starting over simple example

app.py

@@ -6,15 +6,9 @@ import torch
 import gradio as gr
 from huggingface_hub import hf_hub_download
 import spaces
-from comfy import model_management
 
-hf_hub_download(repo_id="black-forest-labs/FLUX.1-Redux-dev", filename="flux1-redux-dev.safetensors", local_dir="models/style_models")
-hf_hub_download(repo_id="black-forest-labs/FLUX.1-Depth-dev", filename="flux1-depth-dev.safetensors", local_dir="models/diffusion_models")
-hf_hub_download(repo_id="Comfy-Org/sigclip_vision_384", filename="sigclip_vision_patch14_384.safetensors", local_dir="models/clip_vision")
-hf_hub_download(repo_id="Kijai/DepthAnythingV2-safetensors", filename="depth_anything_v2_vitl_fp32.safetensors", local_dir="models/depthanything")
-hf_hub_download(repo_id="black-forest-labs/FLUX.1-dev", filename="ae.safetensors", local_dir="models/vae/FLUX1")
-hf_hub_download(repo_id="comfyanonymous/flux_text_encoders", filename="clip_l.safetensors", local_dir="models/text_encoders")
-hf_hub_download(repo_id="comfyanonymous/flux_text_encoders", filename="t5xxl_fp16.safetensors", local_dir="models/text_encoders/t5")
+hf_hub_download(repo_id="Comfy-Org/stable-diffusion-v1-5-archive", filename="v1-5-pruned-emaonly-fp16.safetensors", local_dir="models/checkpoints")
+
 
 def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
     """Returns the value at the given index of a sequence or mapping.
@@ -125,266 +119,86 @@ def import_custom_nodes() -> None:
 
