance_scale=guidance_scale,
-                num_inference_steps=num_inference_steps,
-                width=width,
-                height=height,
-                generator=generator,
-                output_type="pil",
-            ).images[0]
-        return [image_def, image_sigmas], get_cmp_image(image_def, image_sigmas, mul_sigmas), seed
-    else: return [image_sigmas, image_sigmas], None, 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] sigmas test
-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)
-        result = ImageSlider(label="Result", show_label=False, type="pil", slider_color="pink")
-        result_cmp = gr.Image(label="Result (comparing)", show_label=False, type="pil", format="png", height=256, show_download_button=True, show_share_button=False)
-        
-        with gr.Accordion("Advanced Settings", open=True):
-            with gr.Row():
-                sigmas = gr.Slider(
-                    label="Sigmas",
-                    minimum=0,
-                    maximum=1.0,
-                    step=0.01,
-                    value=0.95,
-                )
-                is_cmp = gr.Checkbox(label="Compare images with/without sigmas", value=True)
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=9119,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
-            
-            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, result_cmp, 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, sigmas, is_cmp],
-        outputs = [result, result_cmp, seed]
-    )
-
+import gradio as gr
+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
+from gradio_imageslider import ImageSlider
+from PIL import Image, ImageDraw, ImageFont
+
+dtype = torch.bfloat16
+#model_id = "black-forest-labs/FLUX.1-dev"
+model_id = "camenduru/FLUX.1-dev-diffusers"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+#taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
+good_vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae", torch_dtype=dtype).to(device)
+#pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=dtype, vae=taef1).to(device)
+pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=dtype, vae=good_vae).to(device)
+torch.cuda.empty_cache()
+
+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 get_cmp_image(im1: Image.Image, im2: Image.Image, sigmas: float):
+    dst = Image.new('RGB', (im1.width + im2.width, im1.height))
+    dst.paste(im1.convert('RGB'), (0, 0))
+    dst.paste(im2.convert('RGB'), (im1.width, 0))
+    font = ImageFont.truetype('Roboto-Regular.ttf', 72, encoding='unic')
+    draw = ImageDraw.Draw(dst)
+    draw.text((64, im1.height - 128), 'Default Flux', 'red', font=font)
+    draw.text((im1.width + 64, im1.height - 128), f'Sigmas * factor {sigmas}', 'red', font=font)
+    return dst
+
+# @spaces.GPU(duration=90)
+def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, mul_sigmas=0.95, is_cmp=True, progress=gr.Progress(track_tqdm=True)):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator().manual_seed(seed)
+    
+    sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+    sigmas = sigmas * mul_sigmas
+
+    image_sigmas = pipe(
+            prompt=prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator,
+            output_type="pil",
+            sigmas=sigmas
+        ).images[0]
+    
+    if is_cmp:
+        image_def = pipe(
+                prompt=prompt,
+                guidance_scale=guidance_scale,
+                num_inference_steps=num_inference_steps,
+                width=width,
+                height=height,
+                generator=generator,
+                output_type="pil",
+            ).images[0]
+        return [image_def, image_sigmas], get_cmp_image(image_def, image_sigmas, mul_sigmas), seed
+    else: return [image_sigmas, image_sigmas], None, 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] sigmas test
+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)
+        result = ImageSlider(label="Result", show_label=False, type="pil", slider_color="pink")
+        result_cmp = gr.Image(label="Result (comparing)", show_label=False, type="pil", format="png", height=256, show_download_button=True, show_share_button=False)
+        
+        with gr.Accordion("Advanced Settings", open=True):
+            with gr.Row():
+                sigmas = gr.Slider(
+                    label="Sigmas",
+                    minimum=0,
+                    maximum=1.0,
+                    step=0.01,
+                    value=0.95,
+                )
+                is_cmp = gr.Checkbox(label="Compare images with/without sigmas", value=True)
+            
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=9119,
+            )
+            
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
+            
+            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, result_cmp, 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, sigmas, is_cmp],
+        outputs = [result, result_cmp, seed]
+    )
+
 demo.launch()