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on
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Running
on
Zero
Add live previews (now for realz) (#18)
Browse files- Add live previews (now for realz) (2ed4418f4c92b7cc2acc65a4b655a3a0c28eadd4)
- Update app.py (aaff7099c16d675defc700eb3004d4015af257a0)
- Update loras.json (0a8b917725e831072aefd745f37bcea1888d4a84)
- app.py +28 -12
- live_preview_helpers.py +6 -5
- loras.json +37 -4
app.py
CHANGED
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@@ -5,7 +5,9 @@ import logging
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import torch
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from PIL import Image
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import spaces
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from diffusers import DiffusionPipeline
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from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
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import copy
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import random
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@@ -16,11 +18,18 @@ with open('loras.json', 'r') as f:
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loras = json.load(f)
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# Initialize the base model
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base_model = "black-forest-labs/FLUX.1-dev"
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MAX_SEED = 2**32-1
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class calculateDuration:
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def __init__(self, activity_name=""):
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self.activity_name = activity_name
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@@ -61,10 +70,9 @@ def update_selection(evt: gr.SelectData, width, height):
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def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress):
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pipe.to("cuda")
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generator = torch.Generator(device="cuda").manual_seed(seed)
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with calculateDuration("Generating image"):
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# Generate image
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prompt=prompt_mash,
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num_inference_steps=steps,
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guidance_scale=cfg_scale,
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@@ -72,13 +80,14 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scal
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height=height,
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generator=generator,
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joint_attention_kwargs={"scale": lora_scale},
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def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
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if selected_index is None:
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raise gr.Error("You must select a LoRA before proceeding.")
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-
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selected_lora = loras[selected_index]
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lora_path = selected_lora["repo"]
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trigger_word = selected_lora["trigger_word"]
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@@ -92,24 +101,31 @@ def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, wid
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prompt_mash = f"{trigger_word} {prompt}"
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else:
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prompt_mash = prompt
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# Load LoRA weights
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with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
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if "weights" in selected_lora:
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pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
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#pipe.fuse_lora()
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else:
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pipe.load_lora_weights(lora_path)
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# Set random seed for reproducibility
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with calculateDuration("Randomizing seed"):
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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pipe.to("cpu")
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#pipe.unfuse_lora()
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pipe.unload_lora_weights()
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def get_huggingface_safetensors(link):
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split_link = link.split("/")
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import torch
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from PIL import Image
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import spaces
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from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL
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from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
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from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
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import copy
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import random
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loras = json.load(f)
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# Initialize the base model
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dtype = torch.bfloat16
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device = "cuda" if torch.cuda.is_available() else "cpu"
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base_model = "black-forest-labs/FLUX.1-dev"
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taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
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good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
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pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
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MAX_SEED = 2**32-1
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pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
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class calculateDuration:
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def __init__(self, activity_name=""):
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self.activity_name = activity_name
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def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress):
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pipe.to("cuda")
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generator = torch.Generator(device="cuda").manual_seed(seed)
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with calculateDuration("Generating image"):
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# Generate image
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for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
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prompt=prompt_mash,
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num_inference_steps=steps,
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guidance_scale=cfg_scale,
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height=height,
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generator=generator,
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joint_attention_kwargs={"scale": lora_scale},
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output_type="pil",
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good_vae=good_vae,
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):
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yield img
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def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
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if selected_index is None:
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raise gr.Error("You must select a LoRA before proceeding.")
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selected_lora = loras[selected_index]
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lora_path = selected_lora["repo"]
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trigger_word = selected_lora["trigger_word"]
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prompt_mash = f"{trigger_word} {prompt}"
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else:
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prompt_mash = prompt
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# Load LoRA weights
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with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
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if "weights" in selected_lora:
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pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
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else:
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pipe.load_lora_weights(lora_path)
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# Set random seed for reproducibility
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with calculateDuration("Randomizing seed"):
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress)
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# Consume the generator to get the final image
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final_image = None
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for image in image_generator:
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final_image = image
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yield image, seed # Yield intermediate images and seed
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pipe.to("cpu")
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pipe.unload_lora_weights()
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return final_image, seed # Return the final image and seed
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def get_huggingface_safetensors(link):
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split_link = link.split("/")
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live_preview_helpers.py
CHANGED
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@@ -59,6 +59,7 @@ def flux_pipe_call_that_returns_an_iterable_of_images(
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return_dict: bool = True,
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joint_attention_kwargs: Optional[Dict[str, Any]] = None,
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max_sequence_length: int = 512,
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):
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height = height or self.default_sample_size * self.vae_scale_factor