 from nodes import NODE_CLASS_MAPPINGS
 
-intconstant = NODE_CLASS_MAPPINGS["INTConstant"]()
-dualcliploader = NODE_CLASS_MAPPINGS["DualCLIPLoader"]()
-
-#To be added to `model_loaders` as it loads a model
-dualcliploader_357 = dualcliploader.load_clip(
-    clip_name1="t5/t5xxl_fp16.safetensors",
-    clip_name2="clip_l.safetensors",
-    type="flux",
-)
-cr_clip_input_switch = NODE_CLASS_MAPPINGS["CR Clip Input Switch"]()
-cliptextencode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]()
-loadimage = NODE_CLASS_MAPPINGS["LoadImage"]()
-imageresize = NODE_CLASS_MAPPINGS["ImageResize+"]()
-getimagesizeandcount = NODE_CLASS_MAPPINGS["GetImageSizeAndCount"]()
-vaeloader = NODE_CLASS_MAPPINGS["VAELoader"]()
-
-#To be added to `model_loaders` as it loads a model
-vaeloader_359 = vaeloader.load_vae(vae_name="FLUX1/ae.safetensors")
-
-vaeencode = NODE_CLASS_MAPPINGS["VAEEncode"]()
-unetloader = NODE_CLASS_MAPPINGS["UNETLoader"]()
-
-#To be added to `model_loaders` as it loads a model
-unetloader_358 = unetloader.load_unet(
-    unet_name="flux1-depth-dev.safetensors", weight_dtype="default"
-)
-ksamplerselect = NODE_CLASS_MAPPINGS["KSamplerSelect"]()
-randomnoise = NODE_CLASS_MAPPINGS["RandomNoise"]()
-fluxguidance = NODE_CLASS_MAPPINGS["FluxGuidance"]()
-depthanything_v2 = NODE_CLASS_MAPPINGS["DepthAnything_V2"]()
-downloadandloaddepthanythingv2model = NODE_CLASS_MAPPINGS[
-    "DownloadAndLoadDepthAnythingV2Model"
-]()
-
-#To be added to `model_loaders` as it loads a model
-downloadandloaddepthanythingv2model_437 = (
-    downloadandloaddepthanythingv2model.loadmodel(
-        model="depth_anything_v2_vitl_fp32.safetensors"
-    )
-)
-instructpixtopixconditioning = NODE_CLASS_MAPPINGS[
-    "InstructPixToPixConditioning"
-]()
-text_multiline_454 = text_multiline.text_multiline(text="FLUX_Redux")
-clipvisionloader = NODE_CLASS_MAPPINGS["CLIPVisionLoader"]()
-
-#To be added to `model_loaders` as it loads a model
-clipvisionloader_438 = clipvisionloader.load_clip(
-    clip_name="sigclip_vision_patch14_384.safetensors"
-)
-clipvisionencode = NODE_CLASS_MAPPINGS["CLIPVisionEncode"]()
-stylemodelloader = NODE_CLASS_MAPPINGS["StyleModelLoader"]()
-
-#To be added to `model_loaders` as it loads a model
-stylemodelloader_441 = stylemodelloader.load_style_model(
-    style_model_name="flux1-redux-dev.safetensors"
-)
-text_multiline = NODE_CLASS_MAPPINGS["Text Multiline"]()
-emptylatentimage = NODE_CLASS_MAPPINGS["EmptyLatentImage"]()
-cr_conditioning_input_switch = NODE_CLASS_MAPPINGS[
-    "CR Conditioning Input Switch"
-]()
-cr_model_input_switch = NODE_CLASS_MAPPINGS["CR Model Input Switch"]()
-stylemodelapplyadvanced = NODE_CLASS_MAPPINGS["StyleModelApplyAdvanced"]()
-basicguider = NODE_CLASS_MAPPINGS["BasicGuider"]()
-basicscheduler = NODE_CLASS_MAPPINGS["BasicScheduler"]()
-samplercustomadvanced = NODE_CLASS_MAPPINGS["SamplerCustomAdvanced"]()
-vaedecode = NODE_CLASS_MAPPINGS["VAEDecode"]()
-saveimage = NODE_CLASS_MAPPINGS["SaveImage"]()
-imagecrop = NODE_CLASS_MAPPINGS["ImageCrop+"]()
-
-#Add all the models that load a safetensors file
-model_loaders = [dualcliploader_357, vaeloader_359, unetloader_358, clipvisionloader_438, stylemodelloader_441, downloadandloaddepthanythingv2model_437]
-
-# Check which models are valid and how to best load them
-valid_models = [
-    getattr(loader[0], 'patcher', loader[0]) 
-    for loader in model_loaders
-    if not isinstance(loader[0], dict) and not isinstance(getattr(loader[0], 'patcher', None), dict)
-]
-
-#Finally loads the models
-model_management.load_models_gpu(valid_models)
-
-@spaces.GPU(duration=60)
-def generate_image(prompt, structure_image, style_image, depth_strength, style_strength):
+@spaces.GPU(duration=60) #modify the duration for the average it takes for your worflow to run, in seconds
+def generate_image(prompt):
     import_custom_nodes()
     with torch.inference_mode():
-        
-        intconstant_83 = intconstant.get_value(value=1024)
-
-        intconstant_84 = intconstant.get_value(value=1024)
-
-        cr_clip_input_switch_319 = cr_clip_input_switch.switch(
-            Input=1,
-            clip1=get_value_at_index(dualcliploader_357, 0),
-            clip2=get_value_at_index(dualcliploader_357, 0),
-        )
-
-        cliptextencode_174 = cliptextencode.encode(
-            text=prompt,
-            clip=get_value_at_index(cr_clip_input_switch_319, 0),
-        )
-
-        cliptextencode_175 = cliptextencode.encode(
-            text="purple", clip=get_value_at_index(cr_clip_input_switch_319, 0)
-        )
-
-        loadimage_429 = loadimage.load_image(image=structure_image)
-
-        imageresize_72 = imageresize.execute(
-            width=get_value_at_index(intconstant_83, 0),
-            height=get_value_at_index(intconstant_84, 0),
-            interpolation="bicubic",
-            method="keep proportion",
-            condition="always",
-            multiple_of=16,
-            image=get_value_at_index(loadimage_429, 0),
-        )
-
-        getimagesizeandcount_360 = getimagesizeandcount.getsize(
-            image=get_value_at_index(imageresize_72, 0)
-        )
-
-        vaeencode_197 = vaeencode.encode(
-            pixels=get_value_at_index(getimagesizeandcount_360, 0),
-            vae=get_value_at_index(vaeloader_359, 0),
-        )
-
-        ksamplerselect_363 = ksamplerselect.get_sampler(sampler_name="euler")
-
-        randomnoise_365 = randomnoise.get_noise(noise_seed=random.randint(1, 2**64))
-
-        
-        fluxguidance_430 = fluxguidance.append(
-            guidance=15, conditioning=get_value_at_index(cliptextencode_174, 0)
-        )
-
-        depthanything_v2_436 = depthanything_v2.process(
-            da_model=get_value_at_index(downloadandloaddepthanythingv2model_437, 0),
-            images=get_value_at_index(getimagesizeandcount_360, 0),
+        checkpointloadersimple = NODE_CLASS_MAPPINGS["CheckpointLoaderSimple"]()
+        checkpointloadersimple_4 = checkpointloadersimple.load_checkpoint(
+            ckpt_name="v1-5-pruned.safetensors"
         )
 
