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Browse files- app.py +5 -102
- requirements.txt +1 -4
- safety_checker.py +0 -137
- style.css +0 -12
app.py
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import gradio as gr
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import
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import spaces
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from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler, DiffusionPipeline
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from huggingface_hub import hf_hub_download
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from safetensors.torch import load_file
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import os
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from PIL import Image
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SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", "0") == "1"
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repo = "ByteDance/SDXL-Lightning"
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checkpoints = {
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"1-Step" : ["sdxl_lightning_1step_unet_x0.safetensors", 1],
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"2-Step" : ["sdxl_lightning_2step_unet.safetensors", 2],
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"4-Step" : ["sdxl_lightning_4step_unet.safetensors", 4],
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"8-Step" : ["sdxl_lightning_8step_unet.safetensors", 8],
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}
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# Ensure model and scheduler are initialized in GPU-enabled function
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if torch.cuda.is_available():
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pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda")
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else:
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pipe = DiffusionPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16")
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pipe.to('cuda')
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print('------------------------------')
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if SAFETY_CHECKER:
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from safety_checker import StableDiffusionSafetyChecker
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from transformers import CLIPFeatureExtractor
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safety_checker = StableDiffusionSafetyChecker.from_pretrained(
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"CompVis/stable-diffusion-safety-checker"
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).to("cuda")
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feature_extractor = CLIPFeatureExtractor.from_pretrained(
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"openai/clip-vit-base-patch32"
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)
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def check_nsfw_images(
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images: list[Image.Image],
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) -> tuple[list[Image.Image], list[bool]]:
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safety_checker_input = feature_extractor(images, return_tensors="pt").to("cuda")
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has_nsfw_concepts = safety_checker(
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images=[images],
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clip_input=safety_checker_input.pixel_values.to("cuda")
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)
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return images, has_nsfw_concepts
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# Function
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@spaces.GPU(enable_queue=True)
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def generate_image(prompt, ckpt):
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checkpoint = checkpoints[ckpt][0]
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num_inference_steps = checkpoints[ckpt][1]
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if num_inference_steps==1:
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# Ensure sampler uses "trailing" timesteps and "sample" prediction type for 1-step inference.
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pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample")
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else:
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# Ensure sampler uses "trailing" timesteps.
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pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
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pipe.unet.load_state_dict(load_file(hf_hub_download(repo, checkpoint), device="cuda"))
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results = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=0)
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if SAFETY_CHECKER:
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images, has_nsfw_concepts = check_nsfw_images(results.images)
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if any(has_nsfw_concepts):
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gr.Warning("NSFW content detected.")
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return Image.new("RGB", (512, 512))
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return images[0]
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return results.images[0]
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# Gradio Interface
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description = """
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This demo utilizes the SDXL-Lightning model by ByteDance, which is a lightning-fast text-to-image generative model capable of producing high-quality images in 4 steps.
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As a community effort, this demo was put together by AngryPenguin. Link to model: https://huggingface.co/ByteDance/SDXL-Lightning
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"""
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with gr.Blocks(css="style.css") as demo:
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gr.HTML("<h1><center>Text-to-Image with SDXL-Lightning ⚡</center></h1>")
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gr.Markdown(description)
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with gr.Group():
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with gr.Row():
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prompt = gr.Textbox(label='Enter you image prompt:', scale=8)
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ckpt = gr.Dropdown(label='Select inference steps',choices=['1-Step', '2-Step', '4-Step', '8-Step'], value='4-Step', interactive=True)
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submit = gr.Button(scale=1, variant='primary')
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img = gr.Image(label='SDXL-Lightning Generated Image')
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prompt.submit(fn=generate_image,
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inputs=[prompt, ckpt],
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outputs=img,
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)
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submit.click(fn=generate_image,
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inputs=[prompt, ckpt],
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outputs=img,
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)
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demo.queue().launch()
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import gradio as gr
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from transformers import pipeline
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def helloName(name):
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return "hello" + name
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demo = gr.Interface(fn=helloName, input="textbox", outputs="textbox")
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demo.launch()
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requirements.txt
CHANGED
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transformers
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diffusers
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torch
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gradio
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spaces
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transformers
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torch
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gradio
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safety_checker.py
DELETED
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# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import torch
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import torch.nn as nn
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from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
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def cosine_distance(image_embeds, text_embeds):
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normalized_image_embeds = nn.functional.normalize(image_embeds)
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normalized_text_embeds = nn.functional.normalize(text_embeds)