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width = width or self.default_sample_size * self.vae_scale_factor
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@@ -156,10 +157,10 @@ def flux_pipe_call_that_returns_an_iterable_of_images(
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yield self.image_processor.postprocess(image, output_type=output_type)[0]
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torch.cuda.empty_cache()
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# Final image
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latents = self._unpack_latents(latents, height, width,
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latents = (latents /
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image =
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self.maybe_free_model_hooks()
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torch.cuda.empty_cache()
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return_dict: bool = True,
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joint_attention_kwargs: Optional[Dict[str, Any]] = None,
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max_sequence_length: int = 512,
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good_vae: Optional[Any] = None,
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):
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height = height or self.default_sample_size * self.vae_scale_factor
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width = width or self.default_sample_size * self.vae_scale_factor
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yield self.image_processor.postprocess(image, output_type=output_type)[0]
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torch.cuda.empty_cache()
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# Final image using good_vae
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latents = self._unpack_latents(latents, height, width, good_vae.config.vae_scale_factor)
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latents = (latents / good_vae.config.scaling_factor) + good_vae.config.shift_factor
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image = good_vae.decode(latents, return_dict=False)[0]
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self.maybe_free_model_hooks()
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torch.cuda.empty_cache()
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yield self.image_processor.postprocess(image, output_type=output_type)[0]
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loras.json
CHANGED
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"aspect": "portrait"
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},
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{
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"image": "https://huggingface.co/alvdansen/
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"title": "
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"repo": "alvdansen/
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"trigger_word": ""
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},
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{
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"image": "https://huggingface.co/AIWarper/RubberCore1920sCartoonStyle/resolve/main/images/Rub_00006_.png",
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"trigger_word": "RU883R style",
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"trigger_position": "prepend"
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},
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{
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"image": "https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/picture-6-rev1.png?raw=true",
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"title": "flux-Realism",
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"repo": "XLabs-AI/flux-RealismLora",
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"trigger_word": ""
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},
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{
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"image": "https://huggingface.co/nerijs/animation2k-flux/resolve/main/images/Q8-oVxNnXvZ9HNrgbNpGw_02762aaaba3b47859ee5fe9403a371e3.png",
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"title": "animation2k",
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"repo": "alvdansen/flux-koda",
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"trigger_word": "flmft style"
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},
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{
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"image": "https://pbs.twimg.com/media/GU7NsZPa8AA4Ddl?format=jpg&name=4096x4096",
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"title": "Half Illustration",
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"weights": "anime_lora.safetensors",
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"trigger_word": ", anime"
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},
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{
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"image": "https://huggingface.co/kudzueye/Boreal/resolve/main/images/ComfyUI_00845_.png",
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"title": "Boreal",
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"aspect": "portrait"
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},
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{
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"image": "https://huggingface.co/alvdansen/softpasty-flux-dev/resolve/main/images/ComfyUI_00814_%20(2).png",
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"title": "SoftPasty",
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"repo": "alvdansen/softpasty-flux-dev",
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"trigger_word": "araminta_illus illustration style"
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},
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{
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"image": "https://huggingface.co/AIWarper/RubberCore1920sCartoonStyle/resolve/main/images/Rub_00006_.png",
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"trigger_word": "RU883R style",
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"trigger_position": "prepend"
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},
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{
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"image": "https://huggingface.co/mgwr/Cine-Aesthetic/resolve/main/images/00030-1333633802.png",
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"title": "Cine Aesthetic",
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"repo": "mgwr/Cine-Aesthetic",
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"trigger_word": "mgwr/cine",
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"trigger_position": "prepend"
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},
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{
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"image": "https://huggingface.co/Shakker-Labs/FLUX.1-dev-LoRA-blended-realistic-illustration/resolve/main/images/example3.png",
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"title": "Blended Realistic Illustration",
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"repo": "Shakker-Labs/FLUX.1-dev-LoRA-blended-realistic-illustration",
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"trigger_word": "artistic style blends reality and illustration elements"
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},
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{
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"image": "https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/picture-6-rev1.png?raw=true",
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"title": "flux-Realism",
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"repo": "XLabs-AI/flux-RealismLora",
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"trigger_word": ""
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},
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{
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"image": "https://huggingface.co/multimodalart/vintage-ads-flux/resolve/main/samples/j_XNU6Oe0mgttyvf9uPb3_dc244dd3d6c246b4aff8351444868d66.png",
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"title": "Vintage Ads",
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"repo":"multimodalart/vintage-ads-flux",
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"trigger_word": "a vintage ad of",
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"trigger_position": "prepend"
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},
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{
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"image": "https://huggingface.co/nerijs/animation2k-flux/resolve/main/images/Q8-oVxNnXvZ9HNrgbNpGw_02762aaaba3b47859ee5fe9403a371e3.png",
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"title": "animation2k",
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"repo": "alvdansen/flux-koda",
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"trigger_word": "flmft style"
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},
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{
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"image": "https://huggingface.co/alvdansen/frosting_lane_flux/resolve/main/images/content%20-%202024-08-11T005936.346.jpeg",
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"title": "Frosting Lane Flux",
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"repo": "alvdansen/frosting_lane_flux",
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"trigger_word": ""
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},
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{
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"image": "https://pbs.twimg.com/media/GU7NsZPa8AA4Ddl?format=jpg&name=4096x4096",
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"title": "Half Illustration",
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"weights": "anime_lora.safetensors",
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"trigger_word": ", anime"
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},
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{
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"image": "https://github.com/XLabs-AI/x-flux/blob/main/assets/readme/examples/result_14.png?raw=true",
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"title": "80s Cyberpunk",
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"repo": "fofr/flux-80s-cyberpunk",
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"trigger_word": "style of 80s cyberpunk",
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"aspect": "portrait"
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},
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{
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"image": "https://huggingface.co/kudzueye/Boreal/resolve/main/images/ComfyUI_00845_.png",
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"title": "Boreal",
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