-        instructpixtopixconditioning_431 = instructpixtopixconditioning.encode(
-            positive=get_value_at_index(fluxguidance_430, 0),
-            negative=get_value_at_index(cliptextencode_175, 0),
-            vae=get_value_at_index(vaeloader_359, 0),
-            pixels=get_value_at_index(depthanything_v2_436, 0),
+        emptylatentimage = NODE_CLASS_MAPPINGS["EmptyLatentImage"]()
+        emptylatentimage_5 = emptylatentimage.generate(
+            width=512, height=512, batch_size=1
         )
 
-        loadimage_440 = loadimage.load_image(image=style_image)
-        
-        clipvisionencode_439 = clipvisionencode.encode(
-            crop="center",
-            clip_vision=get_value_at_index(clipvisionloader_438, 0),
-            image=get_value_at_index(loadimage_440, 0),
+        cliptextencode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]()
+        cliptextencode_6 = cliptextencode.encode(
+            text=prompt, clip=get_value_at_index(checkpointloadersimple_4, 1)
         )
-        
 
-        emptylatentimage_10 = emptylatentimage.generate(
-            width=get_value_at_index(imageresize_72, 1),
-            height=get_value_at_index(imageresize_72, 2),
-            batch_size=1,
+        cliptextencode_7 = cliptextencode.encode(
+            text="(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)",
+            clip=get_value_at_index(checkpointloadersimple_4, 1),
         )
 
-        cr_conditioning_input_switch_271 = cr_conditioning_input_switch.switch(
-            Input=1,
-            conditioning1=get_value_at_index(instructpixtopixconditioning_431, 0),
-            conditioning2=get_value_at_index(instructpixtopixconditioning_431, 0),
-        )
-
-        cr_conditioning_input_switch_272 = cr_conditioning_input_switch.switch(
-            Input=1,
-            conditioning1=get_value_at_index(instructpixtopixconditioning_431, 1),
-            conditioning2=get_value_at_index(instructpixtopixconditioning_431, 1),
-        )
-
-        cr_model_input_switch_320 = cr_model_input_switch.switch(
-            Input=1,
-            model1=get_value_at_index(unetloader_358, 0),
-            model2=get_value_at_index(unetloader_358, 0),
-        )
-
-        stylemodelapplyadvanced_442 = stylemodelapplyadvanced.apply_stylemodel(
-            strength=style_strength,
-            conditioning=get_value_at_index(instructpixtopixconditioning_431, 0),
-            style_model=get_value_at_index(stylemodelloader_441, 0),
-            clip_vision_output=get_value_at_index(clipvisionencode_439, 0),
-        )
-
-        basicguider_366 = basicguider.get_guider(
-            model=get_value_at_index(cr_model_input_switch_320, 0),
-            conditioning=get_value_at_index(stylemodelapplyadvanced_442, 0),
-        )
-
-        basicscheduler_364 = basicscheduler.get_sigmas(
-            scheduler="simple",
-            steps=28,
-            denoise=1,
-            model=get_value_at_index(cr_model_input_switch_320, 0),
-        )
-
-        samplercustomadvanced_362 = samplercustomadvanced.sample(
-            noise=get_value_at_index(randomnoise_365, 0),
-            guider=get_value_at_index(basicguider_366, 0),
-            sampler=get_value_at_index(ksamplerselect_363, 0),
-            sigmas=get_value_at_index(basicscheduler_364, 0),
-            latent_image=get_value_at_index(emptylatentimage_10, 0),
-        )
+        ksampler = NODE_CLASS_MAPPINGS["KSampler"]()
+        vaedecode = NODE_CLASS_MAPPINGS["VAEDecode"]()
+        saveimage = NODE_CLASS_MAPPINGS["SaveImage"]()
 