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return torch.mm(normalized_image_embeds, normalized_text_embeds.t())
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class StableDiffusionSafetyChecker(PreTrainedModel):
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config_class = CLIPConfig
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_no_split_modules = ["CLIPEncoderLayer"]
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def __init__(self, config: CLIPConfig):
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super().__init__(config)
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self.vision_model = CLIPVisionModel(config.vision_config)
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self.visual_projection = nn.Linear(
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config.vision_config.hidden_size, config.projection_dim, bias=False
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)
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self.concept_embeds = nn.Parameter(
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torch.ones(17, config.projection_dim), requires_grad=False
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)
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self.special_care_embeds = nn.Parameter(
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torch.ones(3, config.projection_dim), requires_grad=False
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)
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self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False)
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self.special_care_embeds_weights = nn.Parameter(
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torch.ones(3), requires_grad=False
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)
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@torch.no_grad()
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def forward(self, clip_input, images):
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pooled_output = self.vision_model(clip_input)[1] # pooled_output
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image_embeds = self.visual_projection(pooled_output)
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# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
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special_cos_dist = (
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cosine_distance(image_embeds, self.special_care_embeds)
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.cpu()
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.float()
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.numpy()
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)
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cos_dist = (
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cosine_distance(image_embeds, self.concept_embeds).cpu().float().numpy()
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)
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result = []
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batch_size = image_embeds.shape[0]
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for i in range(batch_size):
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result_img = {
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"special_scores": {},
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"special_care": [],
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"concept_scores": {},
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"bad_concepts": [],
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}
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# increase this value to create a stronger `nfsw` filter
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# at the cost of increasing the possibility of filtering benign images
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adjustment = 0.0
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for concept_idx in range(len(special_cos_dist[0])):
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concept_cos = special_cos_dist[i][concept_idx]
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concept_threshold = self.special_care_embeds_weights[concept_idx].item()
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result_img["special_scores"][concept_idx] = round(
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concept_cos - concept_threshold + adjustment, 3
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)
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if result_img["special_scores"][concept_idx] > 0:
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result_img["special_care"].append(
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{concept_idx, result_img["special_scores"][concept_idx]}
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)
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adjustment = 0.01
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for concept_idx in range(len(cos_dist[0])):
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concept_cos = cos_dist[i][concept_idx]
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concept_threshold = self.concept_embeds_weights[concept_idx].item()
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result_img["concept_scores"][concept_idx] = round(
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concept_cos - concept_threshold + adjustment, 3
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)
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if result_img["concept_scores"][concept_idx] > 0:
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result_img["bad_concepts"].append(concept_idx)
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result.append(result_img)
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has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result]
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return has_nsfw_concepts
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@torch.no_grad()
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def forward_onnx(self, clip_input: torch.FloatTensor, images: torch.FloatTensor):
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pooled_output = self.vision_model(clip_input)[1] # pooled_output
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image_embeds = self.visual_projection(pooled_output)
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special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds)
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cos_dist = cosine_distance(image_embeds, self.concept_embeds)
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# increase this value to create a stronger `nsfw` filter
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# at the cost of increasing the possibility of filtering benign images
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adjustment = 0.0
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special_scores = (
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special_cos_dist - self.special_care_embeds_weights + adjustment
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)
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# special_scores = special_scores.round(decimals=3)
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special_care = torch.any(special_scores > 0, dim=1)
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special_adjustment = special_care * 0.01
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special_adjustment = special_adjustment.unsqueeze(1).expand(
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-1, cos_dist.shape[1]
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)
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concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment
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# concept_scores = concept_scores.round(decimals=3)
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has_nsfw_concepts = torch.any(concept_scores > 0, dim=1)
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images[has_nsfw_concepts] = 0.0 # black image
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return images, has_nsfw_concepts
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style.css
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.gradio-container {
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max-width: 690px! important;
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}
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#share-btn-container{padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; max-width: 13rem; margin-left: auto;margin-top: 0.35em;}
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div#share-btn-container > div {flex-direction: row;background: black;align-items: center}
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#share-btn-container:hover {background-color: #060606}
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#share-btn {all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.5rem !important; padding-bottom: 0.5rem !important;right:0;font-size: 15px;}
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#share-btn * {all: unset}
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#share-btn-container div:nth-child(-n+2){width: auto !important;min-height: 0px !important;}
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#share-btn-container .wrap {display: none !important}
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#share-btn-container.hidden {display: none!important}
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