-        vaedecode_321 = vaedecode.decode(
-            samples=get_value_at_index(samplercustomadvanced_362, 0),
-            vae=get_value_at_index(vaeloader_359, 0),
-        )
+        for q in range(1):
+            ksampler_3 = ksampler.sample(
+                seed=random.randint(1, 2**64),
+                steps=35,
+                cfg=7,
+                sampler_name="dpmpp_2m",
+                scheduler="karras",
+                denoise=1,
+                model=get_value_at_index(checkpointloadersimple_4, 0),
+                positive=get_value_at_index(cliptextencode_6, 0),
+                negative=get_value_at_index(cliptextencode_7, 0),
+                latent_image=get_value_at_index(emptylatentimage_5, 0),
+            )
 
-        saveimage_327 = saveimage.save_images(
-            filename_prefix=get_value_at_index(text_multiline_454, 0),
-            images=get_value_at_index(vaedecode_321, 0),
-        )
+            vaedecode_8 = vaedecode.decode(
+                samples=get_value_at_index(ksampler_3, 0),
+                vae=get_value_at_index(checkpointloadersimple_4, 2),
+            )
 
+            saveimage_9 = saveimage.save_images(
+                filename_prefix="ComfyUI", images=get_value_at_index(vaedecode_8, 0)
+            )
 
-        fluxguidance_382 = fluxguidance.append(
-            guidance=depth_strength,
-            conditioning=get_value_at_index(cr_conditioning_input_switch_272, 0),
-        )
+            saved_path = f"output/{saveimage_9['ui']['images'][0]['filename']}"
+            return saved_path
 
-        imagecrop_447 = imagecrop.execute(
-            width=2000,
-            height=2000,
-            position="top-center",
-            x_offset=0,
-            y_offset=0,
-            image=get_value_at_index(loadimage_440, 0),
-        )
 
-        saved_path = f"output/{saveimage_327['ui']['images'][0]['filename']}"
-        return saved_path
+# if __name__ == "__main__":
+#     main()
 
 if __name__ == "__main__":
-    # Comment out the main() call
+    # Comment out the main() call in the exported Python code
     
     # Start your Gradio app
     with gr.Blocks() as app:
         # Add a title
-        gr.Markdown("# FLUX Style Shaping")
+        gr.Markdown("# Simple Example")
 
         with gr.Row():
             with gr.Column():
                 # Add an input
                 prompt_input = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...")
                 # Add a `Row` to include the groups side by side 
-                with gr.Row():
-                    # First group includes structure image and depth strength
-                    with gr.Group():
-                        structure_image = gr.Image(label="Structure Image", type="filepath")
-                        depth_strength = gr.Slider(minimum=0, maximum=50, value=15, label="Depth Strength")
-                    # Second group includes style image and style strength
-                    with gr.Group():
-                        style_image = gr.Image(label="Style Image", type="filepath")
-                        style_strength = gr.Slider(minimum=0, maximum=1, value=0.5, label="Style Strength")
+                # with gr.Row():
+                #     # First group includes structure image and depth strength
+                #     with gr.Group():
+                #         # structure_image = gr.Image(label="Structure Image", type="filepath")
+                #         # depth_strength = gr.Slider(minimum=0, maximum=50, value=15, label="Depth Strength")
+                #     # Second group includes style image and style strength
+                #     # with gr.Group():
+                #     #     style_image = gr.Image(label="Style Image", type="filepath")
+                #     #     style_strength = gr.Slider(minimum=0, maximum=1, value=0.5, label="Style Strength")
                 
                 # The generate button
                 generate_btn = gr.Button("Generate")
@@ -397,7 +211,8 @@ if __name__ == "__main__":
             # and the output an image
             generate_btn.click(
                 fn=generate_image,
-                inputs=[prompt_input, structure_image, style_image, depth_strength, style_strength],
+                # inputs=[prompt_input, structure_image, style_image, depth_strength, style_strength],
+                inputs=[prompt_input],
                 outputs=[output_image]
             )
         app.launch(share=True)

requirements.txt

@@ -13,12 +13,13 @@ Pillow
 scipy
 tqdm
 psutil
+gradio>=4.0.0
+huggingface_hub
 
 #non essential dependencies:
 kornia>=0.7.1
 spandrel
 soundfile
 
-# needed for huggingface spaces, from custom nodes folders
-accelerate
-huggingface_hub
+# needed for huggingface spaces
+accelerate